Datasets
Models
Downloads: 1
26909 lines (25051 with data), 2.3 MB
2024-04-21 13:17:38,890:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-04-21 13:17:38,890:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-04-21 13:17:38,890:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-04-21 13:17:38,890:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-04-21 13:24:26,697:INFO:PyCaret ClassificationExperiment
2024-04-21 13:24:26,698:INFO:Logging name: obesity_classification
2024-04-21 13:24:26,699:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2024-04-21 13:24:26,699:INFO:version 3.3.0
2024-04-21 13:24:26,699:INFO:Initializing setup()
2024-04-21 13:24:26,699:INFO:self.USI: 619b
2024-04-21 13:24:26,699:INFO:self._variable_keys: {'target_param', 'exp_id', 'data', 'idx', 'fold_shuffle_param', 'log_plots_param', 'fold_groups_param', 'html_param', 'seed', 'exp_name_log', '_available_plots', 'y_train', 'X_train', '_ml_usecase', 'pipeline', 'USI', 'gpu_param', 'fold_generator', 'y', 'X_test', 'fix_imbalance', 'is_multiclass', 'memory', 'n_jobs_param', 'y_test', 'gpu_n_jobs_param', 'logging_param', 'X'}
2024-04-21 13:24:26,699:INFO:Checking environment
2024-04-21 13:24:26,700:INFO:python_version: 3.10.13
2024-04-21 13:24:26,700:INFO:python_build: ('main', 'Sep 11 2023 08:16:02')
2024-04-21 13:24:26,700:INFO:machine: arm64
2024-04-21 13:24:26,701:INFO:platform: macOS-14.0-arm64-arm-64bit
2024-04-21 13:24:26,704:INFO:Memory: svmem(total=8589934592, available=1228144640, percent=85.7, used=3116187648, free=46661632, active=1195196416, inactive=1132560384, wired=1920991232)
2024-04-21 13:24:26,705:INFO:Physical Core: 8
2024-04-21 13:24:26,705:INFO:Logical Core: 8
2024-04-21 13:24:26,705:INFO:Checking libraries
2024-04-21 13:24:26,705:INFO:System:
2024-04-21 13:24:26,705:INFO: python: 3.10.13 (main, Sep 11 2023, 08:16:02) [Clang 14.0.6 ]
2024-04-21 13:24:26,705:INFO:executable: /Users/arham/anaconda3/envs/DataScience/bin/python
2024-04-21 13:24:26,705:INFO: machine: macOS-14.0-arm64-arm-64bit
2024-04-21 13:24:26,705:INFO:PyCaret required dependencies:
2024-04-21 13:24:26,721:INFO: pip: 23.3
2024-04-21 13:24:26,721:INFO: setuptools: 60.2.0
2024-04-21 13:24:26,721:INFO: pycaret: 3.3.0
2024-04-21 13:24:26,721:INFO: IPython: 8.15.0
2024-04-21 13:24:26,721:INFO: ipywidgets: 8.0.4
2024-04-21 13:24:26,721:INFO: tqdm: 4.65.2
2024-04-21 13:24:26,721:INFO: numpy: 1.25.2
2024-04-21 13:24:26,721:INFO: pandas: 2.2.2
2024-04-21 13:24:26,721:INFO: jinja2: 3.1.2
2024-04-21 13:24:26,721:INFO: scipy: 1.11.4
2024-04-21 13:24:26,721:INFO: joblib: 1.2.0
2024-04-21 13:24:26,721:INFO: sklearn: 1.4.0
2024-04-21 13:24:26,721:INFO: pyod: 1.1.3
2024-04-21 13:24:26,721:INFO: imblearn: 0.12.2
2024-04-21 13:24:26,721:INFO: category_encoders: 2.6.3
2024-04-21 13:24:26,721:INFO: lightgbm: 4.1.0
2024-04-21 13:24:26,721:INFO: numba: 0.58.1
2024-04-21 13:24:26,721:INFO: requests: 2.28.2
2024-04-21 13:24:26,721:INFO: matplotlib: 3.6.2
2024-04-21 13:24:26,721:INFO: scikitplot: 0.3.7
2024-04-21 13:24:26,721:INFO: yellowbrick: 1.5
2024-04-21 13:24:26,721:INFO: plotly: 5.17.0
2024-04-21 13:24:26,721:INFO: plotly-resampler: Not installed
2024-04-21 13:24:26,721:INFO: kaleido: 0.2.1
2024-04-21 13:24:26,721:INFO: schemdraw: 0.15
2024-04-21 13:24:26,721:INFO: statsmodels: 0.13.5
2024-04-21 13:24:26,721:INFO: sktime: 0.26.1
2024-04-21 13:24:26,721:INFO: tbats: 1.1.3
2024-04-21 13:24:26,721:INFO: pmdarima: 2.0.4
2024-04-21 13:24:26,721:INFO: psutil: 5.9.0
2024-04-21 13:24:26,721:INFO: markupsafe: 2.1.1
2024-04-21 13:24:26,721:INFO: pickle5: Not installed
2024-04-21 13:24:26,721:INFO: cloudpickle: 2.2.1
2024-04-21 13:24:26,721:INFO: deprecation: 2.1.0
2024-04-21 13:24:26,721:INFO: xxhash: 3.4.1
2024-04-21 13:24:26,721:INFO: wurlitzer: 3.0.2
2024-04-21 13:24:26,721:INFO:PyCaret optional dependencies:
2024-04-21 13:24:27,878:INFO: shap: 0.44.0
2024-04-21 13:24:27,879:INFO: interpret: Not installed
2024-04-21 13:24:27,879:INFO: umap: Not installed
2024-04-21 13:24:27,879:INFO: ydata_profiling: 0.0.dev0
2024-04-21 13:24:27,879:INFO: explainerdashboard: Not installed
2024-04-21 13:24:27,879:INFO: autoviz: Not installed
2024-04-21 13:24:27,879:INFO: fairlearn: Not installed
2024-04-21 13:24:27,879:INFO: deepchecks: Not installed
2024-04-21 13:24:27,879:INFO: xgboost: 1.7.3
2024-04-21 13:24:27,879:INFO: catboost: 1.1.1
2024-04-21 13:24:27,879:INFO: kmodes: Not installed
2024-04-21 13:24:27,879:INFO: mlxtend: Not installed
2024-04-21 13:24:27,879:INFO: statsforecast: 1.4.0
2024-04-21 13:24:27,879:INFO: tune_sklearn: Not installed
2024-04-21 13:24:27,879:INFO: ray: 2.10.0
2024-04-21 13:24:27,879:INFO: hyperopt: 0.2.7
2024-04-21 13:24:27,879:INFO: optuna: 3.5.0
2024-04-21 13:24:27,879:INFO: skopt: Not installed
2024-04-21 13:24:27,879:INFO: mlflow: 2.10.2
2024-04-21 13:24:27,879:INFO: gradio: 3.48.0
2024-04-21 13:24:27,879:INFO: fastapi: 0.109.2
2024-04-21 13:24:27,879:INFO: uvicorn: 0.27.1
2024-04-21 13:24:27,879:INFO: m2cgen: Not installed
2024-04-21 13:24:27,879:INFO: evidently: Not installed
2024-04-21 13:24:27,879:INFO: fugue: Not installed
2024-04-21 13:24:27,879:INFO: streamlit: 1.27.2
2024-04-21 13:24:27,879:INFO: prophet: Not installed
2024-04-21 13:24:27,879:INFO:None
2024-04-21 13:24:27,879:INFO:Set up data.
2024-04-21 13:24:27,895:INFO:Set up folding strategy.
2024-04-21 13:24:27,895:INFO:Set up train/test split.
2024-04-21 13:24:27,910:INFO:Set up index.
2024-04-21 13:24:27,910:INFO:Assigning column types.
2024-04-21 13:24:27,919:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2024-04-21 13:24:27,949:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-21 13:24:27,955:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:24:27,983:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:24:28,016:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:24:28,130:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-21 13:24:28,131:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:24:28,149:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:24:28,150:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:24:28,151:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2024-04-21 13:24:28,180:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:24:28,198:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:24:28,200:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:24:28,231:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:24:28,249:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:24:28,250:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:24:28,251:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2024-04-21 13:24:28,297:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:24:28,299:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:24:28,345:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:24:28,347:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:24:28,350:INFO:Preparing preprocessing pipeline...
2024-04-21 13:24:28,352:INFO:Set up simple imputation.
2024-04-21 13:24:28,357:INFO:Set up encoding of categorical features.
2024-04-21 13:24:28,359:INFO:Set up column name cleaning.
2024-04-21 13:24:28,426:INFO:Finished creating preprocessing pipeline.
2024-04-21 13:24:28,431:INFO:Pipeline: Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Scaled_Age',
'Log_Age', 'Scaled_Weight',
'Log_Weight', 'Sc...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False)
2024-04-21 13:24:28,431:INFO:Creating final display dataframe.
2024-04-21 13:24:28,582:INFO:Setup _display_container: Description Value
0 Session id 123
1 Target NObeyesdad
2 Target type Multiclass
3 Original data shape (10793, 35)
4 Transformed data shape (10793, 39)
5 Transformed train set shape (8634, 39)
6 Transformed test set shape (2159, 39)
7 Numeric features 28
8 Categorical features 1
9 Preprocess True
10 Imputation type simple
11 Numeric imputation mean
12 Categorical imputation mode
13 Maximum one-hot encoding 25
14 Encoding method None
15 Fold Generator StratifiedKFold
16 Fold Number 10
17 CPU Jobs -1
18 Use GPU False
19 Log Experiment MlflowLogger
20 Experiment Name obesity_classification
21 USI 619b
2024-04-21 13:24:28,649:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:24:28,651:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:24:28,699:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:24:28,701:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:24:28,702:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour.
warnings.warn(
2024-04-21 13:24:28,703:INFO:Logging experiment in loggers
2024-04-21 13:24:28,868:INFO:SubProcess save_model() called ==================================
2024-04-21 13:24:28,876:INFO:Initializing save_model()
2024-04-21 13:24:28,876:INFO:save_model(model=Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Scaled_Age',
'Log_Age', 'Scaled_Weight',
'Log_Weight', 'Sc...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False), model_name=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/tmpud2ug4co/Transformation Pipeline, prep_pipe_=Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Scaled_Age',
'Log_Age', 'Scaled_Weight',
'Log_Weight', 'Sc...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False), verbose=False, use_case=MLUsecase.CLASSIFICATION, kwargs={})
2024-04-21 13:24:28,876:INFO:Adding model into prep_pipe
2024-04-21 13:24:28,876:WARNING:Only Model saved as it was a pipeline.
2024-04-21 13:24:28,881:INFO:/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/tmpud2ug4co/Transformation Pipeline.pkl saved in current working directory
2024-04-21 13:24:28,884:INFO:Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Scaled_Age',
'Log_Age', 'Scaled_Weight',
'Log_Weight', 'Sc...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False)
2024-04-21 13:24:28,884:INFO:save_model() successfully completed......................................
2024-04-21 13:24:29,186:INFO:SubProcess save_model() end ==================================
2024-04-21 13:24:29,190:INFO:setup() successfully completed in 2.05s...............
2024-04-21 13:25:18,683:INFO:PyCaret ClassificationExperiment
2024-04-21 13:25:18,683:INFO:Logging name: obesity_classification
2024-04-21 13:25:18,683:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2024-04-21 13:25:18,683:INFO:version 3.3.0
2024-04-21 13:25:18,683:INFO:Initializing setup()
2024-04-21 13:25:18,683:INFO:self.USI: fc4d
2024-04-21 13:25:18,683:INFO:self._variable_keys: {'target_param', 'exp_id', 'data', 'idx', 'fold_shuffle_param', 'log_plots_param', 'fold_groups_param', 'html_param', 'seed', 'exp_name_log', '_available_plots', 'y_train', 'X_train', '_ml_usecase', 'pipeline', 'USI', 'gpu_param', 'fold_generator', 'y', 'X_test', 'fix_imbalance', 'is_multiclass', 'memory', 'n_jobs_param', 'y_test', 'gpu_n_jobs_param', 'logging_param', 'X'}
2024-04-21 13:25:18,684:INFO:Checking environment
2024-04-21 13:25:18,684:INFO:python_version: 3.10.13
2024-04-21 13:25:18,684:INFO:python_build: ('main', 'Sep 11 2023 08:16:02')
2024-04-21 13:25:18,684:INFO:machine: arm64
2024-04-21 13:25:18,684:INFO:platform: macOS-14.0-arm64-arm-64bit
2024-04-21 13:25:18,685:INFO:Memory: svmem(total=8589934592, available=1309540352, percent=84.8, used=3173433344, free=56459264, active=1266794496, inactive=1248542720, wired=1906638848)
2024-04-21 13:25:18,685:INFO:Physical Core: 8
2024-04-21 13:25:18,685:INFO:Logical Core: 8
2024-04-21 13:25:18,685:INFO:Checking libraries
2024-04-21 13:25:18,685:INFO:System:
2024-04-21 13:25:18,685:INFO: python: 3.10.13 (main, Sep 11 2023, 08:16:02) [Clang 14.0.6 ]
2024-04-21 13:25:18,685:INFO:executable: /Users/arham/anaconda3/envs/DataScience/bin/python
2024-04-21 13:25:18,685:INFO: machine: macOS-14.0-arm64-arm-64bit
2024-04-21 13:25:18,685:INFO:PyCaret required dependencies:
2024-04-21 13:25:18,685:INFO: pip: 23.3
2024-04-21 13:25:18,685:INFO: setuptools: 60.2.0
2024-04-21 13:25:18,685:INFO: pycaret: 3.3.0
2024-04-21 13:25:18,685:INFO: IPython: 8.15.0
2024-04-21 13:25:18,685:INFO: ipywidgets: 8.0.4
2024-04-21 13:25:18,685:INFO: tqdm: 4.65.2
2024-04-21 13:25:18,685:INFO: numpy: 1.25.2
2024-04-21 13:25:18,686:INFO: pandas: 2.2.2
2024-04-21 13:25:18,686:INFO: jinja2: 3.1.2
2024-04-21 13:25:18,686:INFO: scipy: 1.11.4
2024-04-21 13:25:18,686:INFO: joblib: 1.2.0
2024-04-21 13:25:18,686:INFO: sklearn: 1.4.0
2024-04-21 13:25:18,686:INFO: pyod: 1.1.3
2024-04-21 13:25:18,686:INFO: imblearn: 0.12.2
2024-04-21 13:25:18,686:INFO: category_encoders: 2.6.3
2024-04-21 13:25:18,686:INFO: lightgbm: 4.1.0
2024-04-21 13:25:18,686:INFO: numba: 0.58.1
2024-04-21 13:25:18,686:INFO: requests: 2.28.2
2024-04-21 13:25:18,686:INFO: matplotlib: 3.6.2
2024-04-21 13:25:18,686:INFO: scikitplot: 0.3.7
2024-04-21 13:25:18,686:INFO: yellowbrick: 1.5
2024-04-21 13:25:18,686:INFO: plotly: 5.17.0
2024-04-21 13:25:18,686:INFO: plotly-resampler: Not installed
2024-04-21 13:25:18,686:INFO: kaleido: 0.2.1
2024-04-21 13:25:18,686:INFO: schemdraw: 0.15
2024-04-21 13:25:18,686:INFO: statsmodels: 0.13.5
2024-04-21 13:25:18,686:INFO: sktime: 0.26.1
2024-04-21 13:25:18,686:INFO: tbats: 1.1.3
2024-04-21 13:25:18,686:INFO: pmdarima: 2.0.4
2024-04-21 13:25:18,686:INFO: psutil: 5.9.0
2024-04-21 13:25:18,686:INFO: markupsafe: 2.1.1
2024-04-21 13:25:18,686:INFO: pickle5: Not installed
2024-04-21 13:25:18,686:INFO: cloudpickle: 2.2.1
2024-04-21 13:25:18,686:INFO: deprecation: 2.1.0
2024-04-21 13:25:18,686:INFO: xxhash: 3.4.1
2024-04-21 13:25:18,686:INFO: wurlitzer: 3.0.2
2024-04-21 13:25:18,686:INFO:PyCaret optional dependencies:
2024-04-21 13:25:18,686:INFO: shap: 0.44.0
2024-04-21 13:25:18,686:INFO: interpret: Not installed
2024-04-21 13:25:18,686:INFO: umap: Not installed
2024-04-21 13:25:18,686:INFO: ydata_profiling: 0.0.dev0
2024-04-21 13:25:18,686:INFO: explainerdashboard: Not installed
2024-04-21 13:25:18,686:INFO: autoviz: Not installed
2024-04-21 13:25:18,686:INFO: fairlearn: Not installed
2024-04-21 13:25:18,686:INFO: deepchecks: Not installed
2024-04-21 13:25:18,687:INFO: xgboost: 1.7.3
2024-04-21 13:25:18,687:INFO: catboost: 1.1.1
2024-04-21 13:25:18,687:INFO: kmodes: Not installed
2024-04-21 13:25:18,687:INFO: mlxtend: Not installed
2024-04-21 13:25:18,687:INFO: statsforecast: 1.4.0
2024-04-21 13:25:18,687:INFO: tune_sklearn: Not installed
2024-04-21 13:25:18,687:INFO: ray: 2.10.0
2024-04-21 13:25:18,687:INFO: hyperopt: 0.2.7
2024-04-21 13:25:18,687:INFO: optuna: 3.5.0
2024-04-21 13:25:18,687:INFO: skopt: Not installed
2024-04-21 13:25:18,687:INFO: mlflow: 2.10.2
2024-04-21 13:25:18,687:INFO: gradio: 3.48.0
2024-04-21 13:25:18,687:INFO: fastapi: 0.109.2
2024-04-21 13:25:18,687:INFO: uvicorn: 0.27.1
2024-04-21 13:25:18,687:INFO: m2cgen: Not installed
2024-04-21 13:25:18,687:INFO: evidently: Not installed
2024-04-21 13:25:18,687:INFO: fugue: Not installed
2024-04-21 13:25:18,687:INFO: streamlit: 1.27.2
2024-04-21 13:25:18,687:INFO: prophet: Not installed
2024-04-21 13:25:18,687:INFO:None
2024-04-21 13:25:18,687:INFO:Set up data.
2024-04-21 13:25:18,697:INFO:Set up folding strategy.
2024-04-21 13:25:18,697:INFO:Set up train/test split.
2024-04-21 13:25:18,710:INFO:Set up index.
2024-04-21 13:25:18,710:INFO:Assigning column types.
2024-04-21 13:25:18,720:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2024-04-21 13:25:18,752:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-21 13:25:18,757:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:25:18,785:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:25:18,789:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:25:18,820:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-21 13:25:18,821:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:25:18,840:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:25:18,842:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:25:18,842:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2024-04-21 13:25:18,871:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:25:18,892:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:25:18,895:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:25:18,929:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:25:18,950:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:25:18,952:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:25:18,953:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2024-04-21 13:25:19,009:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:25:19,012:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:25:19,088:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:25:19,091:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:25:19,094:INFO:Preparing preprocessing pipeline...
2024-04-21 13:25:19,097:INFO:Set up simple imputation.
2024-04-21 13:25:19,117:INFO:Set up encoding of categorical features.
2024-04-21 13:25:19,126:INFO:Set up column name cleaning.
2024-04-21 13:25:19,271:INFO:Finished creating preprocessing pipeline.
2024-04-21 13:25:19,282:INFO:Pipeline: Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Scaled_Age',
'Log_Age', 'Scaled_Weight',
'Log_Weight', 'Sc...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False)
2024-04-21 13:25:19,283:INFO:Creating final display dataframe.
2024-04-21 13:25:19,698:INFO:Setup _display_container: Description Value
0 Session id 123
1 Target NObeyesdad
2 Target type Multiclass
3 Original data shape (10793, 35)
4 Transformed data shape (10793, 39)
5 Transformed train set shape (8634, 39)
6 Transformed test set shape (2159, 39)
7 Numeric features 28
8 Categorical features 1
9 Preprocess True
10 Imputation type simple
11 Numeric imputation mean
12 Categorical imputation mode
13 Maximum one-hot encoding 25
14 Encoding method None
15 Fold Generator StratifiedKFold
16 Fold Number 10
17 CPU Jobs -1
18 Use GPU False
19 Log Experiment MlflowLogger
20 Experiment Name obesity_classification
21 USI fc4d
2024-04-21 13:25:19,790:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:25:19,793:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:25:19,853:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:25:19,855:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:25:19,856:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour.
warnings.warn(
2024-04-21 13:25:19,857:INFO:Logging experiment in loggers
2024-04-21 13:25:19,886:INFO:SubProcess save_model() called ==================================
2024-04-21 13:25:19,894:INFO:Initializing save_model()
2024-04-21 13:25:19,894:INFO:save_model(model=Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Scaled_Age',
'Log_Age', 'Scaled_Weight',
'Log_Weight', 'Sc...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False), model_name=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/tmp7ln2wuvw/Transformation Pipeline, prep_pipe_=Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Scaled_Age',
'Log_Age', 'Scaled_Weight',
'Log_Weight', 'Sc...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False), verbose=False, use_case=MLUsecase.CLASSIFICATION, kwargs={})
2024-04-21 13:25:19,894:INFO:Adding model into prep_pipe
2024-04-21 13:25:19,894:WARNING:Only Model saved as it was a pipeline.
2024-04-21 13:25:19,898:INFO:/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/tmp7ln2wuvw/Transformation Pipeline.pkl saved in current working directory
2024-04-21 13:25:19,902:INFO:Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Scaled_Age',
'Log_Age', 'Scaled_Weight',
'Log_Weight', 'Sc...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False)
2024-04-21 13:25:19,902:INFO:save_model() successfully completed......................................
2024-04-21 13:25:20,377:INFO:SubProcess save_model() end ==================================
2024-04-21 13:25:20,383:INFO:setup() successfully completed in 1.19s...............
2024-04-21 13:25:20,389:INFO:Initializing compare_models()
2024-04-21 13:25:20,389:INFO:compare_models(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, include=None, fold=5, 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 0x289ae88e0>, 'include': None, 'exclude': None, 'fold': 5, '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)
2024-04-21 13:25:20,389:INFO:Checking exceptions
2024-04-21 13:25:20,403:INFO:Preparing display monitor
2024-04-21 13:25:20,494:INFO:Initializing Logistic Regression
2024-04-21 13:25:20,494:INFO:Total runtime is 7.09692637125651e-06 minutes
2024-04-21 13:25:20,498:INFO:SubProcess create_model() called ==================================
2024-04-21 13:25:20,498:INFO:Initializing create_model()
2024-04-21 13:25:20,498:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=lr, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:25:20,498:INFO:Checking exceptions
2024-04-21 13:25:20,498:INFO:Importing libraries
2024-04-21 13:25:20,499:INFO:Copying training dataset
2024-04-21 13:25:20,528:INFO:Defining folds
2024-04-21 13:25:20,528:INFO:Declaring metric variables
2024-04-21 13:25:20,532:INFO:Importing untrained model
2024-04-21 13:25:20,536:INFO:Logistic Regression Imported successfully
2024-04-21 13:25:20,543:INFO:Starting cross validation
2024-04-21 13:25:20,545:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:25:24,129:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).
from pandas.core import (
2024-04-21 13:25:24,129:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).
from pandas.core import (
2024-04-21 13:25:24,129:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).
from pandas.core import (
2024-04-21 13:25:24,129:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).
from pandas.core import (
2024-04-21 13:25:24,129:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).
from pandas.core import (
2024-04-21 13:25:32,149:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-21 13:25:32,151:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-21 13:25:32,202:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:32,206:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:32,211:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:32,213:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:32,298:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-21 13:25:32,337:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-21 13:25:32,378:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:32,386:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:32,420:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:32,427:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:32,514:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-21 13:25:32,583:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:32,590:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:32,604:INFO:Calculating mean and std
2024-04-21 13:25:32,609:INFO:Creating metrics dataframe
2024-04-21 13:25:32,618:INFO:Uploading results into container
2024-04-21 13:25:32,619:INFO:Uploading model into container now
2024-04-21 13:25:32,620:INFO:_master_model_container: 1
2024-04-21 13:25:32,620:INFO:_display_container: 2
2024-04-21 13:25:32,622: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)
2024-04-21 13:25:32,622:INFO:create_model() successfully completed......................................
2024-04-21 13:25:32,806:INFO:SubProcess create_model() end ==================================
2024-04-21 13:25:32,807:INFO:Creating metrics dataframe
2024-04-21 13:25:32,812:INFO:Initializing K Neighbors Classifier
2024-04-21 13:25:32,812:INFO:Total runtime is 0.20531094868977867 minutes
2024-04-21 13:25:32,815:INFO:SubProcess create_model() called ==================================
2024-04-21 13:25:32,815:INFO:Initializing create_model()
2024-04-21 13:25:32,815:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=knn, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:25:32,816:INFO:Checking exceptions
2024-04-21 13:25:32,816:INFO:Importing libraries
2024-04-21 13:25:32,816:INFO:Copying training dataset
2024-04-21 13:25:32,831:INFO:Defining folds
2024-04-21 13:25:32,832:INFO:Declaring metric variables
2024-04-21 13:25:32,834:INFO:Importing untrained model
2024-04-21 13:25:32,838:INFO:K Neighbors Classifier Imported successfully
2024-04-21 13:25:32,843:INFO:Starting cross validation
2024-04-21 13:25:32,844:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:25:32,966:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:32,968:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:32,976:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:32,982:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:25:32,982:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:25:32,991:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:25:34,086:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).
from pandas.core import (
2024-04-21 13:25:34,086:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).
from pandas.core import (
2024-04-21 13:25:35,827:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:35,829:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:35,851:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:25:35,853:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:25:35,867:INFO:Calculating mean and std
2024-04-21 13:25:35,885:INFO:Creating metrics dataframe
2024-04-21 13:25:35,908:INFO:Uploading results into container
2024-04-21 13:25:35,909:INFO:Uploading model into container now
2024-04-21 13:25:35,910:INFO:_master_model_container: 2
2024-04-21 13:25:35,911:INFO:_display_container: 2
2024-04-21 13:25:35,912:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform')
2024-04-21 13:25:35,912:INFO:create_model() successfully completed......................................
2024-04-21 13:25:36,458:WARNING:create_model() for KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform') raised an exception or returned all 0.0, trying without fit_kwargs:
2024-04-21 13:25:36,468:WARNING:Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py", line 797, in compare_models
np.sum(
AssertionError
2024-04-21 13:25:36,469:INFO:Initializing create_model()
2024-04-21 13:25:36,469:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=knn, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:25:36,469:INFO:Checking exceptions
2024-04-21 13:25:36,469:INFO:Importing libraries
2024-04-21 13:25:36,470:INFO:Copying training dataset
2024-04-21 13:25:36,574:INFO:Defining folds
2024-04-21 13:25:36,575:INFO:Declaring metric variables
2024-04-21 13:25:36,582:INFO:Importing untrained model
2024-04-21 13:25:36,586:INFO:K Neighbors Classifier Imported successfully
2024-04-21 13:25:36,593:INFO:Starting cross validation
2024-04-21 13:25:36,596:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:25:36,987:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:36,991:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:37,022:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:25:37,036:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:25:37,053:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:37,062:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:37,075:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:25:37,080:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:37,084:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:25:37,097:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:25:37,112:INFO:Calculating mean and std
2024-04-21 13:25:37,125:INFO:Creating metrics dataframe
2024-04-21 13:25:37,139:INFO:Uploading results into container
2024-04-21 13:25:37,140:INFO:Uploading model into container now
2024-04-21 13:25:37,141:INFO:_master_model_container: 3
2024-04-21 13:25:37,142:INFO:_display_container: 2
2024-04-21 13:25:37,143:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform')
2024-04-21 13:25:37,143:INFO:create_model() successfully completed......................................
2024-04-21 13:25:37,366:ERROR:create_model() for KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform') raised an exception or returned all 0.0:
2024-04-21 13:25:37,366:ERROR:Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py", line 797, in compare_models
np.sum(
AssertionError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py", line 818, in compare_models
np.sum(
AssertionError
2024-04-21 13:25:37,366:INFO:Initializing Naive Bayes
2024-04-21 13:25:37,366:INFO:Total runtime is 0.28120676676432294 minutes
2024-04-21 13:25:37,369:INFO:SubProcess create_model() called ==================================
2024-04-21 13:25:37,369:INFO:Initializing create_model()
2024-04-21 13:25:37,369:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=nb, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:25:37,369:INFO:Checking exceptions
2024-04-21 13:25:37,369:INFO:Importing libraries
2024-04-21 13:25:37,369:INFO:Copying training dataset
2024-04-21 13:25:37,385:INFO:Defining folds
2024-04-21 13:25:37,385:INFO:Declaring metric variables
2024-04-21 13:25:37,387:INFO:Importing untrained model
2024-04-21 13:25:37,390:INFO:Naive Bayes Imported successfully
2024-04-21 13:25:37,394:INFO:Starting cross validation
2024-04-21 13:25:37,395:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:25:37,537:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:37,553:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:37,554:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:37,563:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:37,585:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:37,591:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:38,283:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).
from pandas.core import (
2024-04-21 13:25:39,440:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:39,452:INFO:Calculating mean and std
2024-04-21 13:25:39,454:INFO:Creating metrics dataframe
2024-04-21 13:25:39,460:INFO:Uploading results into container
2024-04-21 13:25:39,460:INFO:Uploading model into container now
2024-04-21 13:25:39,461:INFO:_master_model_container: 4
2024-04-21 13:25:39,461:INFO:_display_container: 2
2024-04-21 13:25:39,462:INFO:GaussianNB(priors=None, var_smoothing=1e-09)
2024-04-21 13:25:39,462:INFO:create_model() successfully completed......................................
2024-04-21 13:25:39,600:INFO:SubProcess create_model() end ==================================
2024-04-21 13:25:39,600:INFO:Creating metrics dataframe
2024-04-21 13:25:39,607:INFO:Initializing Decision Tree Classifier
2024-04-21 13:25:39,607:INFO:Total runtime is 0.3185542146364848 minutes
2024-04-21 13:25:39,609:INFO:SubProcess create_model() called ==================================
2024-04-21 13:25:39,609:INFO:Initializing create_model()
2024-04-21 13:25:39,609:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=dt, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:25:39,610:INFO:Checking exceptions
2024-04-21 13:25:39,610:INFO:Importing libraries
2024-04-21 13:25:39,610:INFO:Copying training dataset
2024-04-21 13:25:39,624:INFO:Defining folds
2024-04-21 13:25:39,624:INFO:Declaring metric variables
2024-04-21 13:25:39,626:INFO:Importing untrained model
2024-04-21 13:25:39,629:INFO:Decision Tree Classifier Imported successfully
2024-04-21 13:25:39,633:INFO:Starting cross validation
2024-04-21 13:25:39,634:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:25:40,131:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:40,197:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:40,239:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:40,248:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:40,264:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:40,279:INFO:Calculating mean and std
2024-04-21 13:25:40,280:INFO:Creating metrics dataframe
2024-04-21 13:25:40,282:INFO:Uploading results into container
2024-04-21 13:25:40,282:INFO:Uploading model into container now
2024-04-21 13:25:40,283:INFO:_master_model_container: 5
2024-04-21 13:25:40,283:INFO:_display_container: 2
2024-04-21 13:25:40,283: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')
2024-04-21 13:25:40,284:INFO:create_model() successfully completed......................................
2024-04-21 13:25:40,387:INFO:SubProcess create_model() end ==================================
2024-04-21 13:25:40,387:INFO:Creating metrics dataframe
2024-04-21 13:25:40,393:INFO:Initializing SVM - Linear Kernel
2024-04-21 13:25:40,393:INFO:Total runtime is 0.3316583315531413 minutes
2024-04-21 13:25:40,396:INFO:SubProcess create_model() called ==================================
2024-04-21 13:25:40,396:INFO:Initializing create_model()
2024-04-21 13:25:40,396:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=svm, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:25:40,396:INFO:Checking exceptions
2024-04-21 13:25:40,396:INFO:Importing libraries
2024-04-21 13:25:40,396:INFO:Copying training dataset
2024-04-21 13:25:40,411:INFO:Defining folds
2024-04-21 13:25:40,411:INFO:Declaring metric variables
2024-04-21 13:25:40,413:INFO:Importing untrained model
2024-04-21 13:25:40,416:INFO:SVM - Linear Kernel Imported successfully
2024-04-21 13:25:40,420:INFO:Starting cross validation
2024-04-21 13:25:40,421:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:25:41,651:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:25:41,658:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:41,762:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:25:41,765:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:25:41,768:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:41,771:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:41,780:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:25:41,786:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:41,807:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:25:41,813:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:41,821:INFO:Calculating mean and std
2024-04-21 13:25:41,823:INFO:Creating metrics dataframe
2024-04-21 13:25:41,825:INFO:Uploading results into container
2024-04-21 13:25:41,826:INFO:Uploading model into container now
2024-04-21 13:25:41,826:INFO:_master_model_container: 6
2024-04-21 13:25:41,826:INFO:_display_container: 2
2024-04-21 13:25:41,827: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)
2024-04-21 13:25:41,827:INFO:create_model() successfully completed......................................
2024-04-21 13:25:41,943:INFO:SubProcess create_model() end ==================================
2024-04-21 13:25:41,943:INFO:Creating metrics dataframe
2024-04-21 13:25:41,949:INFO:Initializing Ridge Classifier
2024-04-21 13:25:41,949:INFO:Total runtime is 0.35759286483128866 minutes
2024-04-21 13:25:41,952:INFO:SubProcess create_model() called ==================================
2024-04-21 13:25:41,952:INFO:Initializing create_model()
2024-04-21 13:25:41,952:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=ridge, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:25:41,952:INFO:Checking exceptions
2024-04-21 13:25:41,952:INFO:Importing libraries
2024-04-21 13:25:41,952:INFO:Copying training dataset
2024-04-21 13:25:41,966:INFO:Defining folds
2024-04-21 13:25:41,966:INFO:Declaring metric variables
2024-04-21 13:25:41,968:INFO:Importing untrained model
2024-04-21 13:25:41,971:INFO:Ridge Classifier Imported successfully
2024-04-21 13:25:41,975:INFO:Starting cross validation
2024-04-21 13:25:41,976:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:25:42,089:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:25:42,094:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:25:42,122:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:25:42,129:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:42,139:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:25:42,165:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:25:42,170:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:42,180:INFO:Calculating mean and std
2024-04-21 13:25:42,184:INFO:Creating metrics dataframe
2024-04-21 13:25:42,188:INFO:Uploading results into container
2024-04-21 13:25:42,188:INFO:Uploading model into container now
2024-04-21 13:25:42,189:INFO:_master_model_container: 7
2024-04-21 13:25:42,190:INFO:_display_container: 2
2024-04-21 13:25:42,192: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)
2024-04-21 13:25:42,192:INFO:create_model() successfully completed......................................
2024-04-21 13:25:42,355:INFO:SubProcess create_model() end ==================================
2024-04-21 13:25:42,355:INFO:Creating metrics dataframe
2024-04-21 13:25:42,363:INFO:Initializing Random Forest Classifier
2024-04-21 13:25:42,363:INFO:Total runtime is 0.36449366410573325 minutes
2024-04-21 13:25:42,367:INFO:SubProcess create_model() called ==================================
2024-04-21 13:25:42,367:INFO:Initializing create_model()
2024-04-21 13:25:42,367:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=rf, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:25:42,369:INFO:Checking exceptions
2024-04-21 13:25:42,369:INFO:Importing libraries
2024-04-21 13:25:42,369:INFO:Copying training dataset
2024-04-21 13:25:42,390:INFO:Defining folds
2024-04-21 13:25:42,390:INFO:Declaring metric variables
2024-04-21 13:25:42,393:INFO:Importing untrained model
2024-04-21 13:25:42,396:INFO:Random Forest Classifier Imported successfully
2024-04-21 13:25:42,401:INFO:Starting cross validation
2024-04-21 13:25:42,402:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:25:46,425:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:46,528:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:46,547:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:46,672:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:46,820:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:46,850:INFO:Calculating mean and std
2024-04-21 13:25:46,856:INFO:Creating metrics dataframe
2024-04-21 13:25:46,865:INFO:Uploading results into container
2024-04-21 13:25:46,868:INFO:Uploading model into container now
2024-04-21 13:25:46,871:INFO:_master_model_container: 8
2024-04-21 13:25:46,871:INFO:_display_container: 2
2024-04-21 13:25:46,875: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)
2024-04-21 13:25:46,875:INFO:create_model() successfully completed......................................
2024-04-21 13:25:47,140:INFO:SubProcess create_model() end ==================================
2024-04-21 13:25:47,140:INFO:Creating metrics dataframe
2024-04-21 13:25:47,156:INFO:Initializing Quadratic Discriminant Analysis
2024-04-21 13:25:47,156:INFO:Total runtime is 0.4443730473518372 minutes
2024-04-21 13:25:47,159:INFO:SubProcess create_model() called ==================================
2024-04-21 13:25:47,160:INFO:Initializing create_model()
2024-04-21 13:25:47,160:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=qda, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:25:47,160:INFO:Checking exceptions
2024-04-21 13:25:47,160:INFO:Importing libraries
2024-04-21 13:25:47,160:INFO:Copying training dataset
2024-04-21 13:25:47,180:INFO:Defining folds
2024-04-21 13:25:47,181:INFO:Declaring metric variables
2024-04-21 13:25:47,184:INFO:Importing untrained model
2024-04-21 13:25:47,187:INFO:Quadratic Discriminant Analysis Imported successfully
2024-04-21 13:25:47,192:INFO:Starting cross validation
2024-04-21 13:25:47,194:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:25:47,376:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-21 13:25:47,376:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-21 13:25:47,380:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-21 13:25:47,478:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-21 13:25:47,491:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:47,498:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-21 13:25:47,512:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:47,515:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:47,524:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:47,588:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:47,595:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:47,600:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:47,611:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:25:47,628:INFO:Calculating mean and std
2024-04-21 13:25:47,630:INFO:Creating metrics dataframe
2024-04-21 13:25:47,634:INFO:Uploading results into container
2024-04-21 13:25:47,635:INFO:Uploading model into container now
2024-04-21 13:25:47,636:INFO:_master_model_container: 9
2024-04-21 13:25:47,636:INFO:_display_container: 2
2024-04-21 13:25:47,636:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,
store_covariance=False, tol=0.0001)
2024-04-21 13:25:47,636:INFO:create_model() successfully completed......................................
2024-04-21 13:25:47,776:INFO:SubProcess create_model() end ==================================
2024-04-21 13:25:47,776:INFO:Creating metrics dataframe
2024-04-21 13:25:47,783:INFO:Initializing Ada Boost Classifier
2024-04-21 13:25:47,783:INFO:Total runtime is 0.4548281788825989 minutes
2024-04-21 13:25:47,786:INFO:SubProcess create_model() called ==================================
2024-04-21 13:25:47,786:INFO:Initializing create_model()
2024-04-21 13:25:47,786:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=ada, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:25:47,786:INFO:Checking exceptions
2024-04-21 13:25:47,786:INFO:Importing libraries
2024-04-21 13:25:47,786:INFO:Copying training dataset
2024-04-21 13:25:47,801:INFO:Defining folds
2024-04-21 13:25:47,801:INFO:Declaring metric variables
2024-04-21 13:25:47,803:INFO:Importing untrained model
2024-04-21 13:25:47,805:INFO:Ada Boost Classifier Imported successfully
2024-04-21 13:25:47,810:INFO:Starting cross validation
2024-04-21 13:25:47,811:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:25:47,897:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-21 13:25:47,902:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-21 13:25:47,915:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-21 13:25:47,921:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-21 13:25:47,923:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-21 13:25:49,390:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:49,421:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:49,424:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:49,434:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:49,497:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:25:49,510:INFO:Calculating mean and std
2024-04-21 13:25:49,513:INFO:Creating metrics dataframe
2024-04-21 13:25:49,519:INFO:Uploading results into container
2024-04-21 13:25:49,519:INFO:Uploading model into container now
2024-04-21 13:25:49,520:INFO:_master_model_container: 10
2024-04-21 13:25:49,520:INFO:_display_container: 2
2024-04-21 13:25:49,521:INFO:AdaBoostClassifier(algorithm='SAMME.R', estimator=None, learning_rate=1.0,
n_estimators=50, random_state=123)
2024-04-21 13:25:49,521:INFO:create_model() successfully completed......................................
2024-04-21 13:25:49,725:INFO:SubProcess create_model() end ==================================
2024-04-21 13:25:49,725:INFO:Creating metrics dataframe
2024-04-21 13:25:49,732:INFO:Initializing Gradient Boosting Classifier
2024-04-21 13:25:49,732:INFO:Total runtime is 0.4873103459676107 minutes
2024-04-21 13:25:49,735:INFO:SubProcess create_model() called ==================================
2024-04-21 13:25:49,735:INFO:Initializing create_model()
2024-04-21 13:25:49,736:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=gbc, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:25:49,736:INFO:Checking exceptions
2024-04-21 13:25:49,736:INFO:Importing libraries
2024-04-21 13:25:49,736:INFO:Copying training dataset
2024-04-21 13:25:49,754:INFO:Defining folds
2024-04-21 13:25:49,754:INFO:Declaring metric variables
2024-04-21 13:25:49,757:INFO:Importing untrained model
2024-04-21 13:25:49,761:INFO:Gradient Boosting Classifier Imported successfully
2024-04-21 13:25:49,769:INFO:Starting cross validation
2024-04-21 13:25:49,771:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:26:31,086:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:31,311:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:31,380:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:31,466:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:31,503:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:31,522:INFO:Calculating mean and std
2024-04-21 13:26:31,529:INFO:Creating metrics dataframe
2024-04-21 13:26:31,556:INFO:Uploading results into container
2024-04-21 13:26:31,557:INFO:Uploading model into container now
2024-04-21 13:26:31,558:INFO:_master_model_container: 11
2024-04-21 13:26:31,558:INFO:_display_container: 2
2024-04-21 13:26:31,559: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)
2024-04-21 13:26:31,559:INFO:create_model() successfully completed......................................
2024-04-21 13:26:31,736:INFO:SubProcess create_model() end ==================================
2024-04-21 13:26:31,736:INFO:Creating metrics dataframe
2024-04-21 13:26:31,745:INFO:Initializing Linear Discriminant Analysis
2024-04-21 13:26:31,745:INFO:Total runtime is 1.1875176668167113 minutes
2024-04-21 13:26:31,747:INFO:SubProcess create_model() called ==================================
2024-04-21 13:26:31,747:INFO:Initializing create_model()
2024-04-21 13:26:31,747:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=lda, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:26:31,748:INFO:Checking exceptions
2024-04-21 13:26:31,748:INFO:Importing libraries
2024-04-21 13:26:31,748:INFO:Copying training dataset
2024-04-21 13:26:31,764:INFO:Defining folds
2024-04-21 13:26:31,764:INFO:Declaring metric variables
2024-04-21 13:26:31,766:INFO:Importing untrained model
2024-04-21 13:26:31,768:INFO:Linear Discriminant Analysis Imported successfully
2024-04-21 13:26:31,772:INFO:Starting cross validation
2024-04-21 13:26:31,774:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:26:31,955:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:31,968:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:31,991:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:31,991:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:31,997:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:32,012:INFO:Calculating mean and std
2024-04-21 13:26:32,013:INFO:Creating metrics dataframe
2024-04-21 13:26:32,015:INFO:Uploading results into container
2024-04-21 13:26:32,016:INFO:Uploading model into container now
2024-04-21 13:26:32,017:INFO:_master_model_container: 12
2024-04-21 13:26:32,017:INFO:_display_container: 2
2024-04-21 13:26:32,017:INFO:LinearDiscriminantAnalysis(covariance_estimator=None, n_components=None,
priors=None, shrinkage=None, solver='svd',
store_covariance=False, tol=0.0001)
2024-04-21 13:26:32,017:INFO:create_model() successfully completed......................................
2024-04-21 13:26:32,170:INFO:SubProcess create_model() end ==================================
2024-04-21 13:26:32,171:INFO:Creating metrics dataframe
2024-04-21 13:26:32,179:INFO:Initializing Extra Trees Classifier
2024-04-21 13:26:32,179:INFO:Total runtime is 1.1947577476501463 minutes
2024-04-21 13:26:32,182:INFO:SubProcess create_model() called ==================================
2024-04-21 13:26:32,182:INFO:Initializing create_model()
2024-04-21 13:26:32,182:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=et, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:26:32,183:INFO:Checking exceptions
2024-04-21 13:26:32,183:INFO:Importing libraries
2024-04-21 13:26:32,183:INFO:Copying training dataset
2024-04-21 13:26:32,199:INFO:Defining folds
2024-04-21 13:26:32,199:INFO:Declaring metric variables
2024-04-21 13:26:32,202:INFO:Importing untrained model
2024-04-21 13:26:32,205:INFO:Extra Trees Classifier Imported successfully
2024-04-21 13:26:32,210:INFO:Starting cross validation
2024-04-21 13:26:32,211:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:26:34,255:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:34,393:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:34,500:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:34,505:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:34,528:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:34,545:INFO:Calculating mean and std
2024-04-21 13:26:34,549:INFO:Creating metrics dataframe
2024-04-21 13:26:34,555:INFO:Uploading results into container
2024-04-21 13:26:34,556:INFO:Uploading model into container now
2024-04-21 13:26:34,556:INFO:_master_model_container: 13
2024-04-21 13:26:34,556:INFO:_display_container: 2
2024-04-21 13:26:34,557:INFO:ExtraTreesClassifier(bootstrap=False, 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)
2024-04-21 13:26:34,558:INFO:create_model() successfully completed......................................
2024-04-21 13:26:34,764:INFO:SubProcess create_model() end ==================================
2024-04-21 13:26:34,765:INFO:Creating metrics dataframe
2024-04-21 13:26:34,776:INFO:Initializing Extreme Gradient Boosting
2024-04-21 13:26:34,776:INFO:Total runtime is 1.2380379001299537 minutes
2024-04-21 13:26:34,778:INFO:SubProcess create_model() called ==================================
2024-04-21 13:26:34,779:INFO:Initializing create_model()
2024-04-21 13:26:34,779:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=xgboost, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:26:34,779:INFO:Checking exceptions
2024-04-21 13:26:34,779:INFO:Importing libraries
2024-04-21 13:26:34,779:INFO:Copying training dataset
2024-04-21 13:26:34,794:INFO:Defining folds
2024-04-21 13:26:34,794:INFO:Declaring metric variables
2024-04-21 13:26:34,797:INFO:Importing untrained model
2024-04-21 13:26:34,799:INFO:Extreme Gradient Boosting Imported successfully
2024-04-21 13:26:34,804:INFO:Starting cross validation
2024-04-21 13:26:34,805:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:26:58,958:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:59,243:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:59,246:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:59,304:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:59,356:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:26:59,389:INFO:Calculating mean and std
2024-04-21 13:26:59,394:INFO:Creating metrics dataframe
2024-04-21 13:26:59,402:INFO:Uploading results into container
2024-04-21 13:26:59,402:INFO:Uploading model into container now
2024-04-21 13:26:59,403:INFO:_master_model_container: 14
2024-04-21 13:26:59,404:INFO:_display_container: 2
2024-04-21 13:26:59,405:INFO:XGBClassifier(base_score=None, booster='gbtree', callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=100, n_jobs=-1, num_parallel_tree=None,
objective='binary:logistic', predictor=None, ...)
2024-04-21 13:26:59,406:INFO:create_model() successfully completed......................................
2024-04-21 13:26:59,570:INFO:SubProcess create_model() end ==================================
2024-04-21 13:26:59,570:INFO:Creating metrics dataframe
2024-04-21 13:26:59,583:INFO:Initializing Light Gradient Boosting Machine
2024-04-21 13:26:59,583:INFO:Total runtime is 1.6514953613281247 minutes
2024-04-21 13:26:59,586:INFO:SubProcess create_model() called ==================================
2024-04-21 13:26:59,586:INFO:Initializing create_model()
2024-04-21 13:26:59,586:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=lightgbm, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:26:59,587:INFO:Checking exceptions
2024-04-21 13:26:59,587:INFO:Importing libraries
2024-04-21 13:26:59,587:INFO:Copying training dataset
2024-04-21 13:26:59,602:INFO:Defining folds
2024-04-21 13:26:59,602:INFO:Declaring metric variables
2024-04-21 13:26:59,605:INFO:Importing untrained model
2024-04-21 13:26:59,607:INFO:Light Gradient Boosting Machine Imported successfully
2024-04-21 13:26:59,612:INFO:Starting cross validation
2024-04-21 13:26:59,613:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:28:01,558:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:28:01,813:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:28:01,818:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:28:01,820:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:28:01,901:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:28:01,921:INFO:Calculating mean and std
2024-04-21 13:28:01,925:INFO:Creating metrics dataframe
2024-04-21 13:28:01,932:INFO:Uploading results into container
2024-04-21 13:28:01,933:INFO:Uploading model into container now
2024-04-21 13:28:01,934:INFO:_master_model_container: 15
2024-04-21 13:28:01,934:INFO:_display_container: 2
2024-04-21 13:28:01,935:INFO:LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,
importance_type='split', learning_rate=0.1, max_depth=-1,
min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,
random_state=123, reg_alpha=0.0, reg_lambda=0.0, subsample=1.0,
subsample_for_bin=200000, subsample_freq=0)
2024-04-21 13:28:01,936:INFO:create_model() successfully completed......................................
2024-04-21 13:28:02,181:INFO:SubProcess create_model() end ==================================
2024-04-21 13:28:02,181:INFO:Creating metrics dataframe
2024-04-21 13:28:02,190:INFO:Initializing CatBoost Classifier
2024-04-21 13:28:02,190:INFO:Total runtime is 2.694938079516093 minutes
2024-04-21 13:28:02,195:INFO:SubProcess create_model() called ==================================
2024-04-21 13:28:02,196:INFO:Initializing create_model()
2024-04-21 13:28:02,196:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=catboost, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:28:02,196:INFO:Checking exceptions
2024-04-21 13:28:02,196:INFO:Importing libraries
2024-04-21 13:28:02,196:INFO:Copying training dataset
2024-04-21 13:28:02,224:INFO:Defining folds
2024-04-21 13:28:02,224:INFO:Declaring metric variables
2024-04-21 13:28:02,227:INFO:Importing untrained model
2024-04-21 13:28:02,235:INFO:CatBoost Classifier Imported successfully
2024-04-21 13:28:02,241:INFO:Starting cross validation
2024-04-21 13:28:02,243:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:29:32,032:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:32,516:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:32,772:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:32,793:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:33,066:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:33,093:INFO:Calculating mean and std
2024-04-21 13:29:33,097:INFO:Creating metrics dataframe
2024-04-21 13:29:33,103:INFO:Uploading results into container
2024-04-21 13:29:33,104:INFO:Uploading model into container now
2024-04-21 13:29:33,105:INFO:_master_model_container: 16
2024-04-21 13:29:33,105:INFO:_display_container: 2
2024-04-21 13:29:33,105:INFO:<catboost.core.CatBoostClassifier object at 0x28bbdfd30>
2024-04-21 13:29:33,105:INFO:create_model() successfully completed......................................
2024-04-21 13:29:33,298:INFO:SubProcess create_model() end ==================================
2024-04-21 13:29:33,298:INFO:Creating metrics dataframe
2024-04-21 13:29:33,307:INFO:Initializing Dummy Classifier
2024-04-21 13:29:33,307:INFO:Total runtime is 4.21356391509374 minutes
2024-04-21 13:29:33,310:INFO:SubProcess create_model() called ==================================
2024-04-21 13:29:33,310:INFO:Initializing create_model()
2024-04-21 13:29:33,310:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=dummy, fold=StratifiedKFold(n_splits=5, 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 0x1063d56c0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:29:33,310:INFO:Checking exceptions
2024-04-21 13:29:33,310:INFO:Importing libraries
2024-04-21 13:29:33,310:INFO:Copying training dataset
2024-04-21 13:29:33,323:INFO:Defining folds
2024-04-21 13:29:33,323:INFO:Declaring metric variables
2024-04-21 13:29:33,325:INFO:Importing untrained model
2024-04-21 13:29:33,327:INFO:Dummy Classifier Imported successfully
2024-04-21 13:29:33,331:INFO:Starting cross validation
2024-04-21 13:29:33,332:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:29:33,476:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:33,481:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:33,495:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:33,500:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:33,511:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:33,517:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:33,541:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:33,547:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:33,593:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:33,600:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:33,610:INFO:Calculating mean and std
2024-04-21 13:29:33,614:INFO:Creating metrics dataframe
2024-04-21 13:29:33,616:INFO:Uploading results into container
2024-04-21 13:29:33,617:INFO:Uploading model into container now
2024-04-21 13:29:33,618:INFO:_master_model_container: 17
2024-04-21 13:29:33,618:INFO:_display_container: 2
2024-04-21 13:29:33,618:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:29:33,618:INFO:create_model() successfully completed......................................
2024-04-21 13:29:33,726:INFO:SubProcess create_model() end ==================================
2024-04-21 13:29:33,726:INFO:Creating metrics dataframe
2024-04-21 13:29:33,735:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py:323: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead.
master_display_.apply(
2024-04-21 13:29:33,740:INFO:Initializing create_model()
2024-04-21 13:29:33,740:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:29:33,740:INFO:Checking exceptions
2024-04-21 13:29:33,741:INFO:Importing libraries
2024-04-21 13:29:33,741:INFO:Copying training dataset
2024-04-21 13:29:33,756:INFO:Defining folds
2024-04-21 13:29:33,756:INFO:Declaring metric variables
2024-04-21 13:29:33,757:INFO:Importing untrained model
2024-04-21 13:29:33,757:INFO:Declaring custom model
2024-04-21 13:29:33,757:INFO:Dummy Classifier Imported successfully
2024-04-21 13:29:33,758:INFO:Cross validation set to False
2024-04-21 13:29:33,758:INFO:Fitting Model
2024-04-21 13:29:33,795:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:29:33,795:INFO:create_model() successfully completed......................................
2024-04-21 13:29:33,932:INFO:Creating Dashboard logs
2024-04-21 13:29:33,938:INFO:Model: Dummy Classifier
2024-04-21 13:29:33,966:INFO:Logged params: {'constant': None, 'random_state': 123, 'strategy': 'prior'}
2024-04-21 13:29:34,008:INFO:Initializing predict_model()
2024-04-21 13:29:34,008:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x13019e710>)
2024-04-21 13:29:34,008:INFO:Checking exceptions
2024-04-21 13:29:34,009:INFO:Preloading libraries
2024-04-21 13:29:34,468:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py:585: UserWarning: Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 580, in _calculate_metric
calculated_metric = score_func(y_test, target, sample_weight=weights, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 583, in _calculate_metric
calculated_metric = score_func(y_test, target, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
warnings.warn(traceback.format_exc())
2024-04-21 13:29:34,485:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:35,188:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/_distutils_hack/__init__.py:36: UserWarning: Setuptools is replacing distutils.
warnings.warn("Setuptools is replacing distutils.")
2024-04-21 13:29:36,169:INFO:Creating Dashboard logs
2024-04-21 13:29:36,172:INFO:Model: Logistic Regression
2024-04-21 13:29:36,180:INFO:Logged params: {'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}
2024-04-21 13:29:36,307:INFO:Creating Dashboard logs
2024-04-21 13:29:36,309:INFO:Model: Ridge Classifier
2024-04-21 13:29:36,322:INFO:Logged params: {'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}
2024-04-21 13:29:36,464:INFO:Creating Dashboard logs
2024-04-21 13:29:36,467:INFO:Model: Linear Discriminant Analysis
2024-04-21 13:29:36,475:INFO:Logged params: {'covariance_estimator': None, 'n_components': None, 'priors': None, 'shrinkage': None, 'solver': 'svd', 'store_covariance': False, 'tol': 0.0001}
2024-04-21 13:29:36,645:INFO:Creating Dashboard logs
2024-04-21 13:29:36,647:INFO:Model: Ada Boost Classifier
2024-04-21 13:29:36,657:INFO:Logged params: {'algorithm': 'SAMME.R', 'estimator': None, 'learning_rate': 1.0, 'n_estimators': 50, 'random_state': 123}
2024-04-21 13:29:36,790:INFO:Creating Dashboard logs
2024-04-21 13:29:36,792:INFO:Model: Gradient Boosting Classifier
2024-04-21 13:29:36,801:INFO:Logged params: {'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}
2024-04-21 13:29:36,930:INFO:Creating Dashboard logs
2024-04-21 13:29:36,933:INFO:Model: Extreme Gradient Boosting
2024-04-21 13:29:36,943:INFO:Logged params: {'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': 'gbtree', 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 100, 'n_jobs': -1, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': 'auto', 'validate_parameters': None, 'verbosity': 0}
2024-04-21 13:29:37,120:INFO:Creating Dashboard logs
2024-04-21 13:29:37,122:INFO:Model: Random Forest Classifier
2024-04-21 13:29:37,136:INFO:Logged params: {'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}
2024-04-21 13:29:37,305:INFO:Creating Dashboard logs
2024-04-21 13:29:37,316:INFO:Model: CatBoost Classifier
2024-04-21 13:29:37,347:WARNING:Couldn't get params for model. Exception:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/loggers/dashboard_logger.py", line 78, in log_model
params = params.get_all_params()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/catboost/core.py", line 3413, in get_all_params
raise CatBoostError("There is no trained model to use get_all_params(). Use fit() to train model. Then use this method.")
_catboost.CatBoostError: There is no trained model to use get_all_params(). Use fit() to train model. Then use this method.
2024-04-21 13:29:37,347:INFO:Logged params: {}
2024-04-21 13:29:37,471:INFO:Creating Dashboard logs
2024-04-21 13:29:37,473:INFO:Model: Extra Trees Classifier
2024-04-21 13:29:37,490:INFO:Logged params: {'bootstrap': False, '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}
2024-04-21 13:29:37,634:INFO:Creating Dashboard logs
2024-04-21 13:29:37,636:INFO:Model: Light Gradient Boosting Machine
2024-04-21 13:29:37,648:INFO:Logged params: {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 31, 'objective': None, 'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0}
2024-04-21 13:29:37,771:INFO:Creating Dashboard logs
2024-04-21 13:29:37,774:INFO:Model: Decision Tree Classifier
2024-04-21 13:29:37,783:INFO:Logged params: {'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'}
2024-04-21 13:29:37,924:INFO:Creating Dashboard logs
2024-04-21 13:29:37,927:INFO:Model: SVM - Linear Kernel
2024-04-21 13:29:37,937:INFO:Logged params: {'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}
2024-04-21 13:29:38,074:INFO:Creating Dashboard logs
2024-04-21 13:29:38,077:INFO:Model: Naive Bayes
2024-04-21 13:29:38,091:INFO:Logged params: {'priors': None, 'var_smoothing': 1e-09}
2024-04-21 13:29:38,239:INFO:Creating Dashboard logs
2024-04-21 13:29:38,242:INFO:Model: Quadratic Discriminant Analysis
2024-04-21 13:29:38,255:INFO:Logged params: {'priors': None, 'reg_param': 0.0, 'store_covariance': False, 'tol': 0.0001}
2024-04-21 13:29:38,399:INFO:_master_model_container: 17
2024-04-21 13:29:38,400:INFO:_display_container: 2
2024-04-21 13:29:38,400:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:29:38,400:INFO:compare_models() successfully completed......................................
2024-04-21 13:29:38,407:INFO:Initializing tune_model()
2024-04-21 13:29:38,408:INFO:tune_model(estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fold=5, round=4, n_iter=10, custom_grid=None, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}, self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>)
2024-04-21 13:29:38,408:INFO:Checking exceptions
2024-04-21 13:29:38,422:INFO:Copying training dataset
2024-04-21 13:29:38,431:INFO:Checking base model
2024-04-21 13:29:38,431:INFO:Base model : Dummy Classifier
2024-04-21 13:29:38,433:INFO:Declaring metric variables
2024-04-21 13:29:38,435:INFO:Defining Hyperparameters
2024-04-21 13:29:38,435:INFO:10 is bigger than total combinations 4, setting search algorithm to grid
2024-04-21 13:29:38,522:INFO:Tuning with n_jobs=-1
2024-04-21 13:29:38,522:INFO:Initializing GridSearchCV
2024-04-21 13:29:39,257:INFO:best_params: {'actual_estimator__strategy': 'most_frequent'}
2024-04-21 13:29:39,258:INFO:Hyperparameter search completed
2024-04-21 13:29:39,258:INFO:SubProcess create_model() called ==================================
2024-04-21 13:29:39,260:INFO:Initializing create_model()
2024-04-21 13:29:39,260:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x28bcfe380>, model_only=True, return_train_score=False, error_score=0.0, kwargs={'strategy': 'most_frequent'})
2024-04-21 13:29:39,260:INFO:Checking exceptions
2024-04-21 13:29:39,260:INFO:Importing libraries
2024-04-21 13:29:39,260:INFO:Copying training dataset
2024-04-21 13:29:39,305:INFO:Defining folds
2024-04-21 13:29:39,305:INFO:Declaring metric variables
2024-04-21 13:29:39,313:INFO:Importing untrained model
2024-04-21 13:29:39,313:INFO:Declaring custom model
2024-04-21 13:29:39,320:INFO:Dummy Classifier Imported successfully
2024-04-21 13:29:39,328:INFO:Starting cross validation
2024-04-21 13:29:39,331:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:29:39,473:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:39,483:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:39,488:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:39,500:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:39,504:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:39,504:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:39,507:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:39,512:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:39,987:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:39,991:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:39,997:INFO:Calculating mean and std
2024-04-21 13:29:39,999:INFO:Creating metrics dataframe
2024-04-21 13:29:40,003:INFO:Finalizing model
2024-04-21 13:29:40,053:INFO:Uploading results into container
2024-04-21 13:29:40,053:INFO:Uploading model into container now
2024-04-21 13:29:40,054:INFO:_master_model_container: 18
2024-04-21 13:29:40,054:INFO:_display_container: 3
2024-04-21 13:29:40,054:INFO:DummyClassifier(constant=None, random_state=123, strategy='most_frequent')
2024-04-21 13:29:40,054:INFO:create_model() successfully completed......................................
2024-04-21 13:29:40,218:INFO:SubProcess create_model() end ==================================
2024-04-21 13:29:40,219:INFO:choose_better activated
2024-04-21 13:29:40,221:INFO:SubProcess create_model() called ==================================
2024-04-21 13:29:40,221:INFO:Initializing create_model()
2024-04-21 13:29:40,221:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:29:40,221:INFO:Checking exceptions
2024-04-21 13:29:40,222:INFO:Importing libraries
2024-04-21 13:29:40,223:INFO:Copying training dataset
2024-04-21 13:29:40,234:INFO:Defining folds
2024-04-21 13:29:40,235:INFO:Declaring metric variables
2024-04-21 13:29:40,235:INFO:Importing untrained model
2024-04-21 13:29:40,235:INFO:Declaring custom model
2024-04-21 13:29:40,235:INFO:Dummy Classifier Imported successfully
2024-04-21 13:29:40,235:INFO:Starting cross validation
2024-04-21 13:29:40,236:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:29:40,380:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:40,387:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:40,402:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:40,405:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:40,412:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:40,422:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:40,433:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:40,444:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:40,451:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:29:40,458:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:40,471:INFO:Calculating mean and std
2024-04-21 13:29:40,472:INFO:Creating metrics dataframe
2024-04-21 13:29:40,476:INFO:Finalizing model
2024-04-21 13:29:40,558:INFO:Uploading results into container
2024-04-21 13:29:40,559:INFO:Uploading model into container now
2024-04-21 13:29:40,559:INFO:_master_model_container: 19
2024-04-21 13:29:40,559:INFO:_display_container: 4
2024-04-21 13:29:40,559:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:29:40,559:INFO:create_model() successfully completed......................................
2024-04-21 13:29:40,703:INFO:SubProcess create_model() end ==================================
2024-04-21 13:29:40,704:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior') result for Accuracy is 0.1979
2024-04-21 13:29:40,704:INFO:DummyClassifier(constant=None, random_state=123, strategy='most_frequent') result for Accuracy is 0.1979
2024-04-21 13:29:40,704:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior') is best model
2024-04-21 13:29:40,704:INFO:choose_better completed
2024-04-21 13:29:40,704:INFO:Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).
2024-04-21 13:29:40,704:INFO:Creating Dashboard logs
2024-04-21 13:29:40,708:INFO:Model: Dummy Classifier
2024-04-21 13:29:40,723:INFO:Logged params: {'constant': None, 'random_state': 123, 'strategy': 'prior'}
2024-04-21 13:29:40,765:INFO:Initializing predict_model()
2024-04-21 13:29:40,765:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x13020c9d0>)
2024-04-21 13:29:40,765:INFO:Checking exceptions
2024-04-21 13:29:40,765:INFO:Preloading libraries
2024-04-21 13:29:40,814:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py:585: UserWarning: Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 580, in _calculate_metric
calculated_metric = score_func(y_test, target, sample_weight=weights, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 583, in _calculate_metric
calculated_metric = score_func(y_test, target, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
warnings.warn(traceback.format_exc())
2024-04-21 13:29:40,818:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:41,030:INFO:_master_model_container: 19
2024-04-21 13:29:41,030:INFO:_display_container: 3
2024-04-21 13:29:41,030:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:29:41,030:INFO:tune_model() successfully completed......................................
2024-04-21 13:29:41,117:INFO:Initializing finalize_model()
2024-04-21 13:29:41,118:INFO:finalize_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fit_kwargs=None, groups=None, model_only=False, experiment_custom_tags=None)
2024-04-21 13:29:41,118:INFO:Finalizing DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:29:41,124:INFO:Initializing create_model()
2024-04-21 13:29:41,124:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fold=None, round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=False, metrics=None, display=None, model_only=False, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:29:41,124:INFO:Checking exceptions
2024-04-21 13:29:41,125:INFO:Importing libraries
2024-04-21 13:29:41,125:INFO:Copying training dataset
2024-04-21 13:29:41,126:INFO:Defining folds
2024-04-21 13:29:41,126:INFO:Declaring metric variables
2024-04-21 13:29:41,126:INFO:Importing untrained model
2024-04-21 13:29:41,126:INFO:Declaring custom model
2024-04-21 13:29:41,126:INFO:Dummy Classifier Imported successfully
2024-04-21 13:29:41,127:INFO:Cross validation set to False
2024-04-21 13:29:41,127:INFO:Fitting Model
2024-04-21 13:29:41,169:INFO:Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Scaled_Age',
'Log_Age', 'Scaled_Weight',
'Log_Weight', 'Scaled_Height',
'Log_Height', 'BMI', 'Age^2',
'Age^3', 'BMI^2'...
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
DummyClassifier(constant=None, random_state=123,
strategy='prior'))],
verbose=False)
2024-04-21 13:29:41,169:INFO:create_model() successfully completed......................................
2024-04-21 13:29:41,262:INFO:Creating Dashboard logs
2024-04-21 13:29:41,262:INFO:Model: Dummy Classifier
2024-04-21 13:29:41,275:INFO:Logged params: {'constant': None, 'random_state': 123, 'strategy': 'prior'}
2024-04-21 13:29:41,387:INFO:_master_model_container: 19
2024-04-21 13:29:41,387:INFO:_display_container: 3
2024-04-21 13:29:41,391:INFO:Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Scaled_Age',
'Log_Age', 'Scaled_Weight',
'Log_Weight', 'Scaled_Height',
'Log_Height', 'BMI', 'Age^2',
'Age^3', 'BMI^2'...
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
DummyClassifier(constant=None, random_state=123,
strategy='prior'))],
verbose=False)
2024-04-21 13:29:41,391:INFO:finalize_model() successfully completed......................................
2024-04-21 13:29:41,480:INFO:Initializing predict_model()
2024-04-21 13:29:41,480:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Scaled_Age',
'Log_Age', 'Scaled_Weight',
'Log_Weight', 'Scaled_Height',
'Log_Height', 'BMI', 'Age^2',
'Age^3', 'BMI^2'...
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
DummyClassifier(constant=None, random_state=123,
strategy='prior'))],
verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x28c748ca0>)
2024-04-21 13:29:41,480:INFO:Checking exceptions
2024-04-21 13:29:41,480:INFO:Preloading libraries
2024-04-21 13:29:41,482:INFO:Set up data.
2024-04-21 13:29:41,489:INFO:Set up index.
2024-04-21 13:29:41,506:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py:585: UserWarning: Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 580, in _calculate_metric
calculated_metric = score_func(y_test, target, sample_weight=weights, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 583, in _calculate_metric
calculated_metric = score_func(y_test, target, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
warnings.warn(traceback.format_exc())
2024-04-21 13:29:41,509:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:29:41,607:INFO:Initializing predict_model()
2024-04-21 13:29:41,608:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289ae88e0>, estimator=Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Scaled_Age',
'Log_Age', 'Scaled_Weight',
'Log_Weight', 'Scaled_Height',
'Log_Height', 'BMI', 'Age^2',
'Age^3', 'BMI^2'...
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
DummyClassifier(constant=None, random_state=123,
strategy='prior'))],
verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x290947d90>)
2024-04-21 13:29:41,608:INFO:Checking exceptions
2024-04-21 13:29:41,608:INFO:Preloading libraries
2024-04-21 13:29:41,609:INFO:Set up data.
2024-04-21 13:29:41,620:INFO:Set up index.
2024-04-21 13:29:41,640:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py:585: UserWarning: Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 580, in _calculate_metric
calculated_metric = score_func(y_test, target, sample_weight=weights, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 583, in _calculate_metric
calculated_metric = score_func(y_test, target, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
warnings.warn(traceback.format_exc())
2024-04-21 13:29:41,646:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:43,657:INFO:PyCaret ClassificationExperiment
2024-04-21 13:32:43,657:INFO:Logging name: obesity_classification
2024-04-21 13:32:43,657:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2024-04-21 13:32:43,657:INFO:version 3.3.0
2024-04-21 13:32:43,657:INFO:Initializing setup()
2024-04-21 13:32:43,658:INFO:self.USI: be08
2024-04-21 13:32:43,658:INFO:self._variable_keys: {'target_param', 'exp_id', 'data', 'idx', 'fold_shuffle_param', 'log_plots_param', 'fold_groups_param', 'html_param', 'seed', 'exp_name_log', '_available_plots', 'y_train', 'X_train', '_ml_usecase', 'pipeline', 'USI', 'gpu_param', 'fold_generator', 'y', 'X_test', 'fix_imbalance', 'is_multiclass', 'memory', 'n_jobs_param', 'y_test', 'gpu_n_jobs_param', 'logging_param', 'X'}
2024-04-21 13:32:43,658:INFO:Checking environment
2024-04-21 13:32:43,658:INFO:python_version: 3.10.13
2024-04-21 13:32:43,658:INFO:python_build: ('main', 'Sep 11 2023 08:16:02')
2024-04-21 13:32:43,658:INFO:machine: arm64
2024-04-21 13:32:43,658:INFO:platform: macOS-14.0-arm64-arm-64bit
2024-04-21 13:32:43,658:INFO:Memory: svmem(total=8589934592, available=1242578944, percent=85.5, used=3154051072, free=40615936, active=1220067328, inactive=1199833088, wired=1933983744)
2024-04-21 13:32:43,658:INFO:Physical Core: 8
2024-04-21 13:32:43,658:INFO:Logical Core: 8
2024-04-21 13:32:43,658:INFO:Checking libraries
2024-04-21 13:32:43,658:INFO:System:
2024-04-21 13:32:43,658:INFO: python: 3.10.13 (main, Sep 11 2023, 08:16:02) [Clang 14.0.6 ]
2024-04-21 13:32:43,658:INFO:executable: /Users/arham/anaconda3/envs/DataScience/bin/python
2024-04-21 13:32:43,658:INFO: machine: macOS-14.0-arm64-arm-64bit
2024-04-21 13:32:43,658:INFO:PyCaret required dependencies:
2024-04-21 13:32:43,658:INFO: pip: 23.3
2024-04-21 13:32:43,659:INFO: setuptools: 60.2.0
2024-04-21 13:32:43,659:INFO: pycaret: 3.3.0
2024-04-21 13:32:43,659:INFO: IPython: 8.15.0
2024-04-21 13:32:43,659:INFO: ipywidgets: 8.0.4
2024-04-21 13:32:43,659:INFO: tqdm: 4.65.2
2024-04-21 13:32:43,659:INFO: numpy: 1.25.2
2024-04-21 13:32:43,659:INFO: pandas: 2.2.2
2024-04-21 13:32:43,659:INFO: jinja2: 3.1.2
2024-04-21 13:32:43,659:INFO: scipy: 1.11.4
2024-04-21 13:32:43,659:INFO: joblib: 1.2.0
2024-04-21 13:32:43,659:INFO: sklearn: 1.4.0
2024-04-21 13:32:43,659:INFO: pyod: 1.1.3
2024-04-21 13:32:43,659:INFO: imblearn: 0.12.2
2024-04-21 13:32:43,659:INFO: category_encoders: 2.6.3
2024-04-21 13:32:43,659:INFO: lightgbm: 4.1.0
2024-04-21 13:32:43,659:INFO: numba: 0.58.1
2024-04-21 13:32:43,659:INFO: requests: 2.28.2
2024-04-21 13:32:43,659:INFO: matplotlib: 3.6.2
2024-04-21 13:32:43,659:INFO: scikitplot: 0.3.7
2024-04-21 13:32:43,659:INFO: yellowbrick: 1.5
2024-04-21 13:32:43,659:INFO: plotly: 5.17.0
2024-04-21 13:32:43,659:INFO: plotly-resampler: Not installed
2024-04-21 13:32:43,659:INFO: kaleido: 0.2.1
2024-04-21 13:32:43,659:INFO: schemdraw: 0.15
2024-04-21 13:32:43,659:INFO: statsmodels: 0.13.5
2024-04-21 13:32:43,659:INFO: sktime: 0.26.1
2024-04-21 13:32:43,659:INFO: tbats: 1.1.3
2024-04-21 13:32:43,659:INFO: pmdarima: 2.0.4
2024-04-21 13:32:43,659:INFO: psutil: 5.9.0
2024-04-21 13:32:43,659:INFO: markupsafe: 2.1.1
2024-04-21 13:32:43,659:INFO: pickle5: Not installed
2024-04-21 13:32:43,659:INFO: cloudpickle: 2.2.1
2024-04-21 13:32:43,659:INFO: deprecation: 2.1.0
2024-04-21 13:32:43,659:INFO: xxhash: 3.4.1
2024-04-21 13:32:43,659:INFO: wurlitzer: 3.0.2
2024-04-21 13:32:43,659:INFO:PyCaret optional dependencies:
2024-04-21 13:32:43,659:INFO: shap: 0.44.0
2024-04-21 13:32:43,659:INFO: interpret: Not installed
2024-04-21 13:32:43,659:INFO: umap: Not installed
2024-04-21 13:32:43,659:INFO: ydata_profiling: 0.0.dev0
2024-04-21 13:32:43,659:INFO: explainerdashboard: Not installed
2024-04-21 13:32:43,659:INFO: autoviz: Not installed
2024-04-21 13:32:43,659:INFO: fairlearn: Not installed
2024-04-21 13:32:43,659:INFO: deepchecks: Not installed
2024-04-21 13:32:43,659:INFO: xgboost: 1.7.3
2024-04-21 13:32:43,659:INFO: catboost: 1.1.1
2024-04-21 13:32:43,659:INFO: kmodes: Not installed
2024-04-21 13:32:43,659:INFO: mlxtend: Not installed
2024-04-21 13:32:43,659:INFO: statsforecast: 1.4.0
2024-04-21 13:32:43,659:INFO: tune_sklearn: Not installed
2024-04-21 13:32:43,660:INFO: ray: 2.10.0
2024-04-21 13:32:43,660:INFO: hyperopt: 0.2.7
2024-04-21 13:32:43,660:INFO: optuna: 3.5.0
2024-04-21 13:32:43,660:INFO: skopt: Not installed
2024-04-21 13:32:43,660:INFO: mlflow: 2.10.2
2024-04-21 13:32:43,660:INFO: gradio: 3.48.0
2024-04-21 13:32:43,660:INFO: fastapi: 0.109.2
2024-04-21 13:32:43,660:INFO: uvicorn: 0.27.1
2024-04-21 13:32:43,660:INFO: m2cgen: Not installed
2024-04-21 13:32:43,660:INFO: evidently: Not installed
2024-04-21 13:32:43,660:INFO: fugue: Not installed
2024-04-21 13:32:43,660:INFO: streamlit: 1.27.2
2024-04-21 13:32:43,660:INFO: prophet: Not installed
2024-04-21 13:32:43,660:INFO:None
2024-04-21 13:32:43,660:INFO:Set up data.
2024-04-21 13:32:43,667:INFO:Set up folding strategy.
2024-04-21 13:32:43,667:INFO:Set up train/test split.
2024-04-21 13:32:43,679:INFO:Set up index.
2024-04-21 13:32:43,679:INFO:Assigning column types.
2024-04-21 13:32:43,690:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2024-04-21 13:32:43,727:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-21 13:32:43,729:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:32:43,749:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:32:43,751:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:32:43,782:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-21 13:32:43,782:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:32:43,800:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:32:43,802:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:32:43,802:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2024-04-21 13:32:43,831:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:32:43,849:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:32:43,851:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:32:43,880:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:32:43,898:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:32:43,899:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:32:43,899:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2024-04-21 13:32:43,946:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:32:43,948:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:32:43,995:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:32:43,997:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:32:43,998:INFO:Preparing preprocessing pipeline...
2024-04-21 13:32:44,000:INFO:Set up simple imputation.
2024-04-21 13:32:44,005:INFO:Set up encoding of categorical features.
2024-04-21 13:32:44,006:INFO:Set up column name cleaning.
2024-04-21 13:32:44,060:INFO:Finished creating preprocessing pipeline.
2024-04-21 13:32:44,064:INFO:Pipeline: Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Ag...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False)
2024-04-21 13:32:44,064:INFO:Creating final display dataframe.
2024-04-21 13:32:44,270:INFO:Setup _display_container: Description Value
0 Session id 123
1 Target NObeyesdad
2 Target type Multiclass
3 Original data shape (10793, 29)
4 Transformed data shape (10793, 33)
5 Transformed train set shape (8634, 33)
6 Transformed test set shape (2159, 33)
7 Numeric features 22
8 Categorical features 1
9 Preprocess True
10 Imputation type simple
11 Numeric imputation mean
12 Categorical imputation mode
13 Maximum one-hot encoding 25
14 Encoding method None
15 Fold Generator StratifiedKFold
16 Fold Number 10
17 CPU Jobs -1
18 Use GPU False
19 Log Experiment MlflowLogger
20 Experiment Name obesity_classification
21 USI be08
2024-04-21 13:32:44,324:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:32:44,326:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:32:44,373:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:32:44,375:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:32:44,376:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour.
warnings.warn(
2024-04-21 13:32:44,377:INFO:Logging experiment in loggers
2024-04-21 13:32:44,399:INFO:SubProcess save_model() called ==================================
2024-04-21 13:32:44,406:INFO:Initializing save_model()
2024-04-21 13:32:44,406:INFO:save_model(model=Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Ag...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False), model_name=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/tmpl13xil5k/Transformation Pipeline, prep_pipe_=Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Ag...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False), verbose=False, use_case=MLUsecase.CLASSIFICATION, kwargs={})
2024-04-21 13:32:44,406:INFO:Adding model into prep_pipe
2024-04-21 13:32:44,406:WARNING:Only Model saved as it was a pipeline.
2024-04-21 13:32:44,410:INFO:/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/tmpl13xil5k/Transformation Pipeline.pkl saved in current working directory
2024-04-21 13:32:44,413:INFO:Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Ag...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False)
2024-04-21 13:32:44,413:INFO:save_model() successfully completed......................................
2024-04-21 13:32:44,589:INFO:SubProcess save_model() end ==================================
2024-04-21 13:32:44,593:INFO:setup() successfully completed in 0.73s...............
2024-04-21 13:32:44,597:INFO:Initializing compare_models()
2024-04-21 13:32:44,598:INFO:compare_models(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, include=None, fold=5, 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 0x173f0f670>, 'include': None, 'exclude': None, 'fold': 5, '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)
2024-04-21 13:32:44,598:INFO:Checking exceptions
2024-04-21 13:32:44,605:INFO:Preparing display monitor
2024-04-21 13:32:44,618:INFO:Initializing Logistic Regression
2024-04-21 13:32:44,618:INFO:Total runtime is 2.1696090698242187e-06 minutes
2024-04-21 13:32:44,620:INFO:SubProcess create_model() called ==================================
2024-04-21 13:32:44,620:INFO:Initializing create_model()
2024-04-21 13:32:44,620:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=lr, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:32:44,620:INFO:Checking exceptions
2024-04-21 13:32:44,620:INFO:Importing libraries
2024-04-21 13:32:44,620:INFO:Copying training dataset
2024-04-21 13:32:44,631:INFO:Defining folds
2024-04-21 13:32:44,631:INFO:Declaring metric variables
2024-04-21 13:32:44,633:INFO:Importing untrained model
2024-04-21 13:32:44,635:INFO:Logistic Regression Imported successfully
2024-04-21 13:32:44,638:INFO:Starting cross validation
2024-04-21 13:32:44,639:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:32:47,004:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-21 13:32:47,014:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-21 13:32:47,035:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:47,041:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:47,044:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:47,048:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:47,107:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-21 13:32:47,140:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-21 13:32:47,150:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:47,155:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:47,173:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:47,177:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:47,682:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-21 13:32:47,705:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:47,708:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:47,716:INFO:Calculating mean and std
2024-04-21 13:32:47,719:INFO:Creating metrics dataframe
2024-04-21 13:32:47,724:INFO:Uploading results into container
2024-04-21 13:32:47,725:INFO:Uploading model into container now
2024-04-21 13:32:47,726:INFO:_master_model_container: 1
2024-04-21 13:32:47,726:INFO:_display_container: 2
2024-04-21 13:32:47,727: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)
2024-04-21 13:32:47,727:INFO:create_model() successfully completed......................................
2024-04-21 13:32:47,915:INFO:SubProcess create_model() end ==================================
2024-04-21 13:32:47,916:INFO:Creating metrics dataframe
2024-04-21 13:32:47,921:INFO:Initializing K Neighbors Classifier
2024-04-21 13:32:47,921:INFO:Total runtime is 0.05505248705546061 minutes
2024-04-21 13:32:47,923:INFO:SubProcess create_model() called ==================================
2024-04-21 13:32:47,923:INFO:Initializing create_model()
2024-04-21 13:32:47,923:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=knn, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:32:47,923:INFO:Checking exceptions
2024-04-21 13:32:47,923:INFO:Importing libraries
2024-04-21 13:32:47,923:INFO:Copying training dataset
2024-04-21 13:32:47,934:INFO:Defining folds
2024-04-21 13:32:47,935:INFO:Declaring metric variables
2024-04-21 13:32:47,937:INFO:Importing untrained model
2024-04-21 13:32:47,939:INFO:K Neighbors Classifier Imported successfully
2024-04-21 13:32:47,942:INFO:Starting cross validation
2024-04-21 13:32:47,943:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:32:48,082:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:48,084:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:48,089:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:48,089:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:48,095:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:32:48,099:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:48,099:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:32:48,100:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:32:48,102:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:32:48,118:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:32:48,124:INFO:Calculating mean and std
2024-04-21 13:32:48,124:INFO:Creating metrics dataframe
2024-04-21 13:32:48,126:INFO:Uploading results into container
2024-04-21 13:32:48,126:INFO:Uploading model into container now
2024-04-21 13:32:48,127:INFO:_master_model_container: 2
2024-04-21 13:32:48,127:INFO:_display_container: 2
2024-04-21 13:32:48,127:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform')
2024-04-21 13:32:48,127:INFO:create_model() successfully completed......................................
2024-04-21 13:32:48,229:WARNING:create_model() for KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform') raised an exception or returned all 0.0, trying without fit_kwargs:
2024-04-21 13:32:48,229:WARNING:Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py", line 797, in compare_models
np.sum(
AssertionError
2024-04-21 13:32:48,230:INFO:Initializing create_model()
2024-04-21 13:32:48,230:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=knn, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:32:48,230:INFO:Checking exceptions
2024-04-21 13:32:48,230:INFO:Importing libraries
2024-04-21 13:32:48,230:INFO:Copying training dataset
2024-04-21 13:32:48,240:INFO:Defining folds
2024-04-21 13:32:48,240:INFO:Declaring metric variables
2024-04-21 13:32:48,242:INFO:Importing untrained model
2024-04-21 13:32:48,244:INFO:K Neighbors Classifier Imported successfully
2024-04-21 13:32:48,248:INFO:Starting cross validation
2024-04-21 13:32:48,249:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:32:48,346:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:48,369:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:32:48,369:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:48,385:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:48,390:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:48,390:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:32:48,406:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:32:48,414:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:32:48,454:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:48,464:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:32:48,470:INFO:Calculating mean and std
2024-04-21 13:32:48,471:INFO:Creating metrics dataframe
2024-04-21 13:32:48,473:INFO:Uploading results into container
2024-04-21 13:32:48,473:INFO:Uploading model into container now
2024-04-21 13:32:48,474:INFO:_master_model_container: 3
2024-04-21 13:32:48,474:INFO:_display_container: 2
2024-04-21 13:32:48,474:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform')
2024-04-21 13:32:48,474:INFO:create_model() successfully completed......................................
2024-04-21 13:32:48,570:ERROR:create_model() for KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform') raised an exception or returned all 0.0:
2024-04-21 13:32:48,570:ERROR:Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py", line 797, in compare_models
np.sum(
AssertionError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py", line 818, in compare_models
np.sum(
AssertionError
2024-04-21 13:32:48,570:INFO:Initializing Naive Bayes
2024-04-21 13:32:48,570:INFO:Total runtime is 0.0658716360727946 minutes
2024-04-21 13:32:48,572:INFO:SubProcess create_model() called ==================================
2024-04-21 13:32:48,572:INFO:Initializing create_model()
2024-04-21 13:32:48,572:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=nb, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:32:48,572:INFO:Checking exceptions
2024-04-21 13:32:48,573:INFO:Importing libraries
2024-04-21 13:32:48,573:INFO:Copying training dataset
2024-04-21 13:32:48,583:INFO:Defining folds
2024-04-21 13:32:48,583:INFO:Declaring metric variables
2024-04-21 13:32:48,585:INFO:Importing untrained model
2024-04-21 13:32:48,587:INFO:Naive Bayes Imported successfully
2024-04-21 13:32:48,590:INFO:Starting cross validation
2024-04-21 13:32:48,591:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:32:48,687:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:48,694:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:48,700:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:48,704:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:48,708:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:48,726:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:48,733:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:49,148:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:49,159:INFO:Calculating mean and std
2024-04-21 13:32:49,159:INFO:Creating metrics dataframe
2024-04-21 13:32:49,161:INFO:Uploading results into container
2024-04-21 13:32:49,161:INFO:Uploading model into container now
2024-04-21 13:32:49,161:INFO:_master_model_container: 4
2024-04-21 13:32:49,162:INFO:_display_container: 2
2024-04-21 13:32:49,162:INFO:GaussianNB(priors=None, var_smoothing=1e-09)
2024-04-21 13:32:49,162:INFO:create_model() successfully completed......................................
2024-04-21 13:32:49,283:INFO:SubProcess create_model() end ==================================
2024-04-21 13:32:49,283:INFO:Creating metrics dataframe
2024-04-21 13:32:49,288:INFO:Initializing Decision Tree Classifier
2024-04-21 13:32:49,288:INFO:Total runtime is 0.07784410317738852 minutes
2024-04-21 13:32:49,290:INFO:SubProcess create_model() called ==================================
2024-04-21 13:32:49,291:INFO:Initializing create_model()
2024-04-21 13:32:49,291:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=dt, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:32:49,291:INFO:Checking exceptions
2024-04-21 13:32:49,291:INFO:Importing libraries
2024-04-21 13:32:49,291:INFO:Copying training dataset
2024-04-21 13:32:49,305:INFO:Defining folds
2024-04-21 13:32:49,306:INFO:Declaring metric variables
2024-04-21 13:32:49,308:INFO:Importing untrained model
2024-04-21 13:32:49,310:INFO:Decision Tree Classifier Imported successfully
2024-04-21 13:32:49,313:INFO:Starting cross validation
2024-04-21 13:32:49,314:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:32:49,574:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:49,650:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:49,677:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:49,682:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:49,687:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:49,699:INFO:Calculating mean and std
2024-04-21 13:32:49,700:INFO:Creating metrics dataframe
2024-04-21 13:32:49,702:INFO:Uploading results into container
2024-04-21 13:32:49,703:INFO:Uploading model into container now
2024-04-21 13:32:49,703:INFO:_master_model_container: 5
2024-04-21 13:32:49,703:INFO:_display_container: 2
2024-04-21 13:32:49,704: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')
2024-04-21 13:32:49,704:INFO:create_model() successfully completed......................................
2024-04-21 13:32:49,798:INFO:SubProcess create_model() end ==================================
2024-04-21 13:32:49,798:INFO:Creating metrics dataframe
2024-04-21 13:32:49,802:INFO:Initializing SVM - Linear Kernel
2024-04-21 13:32:49,802:INFO:Total runtime is 0.08641485373179118 minutes
2024-04-21 13:32:49,805:INFO:SubProcess create_model() called ==================================
2024-04-21 13:32:49,805:INFO:Initializing create_model()
2024-04-21 13:32:49,805:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=svm, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:32:49,805:INFO:Checking exceptions
2024-04-21 13:32:49,805:INFO:Importing libraries
2024-04-21 13:32:49,805:INFO:Copying training dataset
2024-04-21 13:32:49,816:INFO:Defining folds
2024-04-21 13:32:49,816:INFO:Declaring metric variables
2024-04-21 13:32:49,818:INFO:Importing untrained model
2024-04-21 13:32:49,820:INFO:SVM - Linear Kernel Imported successfully
2024-04-21 13:32:49,824:INFO:Starting cross validation
2024-04-21 13:32:49,825:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:32:50,606:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:32:50,611:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:50,635:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:32:50,640:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:50,641:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:32:50,646:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:50,651:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:32:50,656:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:50,708:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:32:50,712:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:50,720:INFO:Calculating mean and std
2024-04-21 13:32:50,721:INFO:Creating metrics dataframe
2024-04-21 13:32:50,723:INFO:Uploading results into container
2024-04-21 13:32:50,724:INFO:Uploading model into container now
2024-04-21 13:32:50,724:INFO:_master_model_container: 6
2024-04-21 13:32:50,725:INFO:_display_container: 2
2024-04-21 13:32:50,725: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)
2024-04-21 13:32:50,725:INFO:create_model() successfully completed......................................
2024-04-21 13:32:50,869:INFO:SubProcess create_model() end ==================================
2024-04-21 13:32:50,869:INFO:Creating metrics dataframe
2024-04-21 13:32:50,875:INFO:Initializing Ridge Classifier
2024-04-21 13:32:50,875:INFO:Total runtime is 0.10428828795750936 minutes
2024-04-21 13:32:50,877:INFO:SubProcess create_model() called ==================================
2024-04-21 13:32:50,877:INFO:Initializing create_model()
2024-04-21 13:32:50,877:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=ridge, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:32:50,877:INFO:Checking exceptions
2024-04-21 13:32:50,877:INFO:Importing libraries
2024-04-21 13:32:50,878:INFO:Copying training dataset
2024-04-21 13:32:50,888:INFO:Defining folds
2024-04-21 13:32:50,888:INFO:Declaring metric variables
2024-04-21 13:32:50,890:INFO:Importing untrained model
2024-04-21 13:32:50,892:INFO:Ridge Classifier Imported successfully
2024-04-21 13:32:50,895:INFO:Starting cross validation
2024-04-21 13:32:50,896:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:32:51,012:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:32:51,015:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:32:51,022:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:32:51,025:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:51,058:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:32:51,059:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:32:51,062:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:51,069:INFO:Calculating mean and std
2024-04-21 13:32:51,071:INFO:Creating metrics dataframe
2024-04-21 13:32:51,073:INFO:Uploading results into container
2024-04-21 13:32:51,074:INFO:Uploading model into container now
2024-04-21 13:32:51,074:INFO:_master_model_container: 7
2024-04-21 13:32:51,074:INFO:_display_container: 2
2024-04-21 13:32:51,075: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)
2024-04-21 13:32:51,075:INFO:create_model() successfully completed......................................
2024-04-21 13:32:51,181:INFO:SubProcess create_model() end ==================================
2024-04-21 13:32:51,181:INFO:Creating metrics dataframe
2024-04-21 13:32:51,187:INFO:Initializing Random Forest Classifier
2024-04-21 13:32:51,187:INFO:Total runtime is 0.10948573748270671 minutes
2024-04-21 13:32:51,189:INFO:SubProcess create_model() called ==================================
2024-04-21 13:32:51,189:INFO:Initializing create_model()
2024-04-21 13:32:51,189:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=rf, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:32:51,189:INFO:Checking exceptions
2024-04-21 13:32:51,190:INFO:Importing libraries
2024-04-21 13:32:51,190:INFO:Copying training dataset
2024-04-21 13:32:51,202:INFO:Defining folds
2024-04-21 13:32:51,202:INFO:Declaring metric variables
2024-04-21 13:32:51,204:INFO:Importing untrained model
2024-04-21 13:32:51,207:INFO:Random Forest Classifier Imported successfully
2024-04-21 13:32:51,211:INFO:Starting cross validation
2024-04-21 13:32:51,212:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:32:54,280:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:54,323:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:54,374:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:54,459:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:54,472:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:54,489:INFO:Calculating mean and std
2024-04-21 13:32:54,493:INFO:Creating metrics dataframe
2024-04-21 13:32:54,501:INFO:Uploading results into container
2024-04-21 13:32:54,501:INFO:Uploading model into container now
2024-04-21 13:32:54,502:INFO:_master_model_container: 8
2024-04-21 13:32:54,502:INFO:_display_container: 2
2024-04-21 13:32:54,504: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)
2024-04-21 13:32:54,506:INFO:create_model() successfully completed......................................
2024-04-21 13:32:54,705:INFO:SubProcess create_model() end ==================================
2024-04-21 13:32:54,705:INFO:Creating metrics dataframe
2024-04-21 13:32:54,718:INFO:Initializing Quadratic Discriminant Analysis
2024-04-21 13:32:54,719:INFO:Total runtime is 0.16834913889567057 minutes
2024-04-21 13:32:54,725:INFO:SubProcess create_model() called ==================================
2024-04-21 13:32:54,725:INFO:Initializing create_model()
2024-04-21 13:32:54,725:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=qda, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:32:54,725:INFO:Checking exceptions
2024-04-21 13:32:54,725:INFO:Importing libraries
2024-04-21 13:32:54,726:INFO:Copying training dataset
2024-04-21 13:32:54,743:INFO:Defining folds
2024-04-21 13:32:54,744:INFO:Declaring metric variables
2024-04-21 13:32:54,748:INFO:Importing untrained model
2024-04-21 13:32:54,751:INFO:Quadratic Discriminant Analysis Imported successfully
2024-04-21 13:32:54,757:INFO:Starting cross validation
2024-04-21 13:32:54,758:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:32:54,928:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-21 13:32:54,928:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-21 13:32:54,930:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-21 13:32:54,931:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-21 13:32:54,931:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-21 13:32:54,976:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:54,976:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:54,980:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:54,981:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:54,981:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:54,982:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:54,983:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:54,987:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:32:54,998:INFO:Calculating mean and std
2024-04-21 13:32:55,000:INFO:Creating metrics dataframe
2024-04-21 13:32:55,002:INFO:Uploading results into container
2024-04-21 13:32:55,002:INFO:Uploading model into container now
2024-04-21 13:32:55,002:INFO:_master_model_container: 9
2024-04-21 13:32:55,003:INFO:_display_container: 2
2024-04-21 13:32:55,003:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,
store_covariance=False, tol=0.0001)
2024-04-21 13:32:55,003:INFO:create_model() successfully completed......................................
2024-04-21 13:32:55,138:INFO:SubProcess create_model() end ==================================
2024-04-21 13:32:55,138:INFO:Creating metrics dataframe
2024-04-21 13:32:55,145:INFO:Initializing Ada Boost Classifier
2024-04-21 13:32:55,145:INFO:Total runtime is 0.17545640071233115 minutes
2024-04-21 13:32:55,148:INFO:SubProcess create_model() called ==================================
2024-04-21 13:32:55,148:INFO:Initializing create_model()
2024-04-21 13:32:55,148:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=ada, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:32:55,148:INFO:Checking exceptions
2024-04-21 13:32:55,148:INFO:Importing libraries
2024-04-21 13:32:55,148:INFO:Copying training dataset
2024-04-21 13:32:55,160:INFO:Defining folds
2024-04-21 13:32:55,161:INFO:Declaring metric variables
2024-04-21 13:32:55,163:INFO:Importing untrained model
2024-04-21 13:32:55,165:INFO:Ada Boost Classifier Imported successfully
2024-04-21 13:32:55,169:INFO:Starting cross validation
2024-04-21 13:32:55,170:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:32:55,244:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-21 13:32:55,254:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-21 13:32:55,257:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-21 13:32:55,264:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-21 13:32:55,289:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-21 13:32:56,514:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:56,555:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:56,573:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:56,594:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:56,602:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:32:56,625:INFO:Calculating mean and std
2024-04-21 13:32:56,629:INFO:Creating metrics dataframe
2024-04-21 13:32:56,636:INFO:Uploading results into container
2024-04-21 13:32:56,637:INFO:Uploading model into container now
2024-04-21 13:32:56,638:INFO:_master_model_container: 10
2024-04-21 13:32:56,638:INFO:_display_container: 2
2024-04-21 13:32:56,639:INFO:AdaBoostClassifier(algorithm='SAMME.R', estimator=None, learning_rate=1.0,
n_estimators=50, random_state=123)
2024-04-21 13:32:56,639:INFO:create_model() successfully completed......................................
2024-04-21 13:32:56,840:INFO:SubProcess create_model() end ==================================
2024-04-21 13:32:56,840:INFO:Creating metrics dataframe
2024-04-21 13:32:56,847:INFO:Initializing Gradient Boosting Classifier
2024-04-21 13:32:56,847:INFO:Total runtime is 0.20383105278015137 minutes
2024-04-21 13:32:56,850:INFO:SubProcess create_model() called ==================================
2024-04-21 13:32:56,850:INFO:Initializing create_model()
2024-04-21 13:32:56,850:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=gbc, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:32:56,851:INFO:Checking exceptions
2024-04-21 13:32:56,851:INFO:Importing libraries
2024-04-21 13:32:56,851:INFO:Copying training dataset
2024-04-21 13:32:56,864:INFO:Defining folds
2024-04-21 13:32:56,864:INFO:Declaring metric variables
2024-04-21 13:32:56,867:INFO:Importing untrained model
2024-04-21 13:32:56,869:INFO:Gradient Boosting Classifier Imported successfully
2024-04-21 13:32:56,873:INFO:Starting cross validation
2024-04-21 13:32:56,875:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:33:22,512:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:22,734:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:22,989:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:23,129:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:23,333:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:23,347:INFO:Calculating mean and std
2024-04-21 13:33:23,350:INFO:Creating metrics dataframe
2024-04-21 13:33:23,356:INFO:Uploading results into container
2024-04-21 13:33:23,357:INFO:Uploading model into container now
2024-04-21 13:33:23,358:INFO:_master_model_container: 11
2024-04-21 13:33:23,358:INFO:_display_container: 2
2024-04-21 13:33:23,366: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)
2024-04-21 13:33:23,366:INFO:create_model() successfully completed......................................
2024-04-21 13:33:23,545:INFO:SubProcess create_model() end ==================================
2024-04-21 13:33:23,545:INFO:Creating metrics dataframe
2024-04-21 13:33:23,557:INFO:Initializing Linear Discriminant Analysis
2024-04-21 13:33:23,557:INFO:Total runtime is 0.6489951014518738 minutes
2024-04-21 13:33:23,560:INFO:SubProcess create_model() called ==================================
2024-04-21 13:33:23,560:INFO:Initializing create_model()
2024-04-21 13:33:23,560:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=lda, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:33:23,560:INFO:Checking exceptions
2024-04-21 13:33:23,560:INFO:Importing libraries
2024-04-21 13:33:23,561:INFO:Copying training dataset
2024-04-21 13:33:23,574:INFO:Defining folds
2024-04-21 13:33:23,574:INFO:Declaring metric variables
2024-04-21 13:33:23,576:INFO:Importing untrained model
2024-04-21 13:33:23,579:INFO:Linear Discriminant Analysis Imported successfully
2024-04-21 13:33:23,584:INFO:Starting cross validation
2024-04-21 13:33:23,585:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:33:23,751:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:23,753:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:23,775:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:23,797:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:23,808:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:23,839:INFO:Calculating mean and std
2024-04-21 13:33:23,840:INFO:Creating metrics dataframe
2024-04-21 13:33:23,843:INFO:Uploading results into container
2024-04-21 13:33:23,843:INFO:Uploading model into container now
2024-04-21 13:33:23,844:INFO:_master_model_container: 12
2024-04-21 13:33:23,844:INFO:_display_container: 2
2024-04-21 13:33:23,844:INFO:LinearDiscriminantAnalysis(covariance_estimator=None, n_components=None,
priors=None, shrinkage=None, solver='svd',
store_covariance=False, tol=0.0001)
2024-04-21 13:33:23,844:INFO:create_model() successfully completed......................................
2024-04-21 13:33:23,995:INFO:SubProcess create_model() end ==================================
2024-04-21 13:33:23,995:INFO:Creating metrics dataframe
2024-04-21 13:33:24,003:INFO:Initializing Extra Trees Classifier
2024-04-21 13:33:24,003:INFO:Total runtime is 0.6564273357391357 minutes
2024-04-21 13:33:24,006:INFO:SubProcess create_model() called ==================================
2024-04-21 13:33:24,006:INFO:Initializing create_model()
2024-04-21 13:33:24,006:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=et, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:33:24,006:INFO:Checking exceptions
2024-04-21 13:33:24,007:INFO:Importing libraries
2024-04-21 13:33:24,007:INFO:Copying training dataset
2024-04-21 13:33:24,019:INFO:Defining folds
2024-04-21 13:33:24,019:INFO:Declaring metric variables
2024-04-21 13:33:24,022:INFO:Importing untrained model
2024-04-21 13:33:24,025:INFO:Extra Trees Classifier Imported successfully
2024-04-21 13:33:24,029:INFO:Starting cross validation
2024-04-21 13:33:24,030:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:33:26,142:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:26,239:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:26,242:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:26,356:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:26,375:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:26,397:INFO:Calculating mean and std
2024-04-21 13:33:26,401:INFO:Creating metrics dataframe
2024-04-21 13:33:26,407:INFO:Uploading results into container
2024-04-21 13:33:26,408:INFO:Uploading model into container now
2024-04-21 13:33:26,409:INFO:_master_model_container: 13
2024-04-21 13:33:26,409:INFO:_display_container: 2
2024-04-21 13:33:26,410:INFO:ExtraTreesClassifier(bootstrap=False, 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)
2024-04-21 13:33:26,410:INFO:create_model() successfully completed......................................
2024-04-21 13:33:26,631:INFO:SubProcess create_model() end ==================================
2024-04-21 13:33:26,632:INFO:Creating metrics dataframe
2024-04-21 13:33:26,639:INFO:Initializing Extreme Gradient Boosting
2024-04-21 13:33:26,639:INFO:Total runtime is 0.7003618001937866 minutes
2024-04-21 13:33:26,642:INFO:SubProcess create_model() called ==================================
2024-04-21 13:33:26,642:INFO:Initializing create_model()
2024-04-21 13:33:26,642:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=xgboost, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:33:26,642:INFO:Checking exceptions
2024-04-21 13:33:26,642:INFO:Importing libraries
2024-04-21 13:33:26,642:INFO:Copying training dataset
2024-04-21 13:33:26,655:INFO:Defining folds
2024-04-21 13:33:26,655:INFO:Declaring metric variables
2024-04-21 13:33:26,657:INFO:Importing untrained model
2024-04-21 13:33:26,660:INFO:Extreme Gradient Boosting Imported successfully
2024-04-21 13:33:26,664:INFO:Starting cross validation
2024-04-21 13:33:26,665:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:33:47,287:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:33:48,292:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:34:03,323:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:34:03,508:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:34:03,524:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:34:03,553:INFO:Calculating mean and std
2024-04-21 13:34:03,560:INFO:Creating metrics dataframe
2024-04-21 13:34:03,569:INFO:Uploading results into container
2024-04-21 13:34:03,569:INFO:Uploading model into container now
2024-04-21 13:34:03,571:INFO:_master_model_container: 14
2024-04-21 13:34:03,571:INFO:_display_container: 2
2024-04-21 13:34:03,573:INFO:XGBClassifier(base_score=None, booster='gbtree', callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=100, n_jobs=-1, num_parallel_tree=None,
objective='binary:logistic', predictor=None, ...)
2024-04-21 13:34:03,573:INFO:create_model() successfully completed......................................
2024-04-21 13:34:04,030:INFO:SubProcess create_model() end ==================================
2024-04-21 13:34:04,030:INFO:Creating metrics dataframe
2024-04-21 13:34:04,038:INFO:Initializing Light Gradient Boosting Machine
2024-04-21 13:34:04,038:INFO:Total runtime is 1.3236705660820007 minutes
2024-04-21 13:34:04,040:INFO:SubProcess create_model() called ==================================
2024-04-21 13:34:04,040:INFO:Initializing create_model()
2024-04-21 13:34:04,041:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=lightgbm, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:34:04,041:INFO:Checking exceptions
2024-04-21 13:34:04,041:INFO:Importing libraries
2024-04-21 13:34:04,041:INFO:Copying training dataset
2024-04-21 13:34:04,058:INFO:Defining folds
2024-04-21 13:34:04,061:INFO:Declaring metric variables
2024-04-21 13:34:04,063:INFO:Importing untrained model
2024-04-21 13:34:04,065:INFO:Light Gradient Boosting Machine Imported successfully
2024-04-21 13:34:04,068:INFO:Starting cross validation
2024-04-21 13:34:04,069:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:35:11,205:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:35:11,483:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:35:11,622:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:35:11,946:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:35:12,231:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:35:12,261:INFO:Calculating mean and std
2024-04-21 13:35:12,266:INFO:Creating metrics dataframe
2024-04-21 13:35:12,284:INFO:Uploading results into container
2024-04-21 13:35:12,285:INFO:Uploading model into container now
2024-04-21 13:35:12,289:INFO:_master_model_container: 15
2024-04-21 13:35:12,289:INFO:_display_container: 2
2024-04-21 13:35:12,290:INFO:LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,
importance_type='split', learning_rate=0.1, max_depth=-1,
min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,
random_state=123, reg_alpha=0.0, reg_lambda=0.0, subsample=1.0,
subsample_for_bin=200000, subsample_freq=0)
2024-04-21 13:35:12,291:INFO:create_model() successfully completed......................................
2024-04-21 13:35:12,556:INFO:SubProcess create_model() end ==================================
2024-04-21 13:35:12,556:INFO:Creating metrics dataframe
2024-04-21 13:35:12,566:INFO:Initializing CatBoost Classifier
2024-04-21 13:35:12,566:INFO:Total runtime is 2.465803050994873 minutes
2024-04-21 13:35:12,569:INFO:SubProcess create_model() called ==================================
2024-04-21 13:35:12,569:INFO:Initializing create_model()
2024-04-21 13:35:12,570:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=catboost, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:35:12,570:INFO:Checking exceptions
2024-04-21 13:35:12,570:INFO:Importing libraries
2024-04-21 13:35:12,570:INFO:Copying training dataset
2024-04-21 13:35:12,588:INFO:Defining folds
2024-04-21 13:35:12,589:INFO:Declaring metric variables
2024-04-21 13:35:12,592:INFO:Importing untrained model
2024-04-21 13:35:12,595:INFO:CatBoost Classifier Imported successfully
2024-04-21 13:35:12,599:INFO:Starting cross validation
2024-04-21 13:35:12,600:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:36:34,063:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:34,831:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:35,353:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:36,071:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:36,078:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:36,114:INFO:Calculating mean and std
2024-04-21 13:36:36,123:INFO:Creating metrics dataframe
2024-04-21 13:36:36,143:INFO:Uploading results into container
2024-04-21 13:36:36,148:INFO:Uploading model into container now
2024-04-21 13:36:36,160:INFO:_master_model_container: 16
2024-04-21 13:36:36,161:INFO:_display_container: 2
2024-04-21 13:36:36,162:INFO:<catboost.core.CatBoostClassifier object at 0x28c55da50>
2024-04-21 13:36:36,162:INFO:create_model() successfully completed......................................
2024-04-21 13:36:36,396:INFO:SubProcess create_model() end ==================================
2024-04-21 13:36:36,396:INFO:Creating metrics dataframe
2024-04-21 13:36:36,405:INFO:Initializing Dummy Classifier
2024-04-21 13:36:36,405:INFO:Total runtime is 3.863118318716685 minutes
2024-04-21 13:36:36,407:INFO:SubProcess create_model() called ==================================
2024-04-21 13:36:36,407:INFO:Initializing create_model()
2024-04-21 13:36:36,408:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=dummy, fold=StratifiedKFold(n_splits=5, 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 0x28a676f80>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:36:36,408:INFO:Checking exceptions
2024-04-21 13:36:36,408:INFO:Importing libraries
2024-04-21 13:36:36,408:INFO:Copying training dataset
2024-04-21 13:36:36,421:INFO:Defining folds
2024-04-21 13:36:36,421:INFO:Declaring metric variables
2024-04-21 13:36:36,423:INFO:Importing untrained model
2024-04-21 13:36:36,425:INFO:Dummy Classifier Imported successfully
2024-04-21 13:36:36,429:INFO:Starting cross validation
2024-04-21 13:36:36,431:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:36:36,610:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:36,617:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:36,757:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:36,780:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:36,784:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:36,855:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:36,882:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:36,890:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:36,891:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:36,899:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:36,912:INFO:Calculating mean and std
2024-04-21 13:36:36,921:INFO:Creating metrics dataframe
2024-04-21 13:36:36,927:INFO:Uploading results into container
2024-04-21 13:36:36,928:INFO:Uploading model into container now
2024-04-21 13:36:36,929:INFO:_master_model_container: 17
2024-04-21 13:36:36,930:INFO:_display_container: 2
2024-04-21 13:36:36,930:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:36:36,930:INFO:create_model() successfully completed......................................
2024-04-21 13:36:37,071:INFO:SubProcess create_model() end ==================================
2024-04-21 13:36:37,071:INFO:Creating metrics dataframe
2024-04-21 13:36:37,080:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py:323: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead.
master_display_.apply(
2024-04-21 13:36:37,086:INFO:Initializing create_model()
2024-04-21 13:36:37,086:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:36:37,086:INFO:Checking exceptions
2024-04-21 13:36:37,088:INFO:Importing libraries
2024-04-21 13:36:37,088:INFO:Copying training dataset
2024-04-21 13:36:37,101:INFO:Defining folds
2024-04-21 13:36:37,101:INFO:Declaring metric variables
2024-04-21 13:36:37,101:INFO:Importing untrained model
2024-04-21 13:36:37,101:INFO:Declaring custom model
2024-04-21 13:36:37,102:INFO:Dummy Classifier Imported successfully
2024-04-21 13:36:37,103:INFO:Cross validation set to False
2024-04-21 13:36:37,103:INFO:Fitting Model
2024-04-21 13:36:37,142:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:36:37,142:INFO:create_model() successfully completed......................................
2024-04-21 13:36:37,319:INFO:Creating Dashboard logs
2024-04-21 13:36:37,325:INFO:Model: Dummy Classifier
2024-04-21 13:36:37,345:INFO:Logged params: {'constant': None, 'random_state': 123, 'strategy': 'prior'}
2024-04-21 13:36:37,381:INFO:Initializing predict_model()
2024-04-21 13:36:37,381:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x13020fd90>)
2024-04-21 13:36:37,381:INFO:Checking exceptions
2024-04-21 13:36:37,381:INFO:Preloading libraries
2024-04-21 13:36:37,471:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py:585: UserWarning: Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 580, in _calculate_metric
calculated_metric = score_func(y_test, target, sample_weight=weights, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 583, in _calculate_metric
calculated_metric = score_func(y_test, target, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
warnings.warn(traceback.format_exc())
2024-04-21 13:36:37,476:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:37,699:INFO:Creating Dashboard logs
2024-04-21 13:36:37,702:INFO:Model: Logistic Regression
2024-04-21 13:36:37,712:INFO:Logged params: {'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}
2024-04-21 13:36:37,845:INFO:Creating Dashboard logs
2024-04-21 13:36:37,847:INFO:Model: Ridge Classifier
2024-04-21 13:36:37,857:INFO:Logged params: {'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}
2024-04-21 13:36:38,002:INFO:Creating Dashboard logs
2024-04-21 13:36:38,005:INFO:Model: Linear Discriminant Analysis
2024-04-21 13:36:38,015:INFO:Logged params: {'covariance_estimator': None, 'n_components': None, 'priors': None, 'shrinkage': None, 'solver': 'svd', 'store_covariance': False, 'tol': 0.0001}
2024-04-21 13:36:38,155:INFO:Creating Dashboard logs
2024-04-21 13:36:38,158:INFO:Model: Ada Boost Classifier
2024-04-21 13:36:38,168:INFO:Logged params: {'algorithm': 'SAMME.R', 'estimator': None, 'learning_rate': 1.0, 'n_estimators': 50, 'random_state': 123}
2024-04-21 13:36:38,292:INFO:Creating Dashboard logs
2024-04-21 13:36:38,294:INFO:Model: Gradient Boosting Classifier
2024-04-21 13:36:38,304:INFO:Logged params: {'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}
2024-04-21 13:36:38,436:INFO:Creating Dashboard logs
2024-04-21 13:36:38,439:INFO:Model: Extreme Gradient Boosting
2024-04-21 13:36:38,448:INFO:Logged params: {'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': 'gbtree', 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 100, 'n_jobs': -1, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': 'auto', 'validate_parameters': None, 'verbosity': 0}
2024-04-21 13:36:38,583:INFO:Creating Dashboard logs
2024-04-21 13:36:38,585:INFO:Model: SVM - Linear Kernel
2024-04-21 13:36:38,597:INFO:Logged params: {'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}
2024-04-21 13:36:38,724:INFO:Creating Dashboard logs
2024-04-21 13:36:38,727:INFO:Model: Random Forest Classifier
2024-04-21 13:36:38,736:INFO:Logged params: {'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}
2024-04-21 13:36:38,867:INFO:Creating Dashboard logs
2024-04-21 13:36:38,869:INFO:Model: CatBoost Classifier
2024-04-21 13:36:38,878:WARNING:Couldn't get params for model. Exception:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/loggers/dashboard_logger.py", line 78, in log_model
params = params.get_all_params()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/catboost/core.py", line 3413, in get_all_params
raise CatBoostError("There is no trained model to use get_all_params(). Use fit() to train model. Then use this method.")
_catboost.CatBoostError: There is no trained model to use get_all_params(). Use fit() to train model. Then use this method.
2024-04-21 13:36:38,879:INFO:Logged params: {}
2024-04-21 13:36:39,025:INFO:Creating Dashboard logs
2024-04-21 13:36:39,028:INFO:Model: Extra Trees Classifier
2024-04-21 13:36:39,039:INFO:Logged params: {'bootstrap': False, '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}
2024-04-21 13:36:39,190:INFO:Creating Dashboard logs
2024-04-21 13:36:39,194:INFO:Model: Light Gradient Boosting Machine
2024-04-21 13:36:39,211:INFO:Logged params: {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 31, 'objective': None, 'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0}
2024-04-21 13:36:39,418:INFO:Creating Dashboard logs
2024-04-21 13:36:39,423:INFO:Model: Decision Tree Classifier
2024-04-21 13:36:39,439:INFO:Logged params: {'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'}
2024-04-21 13:36:39,608:INFO:Creating Dashboard logs
2024-04-21 13:36:39,611:INFO:Model: Naive Bayes
2024-04-21 13:36:39,626:INFO:Logged params: {'priors': None, 'var_smoothing': 1e-09}
2024-04-21 13:36:39,794:INFO:Creating Dashboard logs
2024-04-21 13:36:39,798:INFO:Model: Quadratic Discriminant Analysis
2024-04-21 13:36:39,811:INFO:Logged params: {'priors': None, 'reg_param': 0.0, 'store_covariance': False, 'tol': 0.0001}
2024-04-21 13:36:39,988:INFO:_master_model_container: 17
2024-04-21 13:36:39,988:INFO:_display_container: 2
2024-04-21 13:36:39,988:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:36:39,989:INFO:compare_models() successfully completed......................................
2024-04-21 13:36:39,989:INFO:Initializing tune_model()
2024-04-21 13:36:39,989:INFO:tune_model(estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fold=5, round=4, n_iter=10, custom_grid=None, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}, self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>)
2024-04-21 13:36:39,989:INFO:Checking exceptions
2024-04-21 13:36:40,005:INFO:Copying training dataset
2024-04-21 13:36:40,015:INFO:Checking base model
2024-04-21 13:36:40,015:INFO:Base model : Dummy Classifier
2024-04-21 13:36:40,018:INFO:Declaring metric variables
2024-04-21 13:36:40,020:INFO:Defining Hyperparameters
2024-04-21 13:36:40,020:INFO:10 is bigger than total combinations 4, setting search algorithm to grid
2024-04-21 13:36:40,125:INFO:Tuning with n_jobs=-1
2024-04-21 13:36:40,125:INFO:Initializing GridSearchCV
2024-04-21 13:36:40,843:INFO:best_params: {'actual_estimator__strategy': 'most_frequent'}
2024-04-21 13:36:40,843:INFO:Hyperparameter search completed
2024-04-21 13:36:40,843:INFO:SubProcess create_model() called ==================================
2024-04-21 13:36:40,843:INFO:Initializing create_model()
2024-04-21 13:36:40,843:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x28a53c2b0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={'strategy': 'most_frequent'})
2024-04-21 13:36:40,843:INFO:Checking exceptions
2024-04-21 13:36:40,844:INFO:Importing libraries
2024-04-21 13:36:40,844:INFO:Copying training dataset
2024-04-21 13:36:40,862:INFO:Defining folds
2024-04-21 13:36:40,862:INFO:Declaring metric variables
2024-04-21 13:36:40,866:INFO:Importing untrained model
2024-04-21 13:36:40,866:INFO:Declaring custom model
2024-04-21 13:36:40,870:INFO:Dummy Classifier Imported successfully
2024-04-21 13:36:40,876:INFO:Starting cross validation
2024-04-21 13:36:40,877:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:36:40,993:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:40,993:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:40,999:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:41,000:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:41,002:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:41,003:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:41,007:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:41,012:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:41,017:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:41,024:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:41,035:INFO:Calculating mean and std
2024-04-21 13:36:41,037:INFO:Creating metrics dataframe
2024-04-21 13:36:41,045:INFO:Finalizing model
2024-04-21 13:36:41,132:INFO:Uploading results into container
2024-04-21 13:36:41,133:INFO:Uploading model into container now
2024-04-21 13:36:41,133:INFO:_master_model_container: 18
2024-04-21 13:36:41,134:INFO:_display_container: 3
2024-04-21 13:36:41,134:INFO:DummyClassifier(constant=None, random_state=123, strategy='most_frequent')
2024-04-21 13:36:41,134:INFO:create_model() successfully completed......................................
2024-04-21 13:36:41,295:INFO:SubProcess create_model() end ==================================
2024-04-21 13:36:41,295:INFO:choose_better activated
2024-04-21 13:36:41,298:INFO:SubProcess create_model() called ==================================
2024-04-21 13:36:41,299:INFO:Initializing create_model()
2024-04-21 13:36:41,299:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:36:41,299:INFO:Checking exceptions
2024-04-21 13:36:41,300:INFO:Importing libraries
2024-04-21 13:36:41,300:INFO:Copying training dataset
2024-04-21 13:36:41,311:INFO:Defining folds
2024-04-21 13:36:41,311:INFO:Declaring metric variables
2024-04-21 13:36:41,312:INFO:Importing untrained model
2024-04-21 13:36:41,312:INFO:Declaring custom model
2024-04-21 13:36:41,312:INFO:Dummy Classifier Imported successfully
2024-04-21 13:36:41,312:INFO:Starting cross validation
2024-04-21 13:36:41,313:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:36:41,390:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:41,395:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:41,407:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:41,413:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:41,426:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:41,443:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:41,466:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:41,472:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:41,495:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:36:41,501:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:41,508:INFO:Calculating mean and std
2024-04-21 13:36:41,509:INFO:Creating metrics dataframe
2024-04-21 13:36:41,511:INFO:Finalizing model
2024-04-21 13:36:41,558:INFO:Uploading results into container
2024-04-21 13:36:41,558:INFO:Uploading model into container now
2024-04-21 13:36:41,559:INFO:_master_model_container: 19
2024-04-21 13:36:41,559:INFO:_display_container: 4
2024-04-21 13:36:41,559:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:36:41,559:INFO:create_model() successfully completed......................................
2024-04-21 13:36:41,664:INFO:SubProcess create_model() end ==================================
2024-04-21 13:36:41,664:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior') result for Accuracy is 0.1979
2024-04-21 13:36:41,664:INFO:DummyClassifier(constant=None, random_state=123, strategy='most_frequent') result for Accuracy is 0.1979
2024-04-21 13:36:41,664:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior') is best model
2024-04-21 13:36:41,664:INFO:choose_better completed
2024-04-21 13:36:41,664:INFO:Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).
2024-04-21 13:36:41,664:INFO:Creating Dashboard logs
2024-04-21 13:36:41,667:INFO:Model: Dummy Classifier
2024-04-21 13:36:41,679:INFO:Logged params: {'constant': None, 'random_state': 123, 'strategy': 'prior'}
2024-04-21 13:36:41,702:INFO:Initializing predict_model()
2024-04-21 13:36:41,702:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x13020de10>)
2024-04-21 13:36:41,702:INFO:Checking exceptions
2024-04-21 13:36:41,702:INFO:Preloading libraries
2024-04-21 13:36:41,782:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py:585: UserWarning: Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 580, in _calculate_metric
calculated_metric = score_func(y_test, target, sample_weight=weights, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 583, in _calculate_metric
calculated_metric = score_func(y_test, target, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
warnings.warn(traceback.format_exc())
2024-04-21 13:36:41,786:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:42,007:INFO:_master_model_container: 19
2024-04-21 13:36:42,007:INFO:_display_container: 3
2024-04-21 13:36:42,007:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:36:42,007:INFO:tune_model() successfully completed......................................
2024-04-21 13:36:42,098:INFO:Initializing finalize_model()
2024-04-21 13:36:42,098:INFO:finalize_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fit_kwargs=None, groups=None, model_only=False, experiment_custom_tags=None)
2024-04-21 13:36:42,098:INFO:Finalizing DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:36:42,104:INFO:Initializing create_model()
2024-04-21 13:36:42,104:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fold=None, round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=False, metrics=None, display=None, model_only=False, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:36:42,104:INFO:Checking exceptions
2024-04-21 13:36:42,105:INFO:Importing libraries
2024-04-21 13:36:42,105:INFO:Copying training dataset
2024-04-21 13:36:42,105:INFO:Defining folds
2024-04-21 13:36:42,105:INFO:Declaring metric variables
2024-04-21 13:36:42,105:INFO:Importing untrained model
2024-04-21 13:36:42,105:INFO:Declaring custom model
2024-04-21 13:36:42,106:INFO:Dummy Classifier Imported successfully
2024-04-21 13:36:42,107:INFO:Cross validation set to False
2024-04-21 13:36:42,107:INFO:Fitting Model
2024-04-21 13:36:42,149:INFO:Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Age^2 * BMI^2'],
transformer=SimpleImputer(add_indicator=Fal...
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
DummyClassifier(constant=None, random_state=123,
strategy='prior'))],
verbose=False)
2024-04-21 13:36:42,149:INFO:create_model() successfully completed......................................
2024-04-21 13:36:42,239:INFO:Creating Dashboard logs
2024-04-21 13:36:42,239:INFO:Model: Dummy Classifier
2024-04-21 13:36:42,249:INFO:Logged params: {'constant': None, 'random_state': 123, 'strategy': 'prior'}
2024-04-21 13:36:42,361:INFO:_master_model_container: 19
2024-04-21 13:36:42,362:INFO:_display_container: 3
2024-04-21 13:36:42,365:INFO:Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Age^2 * BMI^2'],
transformer=SimpleImputer(add_indicator=Fal...
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
DummyClassifier(constant=None, random_state=123,
strategy='prior'))],
verbose=False)
2024-04-21 13:36:42,366:INFO:finalize_model() successfully completed......................................
2024-04-21 13:36:42,455:INFO:Initializing predict_model()
2024-04-21 13:36:42,455:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Age^2 * BMI^2'],
transformer=SimpleImputer(add_indicator=Fal...
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
DummyClassifier(constant=None, random_state=123,
strategy='prior'))],
verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x28bbcfd00>)
2024-04-21 13:36:42,455:INFO:Checking exceptions
2024-04-21 13:36:42,455:INFO:Preloading libraries
2024-04-21 13:36:42,456:INFO:Set up data.
2024-04-21 13:36:42,463:INFO:Set up index.
2024-04-21 13:36:42,477:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py:585: UserWarning: Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 580, in _calculate_metric
calculated_metric = score_func(y_test, target, sample_weight=weights, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 583, in _calculate_metric
calculated_metric = score_func(y_test, target, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
warnings.warn(traceback.format_exc())
2024-04-21 13:36:42,481:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:36:42,580:INFO:Initializing predict_model()
2024-04-21 13:36:42,580:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x173f0f670>, estimator=Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Age^2 * BMI^2'],
transformer=SimpleImputer(add_indicator=Fal...
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
DummyClassifier(constant=None, random_state=123,
strategy='prior'))],
verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x28bbcfd00>)
2024-04-21 13:36:42,580:INFO:Checking exceptions
2024-04-21 13:36:42,580:INFO:Preloading libraries
2024-04-21 13:36:42,582:INFO:Set up data.
2024-04-21 13:36:42,591:INFO:Set up index.
2024-04-21 13:36:42,609:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py:585: UserWarning: Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 580, in _calculate_metric
calculated_metric = score_func(y_test, target, sample_weight=weights, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 583, in _calculate_metric
calculated_metric = score_func(y_test, target, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
warnings.warn(traceback.format_exc())
2024-04-21 13:36:42,615:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:18,384:INFO:PyCaret ClassificationExperiment
2024-04-21 13:37:18,385:INFO:Logging name: xyz
2024-04-21 13:37:18,385:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2024-04-21 13:37:18,385:INFO:version 3.3.0
2024-04-21 13:37:18,385:INFO:Initializing setup()
2024-04-21 13:37:18,385:INFO:self.USI: cc8b
2024-04-21 13:37:18,385:INFO:self._variable_keys: {'target_param', 'exp_id', 'data', 'idx', 'fold_shuffle_param', 'log_plots_param', 'fold_groups_param', 'html_param', 'seed', 'exp_name_log', '_available_plots', 'y_train', 'X_train', '_ml_usecase', 'pipeline', 'USI', 'gpu_param', 'fold_generator', 'y', 'X_test', 'fix_imbalance', 'is_multiclass', 'memory', 'n_jobs_param', 'y_test', 'gpu_n_jobs_param', 'logging_param', 'X'}
2024-04-21 13:37:18,385:INFO:Checking environment
2024-04-21 13:37:18,385:INFO:python_version: 3.10.13
2024-04-21 13:37:18,385:INFO:python_build: ('main', 'Sep 11 2023 08:16:02')
2024-04-21 13:37:18,385:INFO:machine: arm64
2024-04-21 13:37:18,385:INFO:platform: macOS-14.0-arm64-arm-64bit
2024-04-21 13:37:18,385:INFO:Memory: svmem(total=8589934592, available=1234436096, percent=85.6, used=3155918848, free=26591232, active=1217789952, inactive=1205665792, wired=1938128896)
2024-04-21 13:37:18,385:INFO:Physical Core: 8
2024-04-21 13:37:18,385:INFO:Logical Core: 8
2024-04-21 13:37:18,385:INFO:Checking libraries
2024-04-21 13:37:18,385:INFO:System:
2024-04-21 13:37:18,385:INFO: python: 3.10.13 (main, Sep 11 2023, 08:16:02) [Clang 14.0.6 ]
2024-04-21 13:37:18,385:INFO:executable: /Users/arham/anaconda3/envs/DataScience/bin/python
2024-04-21 13:37:18,385:INFO: machine: macOS-14.0-arm64-arm-64bit
2024-04-21 13:37:18,385:INFO:PyCaret required dependencies:
2024-04-21 13:37:18,385:INFO: pip: 23.3
2024-04-21 13:37:18,385:INFO: setuptools: 60.2.0
2024-04-21 13:37:18,385:INFO: pycaret: 3.3.0
2024-04-21 13:37:18,385:INFO: IPython: 8.15.0
2024-04-21 13:37:18,385:INFO: ipywidgets: 8.0.4
2024-04-21 13:37:18,386:INFO: tqdm: 4.65.2
2024-04-21 13:37:18,386:INFO: numpy: 1.25.2
2024-04-21 13:37:18,386:INFO: pandas: 2.2.2
2024-04-21 13:37:18,386:INFO: jinja2: 3.1.2
2024-04-21 13:37:18,386:INFO: scipy: 1.11.4
2024-04-21 13:37:18,386:INFO: joblib: 1.2.0
2024-04-21 13:37:18,386:INFO: sklearn: 1.4.0
2024-04-21 13:37:18,386:INFO: pyod: 1.1.3
2024-04-21 13:37:18,386:INFO: imblearn: 0.12.2
2024-04-21 13:37:18,386:INFO: category_encoders: 2.6.3
2024-04-21 13:37:18,386:INFO: lightgbm: 4.1.0
2024-04-21 13:37:18,386:INFO: numba: 0.58.1
2024-04-21 13:37:18,386:INFO: requests: 2.28.2
2024-04-21 13:37:18,386:INFO: matplotlib: 3.6.2
2024-04-21 13:37:18,386:INFO: scikitplot: 0.3.7
2024-04-21 13:37:18,386:INFO: yellowbrick: 1.5
2024-04-21 13:37:18,386:INFO: plotly: 5.17.0
2024-04-21 13:37:18,386:INFO: plotly-resampler: Not installed
2024-04-21 13:37:18,386:INFO: kaleido: 0.2.1
2024-04-21 13:37:18,386:INFO: schemdraw: 0.15
2024-04-21 13:37:18,386:INFO: statsmodels: 0.13.5
2024-04-21 13:37:18,386:INFO: sktime: 0.26.1
2024-04-21 13:37:18,386:INFO: tbats: 1.1.3
2024-04-21 13:37:18,386:INFO: pmdarima: 2.0.4
2024-04-21 13:37:18,386:INFO: psutil: 5.9.0
2024-04-21 13:37:18,386:INFO: markupsafe: 2.1.1
2024-04-21 13:37:18,386:INFO: pickle5: Not installed
2024-04-21 13:37:18,386:INFO: cloudpickle: 2.2.1
2024-04-21 13:37:18,386:INFO: deprecation: 2.1.0
2024-04-21 13:37:18,386:INFO: xxhash: 3.4.1
2024-04-21 13:37:18,386:INFO: wurlitzer: 3.0.2
2024-04-21 13:37:18,386:INFO:PyCaret optional dependencies:
2024-04-21 13:37:18,386:INFO: shap: 0.44.0
2024-04-21 13:37:18,386:INFO: interpret: Not installed
2024-04-21 13:37:18,386:INFO: umap: Not installed
2024-04-21 13:37:18,386:INFO: ydata_profiling: 0.0.dev0
2024-04-21 13:37:18,386:INFO: explainerdashboard: Not installed
2024-04-21 13:37:18,386:INFO: autoviz: Not installed
2024-04-21 13:37:18,386:INFO: fairlearn: Not installed
2024-04-21 13:37:18,386:INFO: deepchecks: Not installed
2024-04-21 13:37:18,386:INFO: xgboost: 1.7.3
2024-04-21 13:37:18,386:INFO: catboost: 1.1.1
2024-04-21 13:37:18,386:INFO: kmodes: Not installed
2024-04-21 13:37:18,386:INFO: mlxtend: Not installed
2024-04-21 13:37:18,386:INFO: statsforecast: 1.4.0
2024-04-21 13:37:18,386:INFO: tune_sklearn: Not installed
2024-04-21 13:37:18,386:INFO: ray: 2.10.0
2024-04-21 13:37:18,386:INFO: hyperopt: 0.2.7
2024-04-21 13:37:18,387:INFO: optuna: 3.5.0
2024-04-21 13:37:18,387:INFO: skopt: Not installed
2024-04-21 13:37:18,387:INFO: mlflow: 2.10.2
2024-04-21 13:37:18,387:INFO: gradio: 3.48.0
2024-04-21 13:37:18,387:INFO: fastapi: 0.109.2
2024-04-21 13:37:18,387:INFO: uvicorn: 0.27.1
2024-04-21 13:37:18,387:INFO: m2cgen: Not installed
2024-04-21 13:37:18,387:INFO: evidently: Not installed
2024-04-21 13:37:18,387:INFO: fugue: Not installed
2024-04-21 13:37:18,387:INFO: streamlit: 1.27.2
2024-04-21 13:37:18,387:INFO: prophet: Not installed
2024-04-21 13:37:18,387:INFO:None
2024-04-21 13:37:18,387:INFO:Set up data.
2024-04-21 13:37:18,396:INFO:Set up folding strategy.
2024-04-21 13:37:18,396:INFO:Set up train/test split.
2024-04-21 13:37:18,412:INFO:Set up index.
2024-04-21 13:37:18,412:INFO:Assigning column types.
2024-04-21 13:37:18,419:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2024-04-21 13:37:18,450:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-21 13:37:18,451:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:37:18,470:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:37:18,472:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:37:18,504:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-21 13:37:18,504:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:37:18,523:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:37:18,524:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:37:18,525:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2024-04-21 13:37:18,555:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:37:18,574:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:37:18,576:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:37:18,609:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-21 13:37:18,627:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:37:18,629:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:37:18,629:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2024-04-21 13:37:18,680:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:37:18,682:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:37:18,731:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:37:18,733:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:37:18,734:INFO:Preparing preprocessing pipeline...
2024-04-21 13:37:18,736:INFO:Set up simple imputation.
2024-04-21 13:37:18,740:INFO:Set up encoding of categorical features.
2024-04-21 13:37:18,741:INFO:Set up column name cleaning.
2024-04-21 13:37:18,806:INFO:Finished creating preprocessing pipeline.
2024-04-21 13:37:18,810:INFO:Pipeline: Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Ag...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False)
2024-04-21 13:37:18,810:INFO:Creating final display dataframe.
2024-04-21 13:37:18,964:INFO:Setup _display_container: Description Value
0 Session id 123
1 Target NObeyesdad
2 Target type Multiclass
3 Original data shape (10793, 29)
4 Transformed data shape (10793, 33)
5 Transformed train set shape (8634, 33)
6 Transformed test set shape (2159, 33)
7 Numeric features 22
8 Categorical features 1
9 Preprocess True
10 Imputation type simple
11 Numeric imputation mean
12 Categorical imputation mode
13 Maximum one-hot encoding 25
14 Encoding method None
15 Fold Generator StratifiedKFold
16 Fold Number 10
17 CPU Jobs -1
18 Use GPU False
19 Log Experiment MlflowLogger
20 Experiment Name xyz
21 USI cc8b
2024-04-21 13:37:19,018:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:37:19,020:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:37:19,069:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-21 13:37:19,071:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-21 13:37:19,072:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour.
warnings.warn(
2024-04-21 13:37:19,073:INFO:Logging experiment in loggers
2024-04-21 13:37:19,096:INFO:SubProcess save_model() called ==================================
2024-04-21 13:37:19,103:INFO:Initializing save_model()
2024-04-21 13:37:19,103:INFO:save_model(model=Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Ag...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False), model_name=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/tmpclry_hkj/Transformation Pipeline, prep_pipe_=Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Ag...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False), verbose=False, use_case=MLUsecase.CLASSIFICATION, kwargs={})
2024-04-21 13:37:19,103:INFO:Adding model into prep_pipe
2024-04-21 13:37:19,103:WARNING:Only Model saved as it was a pipeline.
2024-04-21 13:37:19,106:INFO:/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/tmpclry_hkj/Transformation Pipeline.pkl saved in current working directory
2024-04-21 13:37:19,110:INFO:Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Ag...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False)
2024-04-21 13:37:19,110:INFO:save_model() successfully completed......................................
2024-04-21 13:37:19,296:INFO:SubProcess save_model() end ==================================
2024-04-21 13:37:19,300:INFO:setup() successfully completed in 0.7s...............
2024-04-21 13:37:19,306:INFO:Initializing compare_models()
2024-04-21 13:37:19,306:INFO:compare_models(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, include=None, fold=5, 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 0x105c300d0>, 'include': None, 'exclude': None, 'fold': 5, '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)
2024-04-21 13:37:19,306:INFO:Checking exceptions
2024-04-21 13:37:19,313:INFO:Preparing display monitor
2024-04-21 13:37:19,329:INFO:Initializing Logistic Regression
2024-04-21 13:37:19,330:INFO:Total runtime is 2.745787302652995e-06 minutes
2024-04-21 13:37:19,332:INFO:SubProcess create_model() called ==================================
2024-04-21 13:37:19,332:INFO:Initializing create_model()
2024-04-21 13:37:19,332:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=lr, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:37:19,333:INFO:Checking exceptions
2024-04-21 13:37:19,333:INFO:Importing libraries
2024-04-21 13:37:19,333:INFO:Copying training dataset
2024-04-21 13:37:19,346:INFO:Defining folds
2024-04-21 13:37:19,346:INFO:Declaring metric variables
2024-04-21 13:37:19,348:INFO:Importing untrained model
2024-04-21 13:37:19,351:INFO:Logistic Regression Imported successfully
2024-04-21 13:37:19,356:INFO:Starting cross validation
2024-04-21 13:37:19,357:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:37:24,112:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-21 13:37:24,195:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:24,195:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-21 13:37:24,203:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:24,288:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-21 13:37:24,303:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:24,309:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:24,375:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:24,386:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:24,391:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-21 13:37:24,441:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:24,444:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-21 13:37:24,448:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:24,543:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:24,549:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:24,562:INFO:Calculating mean and std
2024-04-21 13:37:24,567:INFO:Creating metrics dataframe
2024-04-21 13:37:24,573:INFO:Uploading results into container
2024-04-21 13:37:24,574:INFO:Uploading model into container now
2024-04-21 13:37:24,574:INFO:_master_model_container: 1
2024-04-21 13:37:24,575:INFO:_display_container: 2
2024-04-21 13:37:24,576: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)
2024-04-21 13:37:24,576:INFO:create_model() successfully completed......................................
2024-04-21 13:37:24,794:INFO:SubProcess create_model() end ==================================
2024-04-21 13:37:24,794:INFO:Creating metrics dataframe
2024-04-21 13:37:24,802:INFO:Initializing K Neighbors Classifier
2024-04-21 13:37:24,802:INFO:Total runtime is 0.0912166158358256 minutes
2024-04-21 13:37:24,807:INFO:SubProcess create_model() called ==================================
2024-04-21 13:37:24,808:INFO:Initializing create_model()
2024-04-21 13:37:24,808:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=knn, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:37:24,808:INFO:Checking exceptions
2024-04-21 13:37:24,809:INFO:Importing libraries
2024-04-21 13:37:24,809:INFO:Copying training dataset
2024-04-21 13:37:24,824:INFO:Defining folds
2024-04-21 13:37:24,824:INFO:Declaring metric variables
2024-04-21 13:37:24,826:INFO:Importing untrained model
2024-04-21 13:37:24,829:INFO:K Neighbors Classifier Imported successfully
2024-04-21 13:37:24,833:INFO:Starting cross validation
2024-04-21 13:37:24,834:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:37:25,019:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:25,066:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:37:25,101:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:25,102:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:25,123:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:37:25,127:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:37:25,155:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:25,176:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:37:25,624:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:25,642:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:37:25,648:INFO:Calculating mean and std
2024-04-21 13:37:25,654:INFO:Creating metrics dataframe
2024-04-21 13:37:25,662:INFO:Uploading results into container
2024-04-21 13:37:25,664:INFO:Uploading model into container now
2024-04-21 13:37:25,665:INFO:_master_model_container: 2
2024-04-21 13:37:25,665:INFO:_display_container: 2
2024-04-21 13:37:25,667:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform')
2024-04-21 13:37:25,667:INFO:create_model() successfully completed......................................
2024-04-21 13:37:25,858:WARNING:create_model() for KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform') raised an exception or returned all 0.0, trying without fit_kwargs:
2024-04-21 13:37:25,858:WARNING:Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py", line 797, in compare_models
np.sum(
AssertionError
2024-04-21 13:37:25,859:INFO:Initializing create_model()
2024-04-21 13:37:25,859:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=knn, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:37:25,859:INFO:Checking exceptions
2024-04-21 13:37:25,859:INFO:Importing libraries
2024-04-21 13:37:25,859:INFO:Copying training dataset
2024-04-21 13:37:25,874:INFO:Defining folds
2024-04-21 13:37:25,874:INFO:Declaring metric variables
2024-04-21 13:37:25,877:INFO:Importing untrained model
2024-04-21 13:37:25,880:INFO:K Neighbors Classifier Imported successfully
2024-04-21 13:37:25,886:INFO:Starting cross validation
2024-04-21 13:37:25,887:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:37:26,166:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:26,205:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:37:26,343:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:26,391:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:26,406:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:37:26,431:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:26,462:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:26,507:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:37:26,509:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:37:26,540:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-21 13:37:26,576:INFO:Calculating mean and std
2024-04-21 13:37:26,582:INFO:Creating metrics dataframe
2024-04-21 13:37:26,592:INFO:Uploading results into container
2024-04-21 13:37:26,593:INFO:Uploading model into container now
2024-04-21 13:37:26,594:INFO:_master_model_container: 3
2024-04-21 13:37:26,594:INFO:_display_container: 2
2024-04-21 13:37:26,611:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform')
2024-04-21 13:37:26,611:INFO:create_model() successfully completed......................................
2024-04-21 13:37:27,008:ERROR:create_model() for KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform') raised an exception or returned all 0.0:
2024-04-21 13:37:27,008:ERROR:Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py", line 797, in compare_models
np.sum(
AssertionError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py", line 818, in compare_models
np.sum(
AssertionError
2024-04-21 13:37:27,008:INFO:Initializing Naive Bayes
2024-04-21 13:37:27,008:INFO:Total runtime is 0.1279792825380961 minutes
2024-04-21 13:37:27,012:INFO:SubProcess create_model() called ==================================
2024-04-21 13:37:27,012:INFO:Initializing create_model()
2024-04-21 13:37:27,013:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=nb, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:37:27,013:INFO:Checking exceptions
2024-04-21 13:37:27,013:INFO:Importing libraries
2024-04-21 13:37:27,013:INFO:Copying training dataset
2024-04-21 13:37:27,032:INFO:Defining folds
2024-04-21 13:37:27,032:INFO:Declaring metric variables
2024-04-21 13:37:27,036:INFO:Importing untrained model
2024-04-21 13:37:27,040:INFO:Naive Bayes Imported successfully
2024-04-21 13:37:27,046:INFO:Starting cross validation
2024-04-21 13:37:27,048:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:37:27,438:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:27,444:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:27,448:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:27,458:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:27,459:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:27,459:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:27,490:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:27,495:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:27,507:INFO:Calculating mean and std
2024-04-21 13:37:27,512:INFO:Creating metrics dataframe
2024-04-21 13:37:27,535:INFO:Uploading results into container
2024-04-21 13:37:27,537:INFO:Uploading model into container now
2024-04-21 13:37:27,538:INFO:_master_model_container: 4
2024-04-21 13:37:27,538:INFO:_display_container: 2
2024-04-21 13:37:27,539:INFO:GaussianNB(priors=None, var_smoothing=1e-09)
2024-04-21 13:37:27,540:INFO:create_model() successfully completed......................................
2024-04-21 13:37:27,718:INFO:SubProcess create_model() end ==================================
2024-04-21 13:37:27,718:INFO:Creating metrics dataframe
2024-04-21 13:37:27,723:INFO:Initializing Decision Tree Classifier
2024-04-21 13:37:27,723:INFO:Total runtime is 0.13990181684494016 minutes
2024-04-21 13:37:27,726:INFO:SubProcess create_model() called ==================================
2024-04-21 13:37:27,726:INFO:Initializing create_model()
2024-04-21 13:37:27,726:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=dt, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:37:27,726:INFO:Checking exceptions
2024-04-21 13:37:27,726:INFO:Importing libraries
2024-04-21 13:37:27,726:INFO:Copying training dataset
2024-04-21 13:37:27,739:INFO:Defining folds
2024-04-21 13:37:27,739:INFO:Declaring metric variables
2024-04-21 13:37:27,742:INFO:Importing untrained model
2024-04-21 13:37:27,744:INFO:Decision Tree Classifier Imported successfully
2024-04-21 13:37:27,748:INFO:Starting cross validation
2024-04-21 13:37:27,749:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:37:28,151:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:28,233:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:28,248:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:28,265:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:28,287:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:28,304:INFO:Calculating mean and std
2024-04-21 13:37:28,305:INFO:Creating metrics dataframe
2024-04-21 13:37:28,307:INFO:Uploading results into container
2024-04-21 13:37:28,307:INFO:Uploading model into container now
2024-04-21 13:37:28,308:INFO:_master_model_container: 5
2024-04-21 13:37:28,308:INFO:_display_container: 2
2024-04-21 13:37:28,308: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')
2024-04-21 13:37:28,309:INFO:create_model() successfully completed......................................
2024-04-21 13:37:28,406:INFO:SubProcess create_model() end ==================================
2024-04-21 13:37:28,407:INFO:Creating metrics dataframe
2024-04-21 13:37:28,412:INFO:Initializing SVM - Linear Kernel
2024-04-21 13:37:28,412:INFO:Total runtime is 0.1513752500216166 minutes
2024-04-21 13:37:28,414:INFO:SubProcess create_model() called ==================================
2024-04-21 13:37:28,415:INFO:Initializing create_model()
2024-04-21 13:37:28,415:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=svm, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:37:28,415:INFO:Checking exceptions
2024-04-21 13:37:28,415:INFO:Importing libraries
2024-04-21 13:37:28,415:INFO:Copying training dataset
2024-04-21 13:37:28,427:INFO:Defining folds
2024-04-21 13:37:28,427:INFO:Declaring metric variables
2024-04-21 13:37:28,429:INFO:Importing untrained model
2024-04-21 13:37:28,431:INFO:SVM - Linear Kernel Imported successfully
2024-04-21 13:37:28,435:INFO:Starting cross validation
2024-04-21 13:37:28,437:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:37:29,533:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:37:29,538:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:29,696:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:37:29,697:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:37:29,701:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:29,702:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:29,712:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:37:29,718:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:29,750:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:37:29,755:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:29,762:INFO:Calculating mean and std
2024-04-21 13:37:29,763:INFO:Creating metrics dataframe
2024-04-21 13:37:29,766:INFO:Uploading results into container
2024-04-21 13:37:29,767:INFO:Uploading model into container now
2024-04-21 13:37:29,767:INFO:_master_model_container: 6
2024-04-21 13:37:29,767:INFO:_display_container: 2
2024-04-21 13:37:29,768: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)
2024-04-21 13:37:29,768:INFO:create_model() successfully completed......................................
2024-04-21 13:37:29,874:INFO:SubProcess create_model() end ==================================
2024-04-21 13:37:29,874:INFO:Creating metrics dataframe
2024-04-21 13:37:29,880:INFO:Initializing Ridge Classifier
2024-04-21 13:37:29,880:INFO:Total runtime is 0.1758458336194356 minutes
2024-04-21 13:37:29,882:INFO:SubProcess create_model() called ==================================
2024-04-21 13:37:29,883:INFO:Initializing create_model()
2024-04-21 13:37:29,883:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=ridge, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:37:29,883:INFO:Checking exceptions
2024-04-21 13:37:29,883:INFO:Importing libraries
2024-04-21 13:37:29,883:INFO:Copying training dataset
2024-04-21 13:37:29,895:INFO:Defining folds
2024-04-21 13:37:29,895:INFO:Declaring metric variables
2024-04-21 13:37:29,897:INFO:Importing untrained model
2024-04-21 13:37:29,900:INFO:Ridge Classifier Imported successfully
2024-04-21 13:37:29,904:INFO:Starting cross validation
2024-04-21 13:37:29,905:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:37:29,997:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:37:30,001:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:37:30,005:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:37:30,006:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:30,008:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:37:30,008:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-21 13:37:30,009:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:30,018:INFO:Calculating mean and std
2024-04-21 13:37:30,019:INFO:Creating metrics dataframe
2024-04-21 13:37:30,021:INFO:Uploading results into container
2024-04-21 13:37:30,021:INFO:Uploading model into container now
2024-04-21 13:37:30,021:INFO:_master_model_container: 7
2024-04-21 13:37:30,021:INFO:_display_container: 2
2024-04-21 13:37:30,022: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)
2024-04-21 13:37:30,022:INFO:create_model() successfully completed......................................
2024-04-21 13:37:30,147:INFO:SubProcess create_model() end ==================================
2024-04-21 13:37:30,148:INFO:Creating metrics dataframe
2024-04-21 13:37:30,153:INFO:Initializing Random Forest Classifier
2024-04-21 13:37:30,153:INFO:Total runtime is 0.1804008324940999 minutes
2024-04-21 13:37:30,156:INFO:SubProcess create_model() called ==================================
2024-04-21 13:37:30,156:INFO:Initializing create_model()
2024-04-21 13:37:30,156:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=rf, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:37:30,156:INFO:Checking exceptions
2024-04-21 13:37:30,156:INFO:Importing libraries
2024-04-21 13:37:30,156:INFO:Copying training dataset
2024-04-21 13:37:30,168:INFO:Defining folds
2024-04-21 13:37:30,168:INFO:Declaring metric variables
2024-04-21 13:37:30,171:INFO:Importing untrained model
2024-04-21 13:37:30,173:INFO:Random Forest Classifier Imported successfully
2024-04-21 13:37:30,177:INFO:Starting cross validation
2024-04-21 13:37:30,178:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:37:32,999:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:33,036:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:33,086:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:33,185:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:33,190:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:33,208:INFO:Calculating mean and std
2024-04-21 13:37:33,214:INFO:Creating metrics dataframe
2024-04-21 13:37:33,223:INFO:Uploading results into container
2024-04-21 13:37:33,224:INFO:Uploading model into container now
2024-04-21 13:37:33,225:INFO:_master_model_container: 8
2024-04-21 13:37:33,225:INFO:_display_container: 2
2024-04-21 13:37:33,226: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)
2024-04-21 13:37:33,227:INFO:create_model() successfully completed......................................
2024-04-21 13:37:33,400:INFO:SubProcess create_model() end ==================================
2024-04-21 13:37:33,400:INFO:Creating metrics dataframe
2024-04-21 13:37:33,406:INFO:Initializing Quadratic Discriminant Analysis
2024-04-21 13:37:33,407:INFO:Total runtime is 0.23462005058924354 minutes
2024-04-21 13:37:33,409:INFO:SubProcess create_model() called ==================================
2024-04-21 13:37:33,409:INFO:Initializing create_model()
2024-04-21 13:37:33,409:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=qda, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:37:33,410:INFO:Checking exceptions
2024-04-21 13:37:33,410:INFO:Importing libraries
2024-04-21 13:37:33,410:INFO:Copying training dataset
2024-04-21 13:37:33,423:INFO:Defining folds
2024-04-21 13:37:33,423:INFO:Declaring metric variables
2024-04-21 13:37:33,425:INFO:Importing untrained model
2024-04-21 13:37:33,428:INFO:Quadratic Discriminant Analysis Imported successfully
2024-04-21 13:37:33,432:INFO:Starting cross validation
2024-04-21 13:37:33,434:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:37:33,547:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-21 13:37:33,548:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-21 13:37:33,549:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-21 13:37:33,560:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-21 13:37:33,563:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-21 13:37:33,590:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:33,594:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:33,595:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:33,598:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:33,603:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:33,603:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:33,604:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:33,610:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:37:33,626:INFO:Calculating mean and std
2024-04-21 13:37:33,628:INFO:Creating metrics dataframe
2024-04-21 13:37:33,630:INFO:Uploading results into container
2024-04-21 13:37:33,631:INFO:Uploading model into container now
2024-04-21 13:37:33,631:INFO:_master_model_container: 9
2024-04-21 13:37:33,632:INFO:_display_container: 2
2024-04-21 13:37:33,632:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,
store_covariance=False, tol=0.0001)
2024-04-21 13:37:33,632:INFO:create_model() successfully completed......................................
2024-04-21 13:37:33,799:INFO:SubProcess create_model() end ==================================
2024-04-21 13:37:33,799:INFO:Creating metrics dataframe
2024-04-21 13:37:33,806:INFO:Initializing Ada Boost Classifier
2024-04-21 13:37:33,806:INFO:Total runtime is 0.24128333330154417 minutes
2024-04-21 13:37:33,809:INFO:SubProcess create_model() called ==================================
2024-04-21 13:37:33,809:INFO:Initializing create_model()
2024-04-21 13:37:33,809:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=ada, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:37:33,809:INFO:Checking exceptions
2024-04-21 13:37:33,809:INFO:Importing libraries
2024-04-21 13:37:33,809:INFO:Copying training dataset
2024-04-21 13:37:33,823:INFO:Defining folds
2024-04-21 13:37:33,823:INFO:Declaring metric variables
2024-04-21 13:37:33,825:INFO:Importing untrained model
2024-04-21 13:37:33,827:INFO:Ada Boost Classifier Imported successfully
2024-04-21 13:37:33,831:INFO:Starting cross validation
2024-04-21 13:37:33,832:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:37:33,908:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-21 13:37:33,910:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-21 13:37:33,916:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-21 13:37:33,917:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-21 13:37:33,942:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-21 13:37:34,910:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:34,918:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:34,921:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:34,926:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:34,931:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:34,950:INFO:Calculating mean and std
2024-04-21 13:37:34,952:INFO:Creating metrics dataframe
2024-04-21 13:37:34,956:INFO:Uploading results into container
2024-04-21 13:37:34,957:INFO:Uploading model into container now
2024-04-21 13:37:34,958:INFO:_master_model_container: 10
2024-04-21 13:37:34,958:INFO:_display_container: 2
2024-04-21 13:37:34,958:INFO:AdaBoostClassifier(algorithm='SAMME.R', estimator=None, learning_rate=1.0,
n_estimators=50, random_state=123)
2024-04-21 13:37:34,959:INFO:create_model() successfully completed......................................
2024-04-21 13:37:35,078:INFO:SubProcess create_model() end ==================================
2024-04-21 13:37:35,078:INFO:Creating metrics dataframe
2024-04-21 13:37:35,085:INFO:Initializing Gradient Boosting Classifier
2024-04-21 13:37:35,085:INFO:Total runtime is 0.26259220043818154 minutes
2024-04-21 13:37:35,087:INFO:SubProcess create_model() called ==================================
2024-04-21 13:37:35,088:INFO:Initializing create_model()
2024-04-21 13:37:35,088:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=gbc, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:37:35,088:INFO:Checking exceptions
2024-04-21 13:37:35,088:INFO:Importing libraries
2024-04-21 13:37:35,088:INFO:Copying training dataset
2024-04-21 13:37:35,101:INFO:Defining folds
2024-04-21 13:37:35,101:INFO:Declaring metric variables
2024-04-21 13:37:35,104:INFO:Importing untrained model
2024-04-21 13:37:35,106:INFO:Gradient Boosting Classifier Imported successfully
2024-04-21 13:37:35,110:INFO:Starting cross validation
2024-04-21 13:37:35,111:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:37:59,618:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:59,641:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:59,703:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:59,724:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:59,869:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:37:59,883:INFO:Calculating mean and std
2024-04-21 13:37:59,887:INFO:Creating metrics dataframe
2024-04-21 13:37:59,902:INFO:Uploading results into container
2024-04-21 13:37:59,903:INFO:Uploading model into container now
2024-04-21 13:37:59,904:INFO:_master_model_container: 11
2024-04-21 13:37:59,904:INFO:_display_container: 2
2024-04-21 13:37:59,908: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)
2024-04-21 13:37:59,908:INFO:create_model() successfully completed......................................
2024-04-21 13:38:00,080:INFO:SubProcess create_model() end ==================================
2024-04-21 13:38:00,080:INFO:Creating metrics dataframe
2024-04-21 13:38:00,087:INFO:Initializing Linear Discriminant Analysis
2024-04-21 13:38:00,087:INFO:Total runtime is 0.6792931159337361 minutes
2024-04-21 13:38:00,089:INFO:SubProcess create_model() called ==================================
2024-04-21 13:38:00,089:INFO:Initializing create_model()
2024-04-21 13:38:00,089:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=lda, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:38:00,089:INFO:Checking exceptions
2024-04-21 13:38:00,089:INFO:Importing libraries
2024-04-21 13:38:00,090:INFO:Copying training dataset
2024-04-21 13:38:00,101:INFO:Defining folds
2024-04-21 13:38:00,101:INFO:Declaring metric variables
2024-04-21 13:38:00,103:INFO:Importing untrained model
2024-04-21 13:38:00,105:INFO:Linear Discriminant Analysis Imported successfully
2024-04-21 13:38:00,108:INFO:Starting cross validation
2024-04-21 13:38:00,109:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:38:00,248:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:38:00,256:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:38:00,274:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:38:00,276:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:38:00,288:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:38:00,300:INFO:Calculating mean and std
2024-04-21 13:38:00,301:INFO:Creating metrics dataframe
2024-04-21 13:38:00,302:INFO:Uploading results into container
2024-04-21 13:38:00,303:INFO:Uploading model into container now
2024-04-21 13:38:00,303:INFO:_master_model_container: 12
2024-04-21 13:38:00,303:INFO:_display_container: 2
2024-04-21 13:38:00,304:INFO:LinearDiscriminantAnalysis(covariance_estimator=None, n_components=None,
priors=None, shrinkage=None, solver='svd',
store_covariance=False, tol=0.0001)
2024-04-21 13:38:00,304:INFO:create_model() successfully completed......................................
2024-04-21 13:38:00,399:INFO:SubProcess create_model() end ==================================
2024-04-21 13:38:00,399:INFO:Creating metrics dataframe
2024-04-21 13:38:00,406:INFO:Initializing Extra Trees Classifier
2024-04-21 13:38:00,406:INFO:Total runtime is 0.6846056818962096 minutes
2024-04-21 13:38:00,408:INFO:SubProcess create_model() called ==================================
2024-04-21 13:38:00,408:INFO:Initializing create_model()
2024-04-21 13:38:00,408:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=et, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:38:00,408:INFO:Checking exceptions
2024-04-21 13:38:00,408:INFO:Importing libraries
2024-04-21 13:38:00,408:INFO:Copying training dataset
2024-04-21 13:38:00,421:INFO:Defining folds
2024-04-21 13:38:00,422:INFO:Declaring metric variables
2024-04-21 13:38:00,424:INFO:Importing untrained model
2024-04-21 13:38:00,426:INFO:Extra Trees Classifier Imported successfully
2024-04-21 13:38:00,430:INFO:Starting cross validation
2024-04-21 13:38:00,431:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:38:02,269:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:38:02,448:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:38:02,465:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:38:02,524:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:38:02,525:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:38:02,542:INFO:Calculating mean and std
2024-04-21 13:38:02,546:INFO:Creating metrics dataframe
2024-04-21 13:38:02,552:INFO:Uploading results into container
2024-04-21 13:38:02,553:INFO:Uploading model into container now
2024-04-21 13:38:02,554:INFO:_master_model_container: 13
2024-04-21 13:38:02,554:INFO:_display_container: 2
2024-04-21 13:38:02,555:INFO:ExtraTreesClassifier(bootstrap=False, 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)
2024-04-21 13:38:02,555:INFO:create_model() successfully completed......................................
2024-04-21 13:38:02,736:INFO:SubProcess create_model() end ==================================
2024-04-21 13:38:02,736:INFO:Creating metrics dataframe
2024-04-21 13:38:02,744:INFO:Initializing Extreme Gradient Boosting
2024-04-21 13:38:02,744:INFO:Total runtime is 0.7235753337542216 minutes
2024-04-21 13:38:02,746:INFO:SubProcess create_model() called ==================================
2024-04-21 13:38:02,747:INFO:Initializing create_model()
2024-04-21 13:38:02,747:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=xgboost, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:38:02,747:INFO:Checking exceptions
2024-04-21 13:38:02,747:INFO:Importing libraries
2024-04-21 13:38:02,747:INFO:Copying training dataset
2024-04-21 13:38:02,760:INFO:Defining folds
2024-04-21 13:38:02,760:INFO:Declaring metric variables
2024-04-21 13:38:02,762:INFO:Importing untrained model
2024-04-21 13:38:02,765:INFO:Extreme Gradient Boosting Imported successfully
2024-04-21 13:38:02,769:INFO:Starting cross validation
2024-04-21 13:38:02,770:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:38:25,157:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:38:25,249:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:38:39,656:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:38:39,892:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:38:40,000:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:38:40,024:INFO:Calculating mean and std
2024-04-21 13:38:40,032:INFO:Creating metrics dataframe
2024-04-21 13:38:40,039:INFO:Uploading results into container
2024-04-21 13:38:40,040:INFO:Uploading model into container now
2024-04-21 13:38:40,041:INFO:_master_model_container: 14
2024-04-21 13:38:40,041:INFO:_display_container: 2
2024-04-21 13:38:40,043:INFO:XGBClassifier(base_score=None, booster='gbtree', callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=100, n_jobs=-1, num_parallel_tree=None,
objective='binary:logistic', predictor=None, ...)
2024-04-21 13:38:40,044:INFO:create_model() successfully completed......................................
2024-04-21 13:38:40,275:INFO:SubProcess create_model() end ==================================
2024-04-21 13:38:40,275:INFO:Creating metrics dataframe
2024-04-21 13:38:40,283:INFO:Initializing Light Gradient Boosting Machine
2024-04-21 13:38:40,283:INFO:Total runtime is 1.3492238958676657 minutes
2024-04-21 13:38:40,285:INFO:SubProcess create_model() called ==================================
2024-04-21 13:38:40,285:INFO:Initializing create_model()
2024-04-21 13:38:40,285:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=lightgbm, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:38:40,286:INFO:Checking exceptions
2024-04-21 13:38:40,286:INFO:Importing libraries
2024-04-21 13:38:40,286:INFO:Copying training dataset
2024-04-21 13:38:40,300:INFO:Defining folds
2024-04-21 13:38:40,300:INFO:Declaring metric variables
2024-04-21 13:38:40,302:INFO:Importing untrained model
2024-04-21 13:38:40,304:INFO:Light Gradient Boosting Machine Imported successfully
2024-04-21 13:38:40,308:INFO:Starting cross validation
2024-04-21 13:38:40,309:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:39:31,040:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:39:31,145:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:39:31,221:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:39:31,440:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:39:31,538:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:39:31,563:INFO:Calculating mean and std
2024-04-21 13:39:31,568:INFO:Creating metrics dataframe
2024-04-21 13:39:31,575:INFO:Uploading results into container
2024-04-21 13:39:31,576:INFO:Uploading model into container now
2024-04-21 13:39:31,577:INFO:_master_model_container: 15
2024-04-21 13:39:31,577:INFO:_display_container: 2
2024-04-21 13:39:31,578:INFO:LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,
importance_type='split', learning_rate=0.1, max_depth=-1,
min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,
random_state=123, reg_alpha=0.0, reg_lambda=0.0, subsample=1.0,
subsample_for_bin=200000, subsample_freq=0)
2024-04-21 13:39:31,578:INFO:create_model() successfully completed......................................
2024-04-21 13:39:31,810:INFO:SubProcess create_model() end ==================================
2024-04-21 13:39:31,811:INFO:Creating metrics dataframe
2024-04-21 13:39:31,818:INFO:Initializing CatBoost Classifier
2024-04-21 13:39:31,819:INFO:Total runtime is 2.208153529961904 minutes
2024-04-21 13:39:31,821:INFO:SubProcess create_model() called ==================================
2024-04-21 13:39:31,821:INFO:Initializing create_model()
2024-04-21 13:39:31,821:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=catboost, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:39:31,821:INFO:Checking exceptions
2024-04-21 13:39:31,822:INFO:Importing libraries
2024-04-21 13:39:31,822:INFO:Copying training dataset
2024-04-21 13:39:31,835:INFO:Defining folds
2024-04-21 13:39:31,835:INFO:Declaring metric variables
2024-04-21 13:39:31,837:INFO:Importing untrained model
2024-04-21 13:39:31,841:INFO:CatBoost Classifier Imported successfully
2024-04-21 13:39:31,845:INFO:Starting cross validation
2024-04-21 13:39:31,846:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:40:48,704:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:48,984:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:49,108:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:49,206:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:49,217:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:49,264:INFO:Calculating mean and std
2024-04-21 13:40:49,281:INFO:Creating metrics dataframe
2024-04-21 13:40:49,305:INFO:Uploading results into container
2024-04-21 13:40:49,307:INFO:Uploading model into container now
2024-04-21 13:40:49,313:INFO:_master_model_container: 16
2024-04-21 13:40:49,313:INFO:_display_container: 2
2024-04-21 13:40:49,313:INFO:<catboost.core.CatBoostClassifier object at 0x28c557220>
2024-04-21 13:40:49,314:INFO:create_model() successfully completed......................................
2024-04-21 13:40:49,690:INFO:SubProcess create_model() end ==================================
2024-04-21 13:40:49,690:INFO:Creating metrics dataframe
2024-04-21 13:40:49,698:INFO:Initializing Dummy Classifier
2024-04-21 13:40:49,699:INFO:Total runtime is 3.506152598063151 minutes
2024-04-21 13:40:49,701:INFO:SubProcess create_model() called ==================================
2024-04-21 13:40:49,702:INFO:Initializing create_model()
2024-04-21 13:40:49,702:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=dummy, fold=StratifiedKFold(n_splits=5, 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 0x28a72feb0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:40:49,702:INFO:Checking exceptions
2024-04-21 13:40:49,702:INFO:Importing libraries
2024-04-21 13:40:49,703:INFO:Copying training dataset
2024-04-21 13:40:49,730:INFO:Defining folds
2024-04-21 13:40:49,730:INFO:Declaring metric variables
2024-04-21 13:40:49,732:INFO:Importing untrained model
2024-04-21 13:40:49,734:INFO:Dummy Classifier Imported successfully
2024-04-21 13:40:49,738:INFO:Starting cross validation
2024-04-21 13:40:49,739:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:40:49,887:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:49,893:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:49,911:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:49,915:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:49,998:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:50,003:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:50,230:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:50,245:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:50,262:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:50,281:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:50,338:INFO:Calculating mean and std
2024-04-21 13:40:50,341:INFO:Creating metrics dataframe
2024-04-21 13:40:50,342:INFO:Uploading results into container
2024-04-21 13:40:50,343:INFO:Uploading model into container now
2024-04-21 13:40:50,343:INFO:_master_model_container: 17
2024-04-21 13:40:50,343:INFO:_display_container: 2
2024-04-21 13:40:50,343:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:40:50,343:INFO:create_model() successfully completed......................................
2024-04-21 13:40:50,457:INFO:SubProcess create_model() end ==================================
2024-04-21 13:40:50,457:INFO:Creating metrics dataframe
2024-04-21 13:40:50,466:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py:323: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead.
master_display_.apply(
2024-04-21 13:40:50,471:INFO:Initializing create_model()
2024-04-21 13:40:50,472:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:40:50,472:INFO:Checking exceptions
2024-04-21 13:40:50,479:INFO:Importing libraries
2024-04-21 13:40:50,479:INFO:Copying training dataset
2024-04-21 13:40:50,491:INFO:Defining folds
2024-04-21 13:40:50,491:INFO:Declaring metric variables
2024-04-21 13:40:50,491:INFO:Importing untrained model
2024-04-21 13:40:50,491:INFO:Declaring custom model
2024-04-21 13:40:50,492:INFO:Dummy Classifier Imported successfully
2024-04-21 13:40:50,493:INFO:Cross validation set to False
2024-04-21 13:40:50,493:INFO:Fitting Model
2024-04-21 13:40:50,531:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:40:50,531:INFO:create_model() successfully completed......................................
2024-04-21 13:40:50,622:INFO:Creating Dashboard logs
2024-04-21 13:40:50,624:INFO:Model: Dummy Classifier
2024-04-21 13:40:50,640:INFO:Logged params: {'constant': None, 'random_state': 123, 'strategy': 'prior'}
2024-04-21 13:40:50,676:INFO:Initializing predict_model()
2024-04-21 13:40:50,676:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x13020ca60>)
2024-04-21 13:40:50,676:INFO:Checking exceptions
2024-04-21 13:40:50,676:INFO:Preloading libraries
2024-04-21 13:40:50,785:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py:585: UserWarning: Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 580, in _calculate_metric
calculated_metric = score_func(y_test, target, sample_weight=weights, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 583, in _calculate_metric
calculated_metric = score_func(y_test, target, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
warnings.warn(traceback.format_exc())
2024-04-21 13:40:50,788:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:51,049:INFO:Creating Dashboard logs
2024-04-21 13:40:51,051:INFO:Model: Logistic Regression
2024-04-21 13:40:51,062:INFO:Logged params: {'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}
2024-04-21 13:40:51,220:INFO:Creating Dashboard logs
2024-04-21 13:40:51,224:INFO:Model: Ridge Classifier
2024-04-21 13:40:51,235:INFO:Logged params: {'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}
2024-04-21 13:40:51,375:INFO:Creating Dashboard logs
2024-04-21 13:40:51,377:INFO:Model: Linear Discriminant Analysis
2024-04-21 13:40:51,388:INFO:Logged params: {'covariance_estimator': None, 'n_components': None, 'priors': None, 'shrinkage': None, 'solver': 'svd', 'store_covariance': False, 'tol': 0.0001}
2024-04-21 13:40:51,548:INFO:Creating Dashboard logs
2024-04-21 13:40:51,551:INFO:Model: Ada Boost Classifier
2024-04-21 13:40:51,568:INFO:Logged params: {'algorithm': 'SAMME.R', 'estimator': None, 'learning_rate': 1.0, 'n_estimators': 50, 'random_state': 123}
2024-04-21 13:40:51,967:INFO:Creating Dashboard logs
2024-04-21 13:40:51,977:INFO:Model: Gradient Boosting Classifier
2024-04-21 13:40:51,997:INFO:Logged params: {'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}
2024-04-21 13:40:52,431:INFO:Creating Dashboard logs
2024-04-21 13:40:52,481:INFO:Model: Extreme Gradient Boosting
2024-04-21 13:40:52,508:INFO:Logged params: {'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': 'gbtree', 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 100, 'n_jobs': -1, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': 'auto', 'validate_parameters': None, 'verbosity': 0}
2024-04-21 13:40:52,842:INFO:Creating Dashboard logs
2024-04-21 13:40:52,845:INFO:Model: SVM - Linear Kernel
2024-04-21 13:40:52,859:INFO:Logged params: {'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}
2024-04-21 13:40:53,149:INFO:Creating Dashboard logs
2024-04-21 13:40:53,159:INFO:Model: Random Forest Classifier
2024-04-21 13:40:53,200:INFO:Logged params: {'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}
2024-04-21 13:40:53,676:INFO:Creating Dashboard logs
2024-04-21 13:40:53,687:INFO:Model: CatBoost Classifier
2024-04-21 13:40:53,729:WARNING:Couldn't get params for model. Exception:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/loggers/dashboard_logger.py", line 78, in log_model
params = params.get_all_params()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/catboost/core.py", line 3413, in get_all_params
raise CatBoostError("There is no trained model to use get_all_params(). Use fit() to train model. Then use this method.")
_catboost.CatBoostError: There is no trained model to use get_all_params(). Use fit() to train model. Then use this method.
2024-04-21 13:40:53,730:INFO:Logged params: {}
2024-04-21 13:40:54,872:INFO:Creating Dashboard logs
2024-04-21 13:40:54,883:INFO:Model: Extra Trees Classifier
2024-04-21 13:40:54,905:INFO:Logged params: {'bootstrap': False, '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}
2024-04-21 13:40:55,430:INFO:Creating Dashboard logs
2024-04-21 13:40:55,434:INFO:Model: Light Gradient Boosting Machine
2024-04-21 13:40:55,447:INFO:Logged params: {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': -1, 'num_leaves': 31, 'objective': None, 'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0}
2024-04-21 13:40:55,612:INFO:Creating Dashboard logs
2024-04-21 13:40:55,614:INFO:Model: Decision Tree Classifier
2024-04-21 13:40:55,630:INFO:Logged params: {'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'}
2024-04-21 13:40:55,835:INFO:Creating Dashboard logs
2024-04-21 13:40:55,838:INFO:Model: Naive Bayes
2024-04-21 13:40:55,848:INFO:Logged params: {'priors': None, 'var_smoothing': 1e-09}
2024-04-21 13:40:56,056:INFO:Creating Dashboard logs
2024-04-21 13:40:56,061:INFO:Model: Quadratic Discriminant Analysis
2024-04-21 13:40:56,098:INFO:Logged params: {'priors': None, 'reg_param': 0.0, 'store_covariance': False, 'tol': 0.0001}
2024-04-21 13:40:56,577:INFO:_master_model_container: 17
2024-04-21 13:40:56,577:INFO:_display_container: 2
2024-04-21 13:40:56,577:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:40:56,577:INFO:compare_models() successfully completed......................................
2024-04-21 13:40:56,578:INFO:Initializing tune_model()
2024-04-21 13:40:56,578:INFO:tune_model(estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fold=5, round=4, n_iter=10, custom_grid=None, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}, self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>)
2024-04-21 13:40:56,578:INFO:Checking exceptions
2024-04-21 13:40:56,595:INFO:Copying training dataset
2024-04-21 13:40:56,616:INFO:Checking base model
2024-04-21 13:40:56,616:INFO:Base model : Dummy Classifier
2024-04-21 13:40:56,619:INFO:Declaring metric variables
2024-04-21 13:40:56,622:INFO:Defining Hyperparameters
2024-04-21 13:40:56,622:INFO:10 is bigger than total combinations 4, setting search algorithm to grid
2024-04-21 13:40:56,727:INFO:Tuning with n_jobs=-1
2024-04-21 13:40:56,727:INFO:Initializing GridSearchCV
2024-04-21 13:40:58,781:INFO:best_params: {'actual_estimator__strategy': 'most_frequent'}
2024-04-21 13:40:58,786:INFO:Hyperparameter search completed
2024-04-21 13:40:58,787:INFO:SubProcess create_model() called ==================================
2024-04-21 13:40:58,790:INFO:Initializing create_model()
2024-04-21 13:40:58,790:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x28a53c2b0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={'strategy': 'most_frequent'})
2024-04-21 13:40:58,790:INFO:Checking exceptions
2024-04-21 13:40:58,790:INFO:Importing libraries
2024-04-21 13:40:58,791:INFO:Copying training dataset
2024-04-21 13:40:58,826:INFO:Defining folds
2024-04-21 13:40:58,826:INFO:Declaring metric variables
2024-04-21 13:40:58,835:INFO:Importing untrained model
2024-04-21 13:40:58,835:INFO:Declaring custom model
2024-04-21 13:40:58,842:INFO:Dummy Classifier Imported successfully
2024-04-21 13:40:58,851:INFO:Starting cross validation
2024-04-21 13:40:58,853:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:40:59,029:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:59,031:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:59,039:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:59,036:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:59,040:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:59,050:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:59,054:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:59,062:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:59,064:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:59,071:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:59,082:INFO:Calculating mean and std
2024-04-21 13:40:59,085:INFO:Creating metrics dataframe
2024-04-21 13:40:59,166:INFO:Finalizing model
2024-04-21 13:40:59,292:INFO:Uploading results into container
2024-04-21 13:40:59,293:INFO:Uploading model into container now
2024-04-21 13:40:59,294:INFO:_master_model_container: 18
2024-04-21 13:40:59,294:INFO:_display_container: 3
2024-04-21 13:40:59,295:INFO:DummyClassifier(constant=None, random_state=123, strategy='most_frequent')
2024-04-21 13:40:59,295:INFO:create_model() successfully completed......................................
2024-04-21 13:40:59,590:INFO:SubProcess create_model() end ==================================
2024-04-21 13:40:59,590:INFO:choose_better activated
2024-04-21 13:40:59,594:INFO:SubProcess create_model() called ==================================
2024-04-21 13:40:59,595:INFO:Initializing create_model()
2024-04-21 13:40:59,595:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:40:59,595:INFO:Checking exceptions
2024-04-21 13:40:59,597:INFO:Importing libraries
2024-04-21 13:40:59,597:INFO:Copying training dataset
2024-04-21 13:40:59,614:INFO:Defining folds
2024-04-21 13:40:59,614:INFO:Declaring metric variables
2024-04-21 13:40:59,615:INFO:Importing untrained model
2024-04-21 13:40:59,615:INFO:Declaring custom model
2024-04-21 13:40:59,615:INFO:Dummy Classifier Imported successfully
2024-04-21 13:40:59,616:INFO:Starting cross validation
2024-04-21 13:40:59,617:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2024-04-21 13:40:59,815:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:59,820:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:59,822:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:59,826:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:59,829:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:59,853:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:59,853:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:59,859:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:59,857:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 4108, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6200, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6249, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-21 13:40:59,865:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:40:59,874:INFO:Calculating mean and std
2024-04-21 13:40:59,875:INFO:Creating metrics dataframe
2024-04-21 13:40:59,884:INFO:Finalizing model
2024-04-21 13:40:59,956:INFO:Uploading results into container
2024-04-21 13:40:59,957:INFO:Uploading model into container now
2024-04-21 13:40:59,957:INFO:_master_model_container: 19
2024-04-21 13:40:59,958:INFO:_display_container: 4
2024-04-21 13:40:59,958:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:40:59,958:INFO:create_model() successfully completed......................................
2024-04-21 13:41:00,141:INFO:SubProcess create_model() end ==================================
2024-04-21 13:41:00,142:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior') result for Accuracy is 0.1979
2024-04-21 13:41:00,142:INFO:DummyClassifier(constant=None, random_state=123, strategy='most_frequent') result for Accuracy is 0.1979
2024-04-21 13:41:00,142:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior') is best model
2024-04-21 13:41:00,143:INFO:choose_better completed
2024-04-21 13:41:00,143:INFO:Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).
2024-04-21 13:41:00,143:INFO:Creating Dashboard logs
2024-04-21 13:41:00,147:INFO:Model: Dummy Classifier
2024-04-21 13:41:00,166:INFO:Logged params: {'constant': None, 'random_state': 123, 'strategy': 'prior'}
2024-04-21 13:41:00,200:INFO:Initializing predict_model()
2024-04-21 13:41:00,200:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x13020e7a0>)
2024-04-21 13:41:00,200:INFO:Checking exceptions
2024-04-21 13:41:00,200:INFO:Preloading libraries
2024-04-21 13:41:00,359:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py:585: UserWarning: Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 580, in _calculate_metric
calculated_metric = score_func(y_test, target, sample_weight=weights, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 583, in _calculate_metric
calculated_metric = score_func(y_test, target, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
warnings.warn(traceback.format_exc())
2024-04-21 13:41:00,365:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:41:00,774:INFO:_master_model_container: 19
2024-04-21 13:41:00,774:INFO:_display_container: 3
2024-04-21 13:41:00,774:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:41:00,774:INFO:tune_model() successfully completed......................................
2024-04-21 13:41:00,881:INFO:Initializing finalize_model()
2024-04-21 13:41:00,881:INFO:finalize_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fit_kwargs=None, groups=None, model_only=False, experiment_custom_tags=None)
2024-04-21 13:41:00,884:INFO:Finalizing DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-21 13:41:00,891:INFO:Initializing create_model()
2024-04-21 13:41:00,891:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=DummyClassifier(constant=None, random_state=123, strategy='prior'), fold=None, round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=False, metrics=None, display=None, model_only=False, return_train_score=False, error_score=0.0, kwargs={})
2024-04-21 13:41:00,891:INFO:Checking exceptions
2024-04-21 13:41:00,892:INFO:Importing libraries
2024-04-21 13:41:00,892:INFO:Copying training dataset
2024-04-21 13:41:00,894:INFO:Defining folds
2024-04-21 13:41:00,894:INFO:Declaring metric variables
2024-04-21 13:41:00,894:INFO:Importing untrained model
2024-04-21 13:41:00,894:INFO:Declaring custom model
2024-04-21 13:41:00,895:INFO:Dummy Classifier Imported successfully
2024-04-21 13:41:00,896:INFO:Cross validation set to False
2024-04-21 13:41:00,896:INFO:Fitting Model
2024-04-21 13:41:00,936:INFO:Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Age^2 * BMI^2'],
transformer=SimpleImputer(add_indicator=Fal...
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
DummyClassifier(constant=None, random_state=123,
strategy='prior'))],
verbose=False)
2024-04-21 13:41:00,936:INFO:create_model() successfully completed......................................
2024-04-21 13:41:01,029:INFO:Creating Dashboard logs
2024-04-21 13:41:01,029:INFO:Model: Dummy Classifier
2024-04-21 13:41:01,039:INFO:Logged params: {'constant': None, 'random_state': 123, 'strategy': 'prior'}
2024-04-21 13:41:01,160:INFO:_master_model_container: 19
2024-04-21 13:41:01,160:INFO:_display_container: 3
2024-04-21 13:41:01,164:INFO:Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Age^2 * BMI^2'],
transformer=SimpleImputer(add_indicator=Fal...
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
DummyClassifier(constant=None, random_state=123,
strategy='prior'))],
verbose=False)
2024-04-21 13:41:01,164:INFO:finalize_model() successfully completed......................................
2024-04-21 13:41:01,269:INFO:Initializing predict_model()
2024-04-21 13:41:01,269:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Age^2 * BMI^2'],
transformer=SimpleImputer(add_indicator=Fal...
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
DummyClassifier(constant=None, random_state=123,
strategy='prior'))],
verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x13019e7a0>)
2024-04-21 13:41:01,269:INFO:Checking exceptions
2024-04-21 13:41:01,269:INFO:Preloading libraries
2024-04-21 13:41:01,270:INFO:Set up data.
2024-04-21 13:41:01,280:INFO:Set up index.
2024-04-21 13:41:01,296:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py:585: UserWarning: Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 580, in _calculate_metric
calculated_metric = score_func(y_test, target, sample_weight=weights, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 583, in _calculate_metric
calculated_metric = score_func(y_test, target, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
warnings.warn(traceback.format_exc())
2024-04-21 13:41:01,299:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-21 13:41:01,402:INFO:Initializing predict_model()
2024-04-21 13:41:01,402:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x105c300d0>, estimator=Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'BMI', 'Age^2',
'Age^3', 'BMI^2', 'Age * BMI',
'Age * BMI^2', 'Age^2 * BMI^2'],
transformer=SimpleImputer(add_indicator=Fal...
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
DummyClassifier(constant=None, random_state=123,
strategy='prior'))],
verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x13019e7a0>)
2024-04-21 13:41:01,402:INFO:Checking exceptions
2024-04-21 13:41:01,402:INFO:Preloading libraries
2024-04-21 13:41:01,403:INFO:Set up data.
2024-04-21 13:41:01,437:INFO:Set up index.
2024-04-21 13:41:01,482:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py:585: UserWarning: Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 580, in _calculate_metric
calculated_metric = score_func(y_test, target, sample_weight=weights, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/utils/generic.py", line 583, in _calculate_metric
calculated_metric = score_func(y_test, target, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 136, in __call__
return self.score_func(y_true, y_pred, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 621, in roc_auc_score
return _multiclass_roc_auc_score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_ranking.py", line 693, in _multiclass_roc_auc_score
if not np.allclose(1, y_score.sum(axis=1)):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/numpy/core/_methods.py", line 49, in _sum
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
warnings.warn(traceback.format_exc())
2024-04-21 13:41:01,495:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:42:48,136:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-04-26 01:42:48,137:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-04-26 01:42:48,137:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-04-26 01:42:48,137:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-04-26 01:42:49,843:INFO:PyCaret ClassificationExperiment
2024-04-26 01:42:49,843:INFO:Logging name: clf-default-name
2024-04-26 01:42:49,843:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2024-04-26 01:42:49,843:INFO:version 3.3.0
2024-04-26 01:42:49,843:INFO:Initializing setup()
2024-04-26 01:42:49,843:INFO:self.USI: 53f2
2024-04-26 01:42:49,843:INFO:self._variable_keys: {'X', 'html_param', 'X_train', 'pipeline', 'fold_generator', 'fold_groups_param', 'idx', 'y', 'target_param', 'exp_name_log', '_ml_usecase', '_available_plots', 'is_multiclass', 'USI', 'log_plots_param', 'data', 'fold_shuffle_param', 'logging_param', 'seed', 'memory', 'y_train', 'gpu_n_jobs_param', 'fix_imbalance', 'y_test', 'n_jobs_param', 'gpu_param', 'X_test', 'exp_id'}
2024-04-26 01:42:49,843:INFO:Checking environment
2024-04-26 01:42:49,843:INFO:python_version: 3.10.13
2024-04-26 01:42:49,843:INFO:python_build: ('main', 'Sep 11 2023 08:16:02')
2024-04-26 01:42:49,843:INFO:machine: arm64
2024-04-26 01:42:49,843:INFO:platform: macOS-14.0-arm64-arm-64bit
2024-04-26 01:42:49,843:INFO:Memory: svmem(total=8589934592, available=1313374208, percent=84.7, used=2849144832, free=17711104, active=1304920064, inactive=1249099776, wired=1544224768)
2024-04-26 01:42:49,843:INFO:Physical Core: 8
2024-04-26 01:42:49,843:INFO:Logical Core: 8
2024-04-26 01:42:49,843:INFO:Checking libraries
2024-04-26 01:42:49,843:INFO:System:
2024-04-26 01:42:49,843:INFO: python: 3.10.13 (main, Sep 11 2023, 08:16:02) [Clang 14.0.6 ]
2024-04-26 01:42:49,843:INFO:executable: /Users/arham/anaconda3/envs/DataScience/bin/python
2024-04-26 01:42:49,844:INFO: machine: macOS-14.0-arm64-arm-64bit
2024-04-26 01:42:49,844:INFO:PyCaret required dependencies:
2024-04-26 01:42:49,845:INFO: pip: 23.3
2024-04-26 01:42:49,845:INFO: setuptools: 60.2.0
2024-04-26 01:42:49,845:INFO: pycaret: 3.3.0
2024-04-26 01:42:49,845:INFO: IPython: 8.15.0
2024-04-26 01:42:49,845:INFO: ipywidgets: 8.0.4
2024-04-26 01:42:49,845:INFO: tqdm: 4.65.2
2024-04-26 01:42:49,845:INFO: numpy: 1.25.2
2024-04-26 01:42:49,845:INFO: pandas: 1.5.3
2024-04-26 01:42:49,845:INFO: jinja2: 3.1.3
2024-04-26 01:42:49,845:INFO: scipy: 1.11.4
2024-04-26 01:42:49,845:INFO: joblib: 1.2.0
2024-04-26 01:42:49,845:INFO: sklearn: 1.4.0
2024-04-26 01:42:49,845:INFO: pyod: 1.1.3
2024-04-26 01:42:49,845:INFO: imblearn: 0.12.2
2024-04-26 01:42:49,845:INFO: category_encoders: 2.6.3
2024-04-26 01:42:49,845:INFO: lightgbm: 4.1.0
2024-04-26 01:42:49,845:INFO: numba: 0.58.1
2024-04-26 01:42:49,845:INFO: requests: 2.31.0
2024-04-26 01:42:49,845:INFO: matplotlib: 3.8.4
2024-04-26 01:42:49,845:INFO: scikitplot: 0.3.7
2024-04-26 01:42:49,845:INFO: yellowbrick: 1.5
2024-04-26 01:42:49,845:INFO: plotly: 5.17.0
2024-04-26 01:42:49,845:INFO: plotly-resampler: Not installed
2024-04-26 01:42:49,845:INFO: kaleido: 0.2.1
2024-04-26 01:42:49,845:INFO: schemdraw: 0.15
2024-04-26 01:42:49,845:INFO: statsmodels: 0.13.5
2024-04-26 01:42:49,845:INFO: sktime: 0.26.1
2024-04-26 01:42:49,845:INFO: tbats: 1.1.3
2024-04-26 01:42:49,845:INFO: pmdarima: 2.0.4
2024-04-26 01:42:49,845:INFO: psutil: 5.9.0
2024-04-26 01:42:49,845:INFO: markupsafe: 2.1.1
2024-04-26 01:42:49,845:INFO: pickle5: Not installed
2024-04-26 01:42:49,845:INFO: cloudpickle: 2.2.1
2024-04-26 01:42:49,845:INFO: deprecation: 2.1.0
2024-04-26 01:42:49,846:INFO: xxhash: 3.4.1
2024-04-26 01:42:49,846:INFO: wurlitzer: 3.0.2
2024-04-26 01:42:49,846:INFO:PyCaret optional dependencies:
2024-04-26 01:42:51,005:INFO: shap: 0.44.0
2024-04-26 01:42:51,005:INFO: interpret: Not installed
2024-04-26 01:42:51,005:INFO: umap: Not installed
2024-04-26 01:42:51,005:INFO: ydata_profiling: 0.0.dev0
2024-04-26 01:42:51,005:INFO: explainerdashboard: Not installed
2024-04-26 01:42:51,005:INFO: autoviz: Not installed
2024-04-26 01:42:51,005:INFO: fairlearn: Not installed
2024-04-26 01:42:51,005:INFO: deepchecks: Not installed
2024-04-26 01:42:51,005:INFO: xgboost: 1.7.3
2024-04-26 01:42:51,005:INFO: catboost: 1.1.1
2024-04-26 01:42:51,005:INFO: kmodes: Not installed
2024-04-26 01:42:51,006:INFO: mlxtend: Not installed
2024-04-26 01:42:51,006:INFO: statsforecast: 1.4.0
2024-04-26 01:42:51,006:INFO: tune_sklearn: Not installed
2024-04-26 01:42:51,006:INFO: ray: 2.10.0
2024-04-26 01:42:51,006:INFO: hyperopt: 0.2.7
2024-04-26 01:42:51,006:INFO: optuna: 3.5.0
2024-04-26 01:42:51,006:INFO: skopt: Not installed
2024-04-26 01:42:51,006:INFO: mlflow: 2.10.2
2024-04-26 01:42:51,006:INFO: gradio: 3.48.0
2024-04-26 01:42:51,006:INFO: fastapi: 0.109.2
2024-04-26 01:42:51,006:INFO: uvicorn: 0.27.1
2024-04-26 01:42:51,006:INFO: m2cgen: Not installed
2024-04-26 01:42:51,006:INFO: evidently: Not installed
2024-04-26 01:42:51,006:INFO: fugue: Not installed
2024-04-26 01:42:51,006:INFO: streamlit: 1.27.2
2024-04-26 01:42:51,006:INFO: prophet: Not installed
2024-04-26 01:42:51,006:INFO:None
2024-04-26 01:42:51,006:INFO:Set up data.
2024-04-26 01:42:51,023:INFO:Set up folding strategy.
2024-04-26 01:42:51,023:INFO:Set up train/test split.
2024-04-26 01:42:51,039:INFO:Set up index.
2024-04-26 01:42:51,041:INFO:Assigning column types.
2024-04-26 01:42:51,045:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2024-04-26 01:42:51,079:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-26 01:42:51,082:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 01:42:51,107:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 01:42:51,142:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 01:42:51,266:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-26 01:42:51,267:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 01:42:51,287:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 01:42:51,289:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 01:42:51,290:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2024-04-26 01:42:51,323:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 01:42:51,344:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 01:42:51,346:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 01:42:51,375:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 01:42:51,392:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 01:42:51,394:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 01:42:51,395:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2024-04-26 01:42:51,441:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 01:42:51,443:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 01:42:51,490:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 01:42:51,492:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 01:42:51,494:INFO:Preparing preprocessing pipeline...
2024-04-26 01:42:51,496:INFO:Set up simple imputation.
2024-04-26 01:42:51,499:INFO:Set up encoding of categorical features.
2024-04-26 01:42:51,500:INFO:Set up column name cleaning.
2024-04-26 01:42:51,570:INFO:Finished creating preprocessing pipeline.
2024-04-26 01:42:51,576:INFO:Pipeline: Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'M...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False)
2024-04-26 01:42:51,576:INFO:Creating final display dataframe.
2024-04-26 01:42:51,744:INFO:Setup _display_container: Description Value
0 Session id 123
1 Target NObeyesdad
2 Target type Multiclass
3 Original data shape (20758, 35)
4 Transformed data shape (20758, 39)
5 Transformed train set shape (14530, 39)
6 Transformed test set shape (6228, 39)
7 Numeric features 33
8 Categorical features 1
9 Preprocess True
10 Imputation type simple
11 Numeric imputation mean
12 Categorical imputation mode
13 Maximum one-hot encoding 25
14 Encoding method None
15 Fold Generator StratifiedKFold
16 Fold Number 10
17 CPU Jobs -1
18 Use GPU False
19 Log Experiment False
20 Experiment Name clf-default-name
21 USI 53f2
2024-04-26 01:42:51,810:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 01:42:51,812:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 01:42:51,868:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 01:42:51,870:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 01:42:51,871:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour.
warnings.warn(
2024-04-26 01:42:51,872:INFO:setup() successfully completed in 2.03s...............
2024-04-26 01:42:51,872:INFO:Initializing compare_models()
2024-04-26 01:42:51,872:INFO:compare_models(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, 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 0x2844e0250>, '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)
2024-04-26 01:42:51,872:INFO:Checking exceptions
2024-04-26 01:42:51,878:INFO:Preparing display monitor
2024-04-26 01:42:51,930:INFO:Initializing Logistic Regression
2024-04-26 01:42:51,930:INFO:Total runtime is 7.414817810058594e-06 minutes
2024-04-26 01:42:51,933:INFO:SubProcess create_model() called ==================================
2024-04-26 01:42:51,933:INFO:Initializing create_model()
2024-04-26 01:42:51,934:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:42:51,934:INFO:Checking exceptions
2024-04-26 01:42:51,934:INFO:Importing libraries
2024-04-26 01:42:51,934:INFO:Copying training dataset
2024-04-26 01:42:51,948:INFO:Defining folds
2024-04-26 01:42:51,948:INFO:Declaring metric variables
2024-04-26 01:42:51,951:INFO:Importing untrained model
2024-04-26 01:42:51,954:INFO:Logistic Regression Imported successfully
2024-04-26 01:42:51,959:INFO:Starting cross validation
2024-04-26 01:42:51,961:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:43:14,111:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 01:43:14,150:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 01:43:14,209:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 01:43:14,300:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:14,301:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:14,309:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 01:43:14,363:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 01:43:14,391:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:14,404:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 01:43:14,456:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:14,485:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:14,519:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 01:43:14,572:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:14,573:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 01:43:14,642:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:14,657:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:19,199:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 01:43:19,202:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 01:43:19,242:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:19,245:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:19,368:INFO:Calculating mean and std
2024-04-26 01:43:19,371:INFO:Creating metrics dataframe
2024-04-26 01:43:19,379:INFO:Uploading results into container
2024-04-26 01:43:19,379:INFO:Uploading model into container now
2024-04-26 01:43:19,380:INFO:_master_model_container: 1
2024-04-26 01:43:19,380:INFO:_display_container: 2
2024-04-26 01:43:19,381: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)
2024-04-26 01:43:19,381:INFO:create_model() successfully completed......................................
2024-04-26 01:43:19,554:INFO:SubProcess create_model() end ==================================
2024-04-26 01:43:19,554:INFO:Creating metrics dataframe
2024-04-26 01:43:19,561:INFO:Initializing K Neighbors Classifier
2024-04-26 01:43:19,561:INFO:Total runtime is 0.46051711638768517 minutes
2024-04-26 01:43:19,564:INFO:SubProcess create_model() called ==================================
2024-04-26 01:43:19,564:INFO:Initializing create_model()
2024-04-26 01:43:19,564:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:43:19,564:INFO:Checking exceptions
2024-04-26 01:43:19,565:INFO:Importing libraries
2024-04-26 01:43:19,565:INFO:Copying training dataset
2024-04-26 01:43:19,575:INFO:Defining folds
2024-04-26 01:43:19,575:INFO:Declaring metric variables
2024-04-26 01:43:19,577:INFO:Importing untrained model
2024-04-26 01:43:19,580:INFO:K Neighbors Classifier Imported successfully
2024-04-26 01:43:19,586:INFO:Starting cross validation
2024-04-26 01:43:19,587:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:43:20,119:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:20,130:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:20,137:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:20,160:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:20,166:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:20,172:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:20,176:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:20,181:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:20,184:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:20,188:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:20,188:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:20,191:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:20,196:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:20,205:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:20,210:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:20,210:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:20,324:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:20,332:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:20,364:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:20,367:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:20,470:INFO:Calculating mean and std
2024-04-26 01:43:20,472:INFO:Creating metrics dataframe
2024-04-26 01:43:20,476:INFO:Uploading results into container
2024-04-26 01:43:20,476:INFO:Uploading model into container now
2024-04-26 01:43:20,477:INFO:_master_model_container: 2
2024-04-26 01:43:20,477:INFO:_display_container: 2
2024-04-26 01:43:20,477:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform')
2024-04-26 01:43:20,478:INFO:create_model() successfully completed......................................
2024-04-26 01:43:20,609:WARNING:create_model() for KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform') raised an exception or returned all 0.0, trying without fit_kwargs:
2024-04-26 01:43:20,612:WARNING:Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py", line 797, in compare_models
np.sum(
AssertionError
2024-04-26 01:43:20,614:INFO:Initializing create_model()
2024-04-26 01:43:20,614:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:43:20,614:INFO:Checking exceptions
2024-04-26 01:43:20,614:INFO:Importing libraries
2024-04-26 01:43:20,614:INFO:Copying training dataset
2024-04-26 01:43:20,623:INFO:Defining folds
2024-04-26 01:43:20,623:INFO:Declaring metric variables
2024-04-26 01:43:20,626:INFO:Importing untrained model
2024-04-26 01:43:20,628:INFO:K Neighbors Classifier Imported successfully
2024-04-26 01:43:20,631:INFO:Starting cross validation
2024-04-26 01:43:20,632:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:43:21,885:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:21,925:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:21,987:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:22,049:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:22,115:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:22,128:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:22,190:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:22,238:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:22,244:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:22,295:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:22,349:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:22,361:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:22,385:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:22,388:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:22,410:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:22,411:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:22,513:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:22,553:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:22,558:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:22,603:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 01:43:22,775:INFO:Calculating mean and std
2024-04-26 01:43:22,819:INFO:Creating metrics dataframe
2024-04-26 01:43:22,900:INFO:Uploading results into container
2024-04-26 01:43:22,904:INFO:Uploading model into container now
2024-04-26 01:43:22,908:INFO:_master_model_container: 3
2024-04-26 01:43:22,909:INFO:_display_container: 2
2024-04-26 01:43:22,912:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform')
2024-04-26 01:43:22,912:INFO:create_model() successfully completed......................................
2024-04-26 01:43:23,288:ERROR:create_model() for KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform') raised an exception or returned all 0.0:
2024-04-26 01:43:23,289:ERROR:Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py", line 797, in compare_models
np.sum(
AssertionError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py", line 818, in compare_models
np.sum(
AssertionError
2024-04-26 01:43:23,289:INFO:Initializing Naive Bayes
2024-04-26 01:43:23,289:INFO:Total runtime is 0.5226539810498556 minutes
2024-04-26 01:43:23,293:INFO:SubProcess create_model() called ==================================
2024-04-26 01:43:23,294:INFO:Initializing create_model()
2024-04-26 01:43:23,294:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:43:23,294:INFO:Checking exceptions
2024-04-26 01:43:23,294:INFO:Importing libraries
2024-04-26 01:43:23,294:INFO:Copying training dataset
2024-04-26 01:43:23,304:INFO:Defining folds
2024-04-26 01:43:23,304:INFO:Declaring metric variables
2024-04-26 01:43:23,306:INFO:Importing untrained model
2024-04-26 01:43:23,309:INFO:Naive Bayes Imported successfully
2024-04-26 01:43:23,314:INFO:Starting cross validation
2024-04-26 01:43:23,316:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:43:24,073:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:24,087:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:24,175:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:24,196:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:24,372:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:24,316:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:24,662:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:24,706:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:24,884:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:24,904:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:25,024:INFO:Calculating mean and std
2024-04-26 01:43:25,041:INFO:Creating metrics dataframe
2024-04-26 01:43:25,049:INFO:Uploading results into container
2024-04-26 01:43:25,050:INFO:Uploading model into container now
2024-04-26 01:43:25,051:INFO:_master_model_container: 4
2024-04-26 01:43:25,052:INFO:_display_container: 2
2024-04-26 01:43:25,053:INFO:GaussianNB(priors=None, var_smoothing=1e-09)
2024-04-26 01:43:25,053:INFO:create_model() successfully completed......................................
2024-04-26 01:43:25,306:INFO:SubProcess create_model() end ==================================
2024-04-26 01:43:25,306:INFO:Creating metrics dataframe
2024-04-26 01:43:25,317:INFO:Initializing Decision Tree Classifier
2024-04-26 01:43:25,317:INFO:Total runtime is 0.5564561843872071 minutes
2024-04-26 01:43:25,320:INFO:SubProcess create_model() called ==================================
2024-04-26 01:43:25,320:INFO:Initializing create_model()
2024-04-26 01:43:25,320:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:43:25,321:INFO:Checking exceptions
2024-04-26 01:43:25,321:INFO:Importing libraries
2024-04-26 01:43:25,321:INFO:Copying training dataset
2024-04-26 01:43:25,339:INFO:Defining folds
2024-04-26 01:43:25,340:INFO:Declaring metric variables
2024-04-26 01:43:25,346:INFO:Importing untrained model
2024-04-26 01:43:25,350:INFO:Decision Tree Classifier Imported successfully
2024-04-26 01:43:25,357:INFO:Starting cross validation
2024-04-26 01:43:25,359:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:43:26,441:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:26,441:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:26,451:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:26,469:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:26,475:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:26,593:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:26,597:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:26,598:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:26,978:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:27,004:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:27,120:INFO:Calculating mean and std
2024-04-26 01:43:27,123:INFO:Creating metrics dataframe
2024-04-26 01:43:27,129:INFO:Uploading results into container
2024-04-26 01:43:27,130:INFO:Uploading model into container now
2024-04-26 01:43:27,131:INFO:_master_model_container: 5
2024-04-26 01:43:27,131:INFO:_display_container: 2
2024-04-26 01:43:27,131: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')
2024-04-26 01:43:27,131:INFO:create_model() successfully completed......................................
2024-04-26 01:43:27,279:INFO:SubProcess create_model() end ==================================
2024-04-26 01:43:27,279:INFO:Creating metrics dataframe
2024-04-26 01:43:27,286:INFO:Initializing SVM - Linear Kernel
2024-04-26 01:43:27,286:INFO:Total runtime is 0.5892675956090292 minutes
2024-04-26 01:43:27,288:INFO:SubProcess create_model() called ==================================
2024-04-26 01:43:27,288:INFO:Initializing create_model()
2024-04-26 01:43:27,288:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:43:27,288:INFO:Checking exceptions
2024-04-26 01:43:27,288:INFO:Importing libraries
2024-04-26 01:43:27,288:INFO:Copying training dataset
2024-04-26 01:43:27,296:INFO:Defining folds
2024-04-26 01:43:27,296:INFO:Declaring metric variables
2024-04-26 01:43:27,298:INFO:Importing untrained model
2024-04-26 01:43:27,301:INFO:SVM - Linear Kernel Imported successfully
2024-04-26 01:43:27,310:INFO:Starting cross validation
2024-04-26 01:43:27,313:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:43:32,005:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:32,012:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:43:32,368:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:32,378:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:43:32,606:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:32,613:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:43:33,197:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:33,316:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:33,337:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:43:33,345:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:33,354:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:43:33,632:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:33,640:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:43:33,648:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:33,657:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:43:33,755:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:33,760:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:43:33,844:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:33,850:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:43:33,961:INFO:Calculating mean and std
2024-04-26 01:43:33,964:INFO:Creating metrics dataframe
2024-04-26 01:43:33,979:INFO:Uploading results into container
2024-04-26 01:43:33,981:INFO:Uploading model into container now
2024-04-26 01:43:33,981:INFO:_master_model_container: 6
2024-04-26 01:43:33,981:INFO:_display_container: 2
2024-04-26 01:43:33,982: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)
2024-04-26 01:43:33,983:INFO:create_model() successfully completed......................................
2024-04-26 01:43:34,121:INFO:SubProcess create_model() end ==================================
2024-04-26 01:43:34,121:INFO:Creating metrics dataframe
2024-04-26 01:43:34,128:INFO:Initializing Ridge Classifier
2024-04-26 01:43:34,128:INFO:Total runtime is 0.7033106644948324 minutes
2024-04-26 01:43:34,130:INFO:SubProcess create_model() called ==================================
2024-04-26 01:43:34,131:INFO:Initializing create_model()
2024-04-26 01:43:34,131:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:43:34,131:INFO:Checking exceptions
2024-04-26 01:43:34,131:INFO:Importing libraries
2024-04-26 01:43:34,131:INFO:Copying training dataset
2024-04-26 01:43:34,140:INFO:Defining folds
2024-04-26 01:43:34,140:INFO:Declaring metric variables
2024-04-26 01:43:34,142:INFO:Importing untrained model
2024-04-26 01:43:34,145:INFO:Ridge Classifier Imported successfully
2024-04-26 01:43:34,150:INFO:Starting cross validation
2024-04-26 01:43:34,151:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:43:34,355:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:34,363:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:34,368:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:34,388:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:34,413:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:34,421:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:34,451:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:34,459:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:34,531:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:34,534:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 01:43:34,652:INFO:Calculating mean and std
2024-04-26 01:43:34,654:INFO:Creating metrics dataframe
2024-04-26 01:43:34,661:INFO:Uploading results into container
2024-04-26 01:43:34,661:INFO:Uploading model into container now
2024-04-26 01:43:34,663:INFO:_master_model_container: 7
2024-04-26 01:43:34,663:INFO:_display_container: 2
2024-04-26 01:43:34,664: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)
2024-04-26 01:43:34,664:INFO:create_model() successfully completed......................................
2024-04-26 01:43:34,777:INFO:SubProcess create_model() end ==================================
2024-04-26 01:43:34,777:INFO:Creating metrics dataframe
2024-04-26 01:43:34,784:INFO:Initializing Random Forest Classifier
2024-04-26 01:43:34,785:INFO:Total runtime is 0.7142472505569458 minutes
2024-04-26 01:43:34,787:INFO:SubProcess create_model() called ==================================
2024-04-26 01:43:34,787:INFO:Initializing create_model()
2024-04-26 01:43:34,787:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:43:34,787:INFO:Checking exceptions
2024-04-26 01:43:34,787:INFO:Importing libraries
2024-04-26 01:43:34,788:INFO:Copying training dataset
2024-04-26 01:43:34,796:INFO:Defining folds
2024-04-26 01:43:34,796:INFO:Declaring metric variables
2024-04-26 01:43:34,799:INFO:Importing untrained model
2024-04-26 01:43:34,802:INFO:Random Forest Classifier Imported successfully
2024-04-26 01:43:34,807:INFO:Starting cross validation
2024-04-26 01:43:34,808:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:43:43,945:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:44,313:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:44,353:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:44,621:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:44,708:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:44,755:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:45,119:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:45,147:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:46,943:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:46,991:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:47,114:INFO:Calculating mean and std
2024-04-26 01:43:47,118:INFO:Creating metrics dataframe
2024-04-26 01:43:47,140:INFO:Uploading results into container
2024-04-26 01:43:47,141:INFO:Uploading model into container now
2024-04-26 01:43:47,141:INFO:_master_model_container: 8
2024-04-26 01:43:47,141:INFO:_display_container: 2
2024-04-26 01:43:47,142: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)
2024-04-26 01:43:47,142:INFO:create_model() successfully completed......................................
2024-04-26 01:43:47,290:INFO:SubProcess create_model() end ==================================
2024-04-26 01:43:47,290:INFO:Creating metrics dataframe
2024-04-26 01:43:47,301:INFO:Initializing Quadratic Discriminant Analysis
2024-04-26 01:43:47,301:INFO:Total runtime is 0.9228556513786317 minutes
2024-04-26 01:43:47,304:INFO:SubProcess create_model() called ==================================
2024-04-26 01:43:47,304:INFO:Initializing create_model()
2024-04-26 01:43:47,304:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:43:47,304:INFO:Checking exceptions
2024-04-26 01:43:47,305:INFO:Importing libraries
2024-04-26 01:43:47,305:INFO:Copying training dataset
2024-04-26 01:43:47,313:INFO:Defining folds
2024-04-26 01:43:47,313:INFO:Declaring metric variables
2024-04-26 01:43:47,316:INFO:Importing untrained model
2024-04-26 01:43:47,318:INFO:Quadratic Discriminant Analysis Imported successfully
2024-04-26 01:43:47,323:INFO:Starting cross validation
2024-04-26 01:43:47,324:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:43:47,500:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 01:43:47,506:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 01:43:47,528:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 01:43:47,541:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 01:43:47,542:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 01:43:47,564:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 01:43:47,634:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 01:43:47,647:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:47,665:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:47,684:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:47,698:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:47,719:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 01:43:47,757:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:47,774:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:47,857:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:47,861:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:47,887:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 01:43:47,901:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 01:43:47,963:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:47,973:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:48,090:INFO:Calculating mean and std
2024-04-26 01:43:48,091:INFO:Creating metrics dataframe
2024-04-26 01:43:48,093:INFO:Uploading results into container
2024-04-26 01:43:48,094:INFO:Uploading model into container now
2024-04-26 01:43:48,094:INFO:_master_model_container: 9
2024-04-26 01:43:48,094:INFO:_display_container: 2
2024-04-26 01:43:48,095:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,
store_covariance=False, tol=0.0001)
2024-04-26 01:43:48,095:INFO:create_model() successfully completed......................................
2024-04-26 01:43:48,210:INFO:SubProcess create_model() end ==================================
2024-04-26 01:43:48,210:INFO:Creating metrics dataframe
2024-04-26 01:43:48,217:INFO:Initializing Ada Boost Classifier
2024-04-26 01:43:48,218:INFO:Total runtime is 0.93813773393631 minutes
2024-04-26 01:43:48,220:INFO:SubProcess create_model() called ==================================
2024-04-26 01:43:48,220:INFO:Initializing create_model()
2024-04-26 01:43:48,220:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:43:48,221:INFO:Checking exceptions
2024-04-26 01:43:48,221:INFO:Importing libraries
2024-04-26 01:43:48,221:INFO:Copying training dataset
2024-04-26 01:43:48,228:INFO:Defining folds
2024-04-26 01:43:48,229:INFO:Declaring metric variables
2024-04-26 01:43:48,231:INFO:Importing untrained model
2024-04-26 01:43:48,234:INFO:Ada Boost Classifier Imported successfully
2024-04-26 01:43:48,239:INFO:Starting cross validation
2024-04-26 01:43:48,241:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:43:48,392:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 01:43:48,405:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 01:43:48,411:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 01:43:48,423:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 01:43:48,474:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 01:43:48,481:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 01:43:48,544:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 01:43:48,620:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 01:43:51,928:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:51,990:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:52,017:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:52,025:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:43:52,037:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:52,040:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:52,082:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:52,099:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:52,104:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:52,113:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 01:43:52,128:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 01:43:53,806:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:53,825:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:43:53,941:INFO:Calculating mean and std
2024-04-26 01:43:53,944:INFO:Creating metrics dataframe
2024-04-26 01:43:53,951:INFO:Uploading results into container
2024-04-26 01:43:53,952:INFO:Uploading model into container now
2024-04-26 01:43:53,953:INFO:_master_model_container: 10
2024-04-26 01:43:53,953:INFO:_display_container: 2
2024-04-26 01:43:53,953:INFO:AdaBoostClassifier(algorithm='SAMME.R', estimator=None, learning_rate=1.0,
n_estimators=50, random_state=123)
2024-04-26 01:43:53,954:INFO:create_model() successfully completed......................................
2024-04-26 01:43:54,104:INFO:SubProcess create_model() end ==================================
2024-04-26 01:43:54,105:INFO:Creating metrics dataframe
2024-04-26 01:43:54,112:INFO:Initializing Gradient Boosting Classifier
2024-04-26 01:43:54,112:INFO:Total runtime is 1.0363742510477703 minutes
2024-04-26 01:43:54,114:INFO:SubProcess create_model() called ==================================
2024-04-26 01:43:54,115:INFO:Initializing create_model()
2024-04-26 01:43:54,115:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:43:54,115:INFO:Checking exceptions
2024-04-26 01:43:54,115:INFO:Importing libraries
2024-04-26 01:43:54,115:INFO:Copying training dataset
2024-04-26 01:43:54,123:INFO:Defining folds
2024-04-26 01:43:54,123:INFO:Declaring metric variables
2024-04-26 01:43:54,125:INFO:Importing untrained model
2024-04-26 01:43:54,127:INFO:Gradient Boosting Classifier Imported successfully
2024-04-26 01:43:54,131:INFO:Starting cross validation
2024-04-26 01:43:54,133:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:45:20,971:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:45:21,005:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:45:21,199:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:45:21,204:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:45:21,510:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:45:21,641:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:45:21,801:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:45:22,159:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:15,411:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:15,420:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:15,540:INFO:Calculating mean and std
2024-04-26 01:46:15,543:INFO:Creating metrics dataframe
2024-04-26 01:46:15,550:INFO:Uploading results into container
2024-04-26 01:46:15,550:INFO:Uploading model into container now
2024-04-26 01:46:15,551:INFO:_master_model_container: 11
2024-04-26 01:46:15,551:INFO:_display_container: 2
2024-04-26 01:46:15,552: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)
2024-04-26 01:46:15,552:INFO:create_model() successfully completed......................................
2024-04-26 01:46:15,705:INFO:SubProcess create_model() end ==================================
2024-04-26 01:46:15,705:INFO:Creating metrics dataframe
2024-04-26 01:46:15,714:INFO:Initializing Linear Discriminant Analysis
2024-04-26 01:46:15,714:INFO:Total runtime is 3.396401329835256 minutes
2024-04-26 01:46:15,716:INFO:SubProcess create_model() called ==================================
2024-04-26 01:46:15,716:INFO:Initializing create_model()
2024-04-26 01:46:15,716:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, estimator=lda, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:46:15,716:INFO:Checking exceptions
2024-04-26 01:46:15,716:INFO:Importing libraries
2024-04-26 01:46:15,716:INFO:Copying training dataset
2024-04-26 01:46:15,729:INFO:Defining folds
2024-04-26 01:46:15,729:INFO:Declaring metric variables
2024-04-26 01:46:15,732:INFO:Importing untrained model
2024-04-26 01:46:15,736:INFO:Linear Discriminant Analysis Imported successfully
2024-04-26 01:46:15,742:INFO:Starting cross validation
2024-04-26 01:46:15,744:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:46:16,140:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:16,179:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:16,194:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:16,202:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:16,209:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:16,217:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:16,304:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:16,322:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:16,442:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:16,447:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:16,561:INFO:Calculating mean and std
2024-04-26 01:46:16,562:INFO:Creating metrics dataframe
2024-04-26 01:46:16,564:INFO:Uploading results into container
2024-04-26 01:46:16,565:INFO:Uploading model into container now
2024-04-26 01:46:16,565:INFO:_master_model_container: 12
2024-04-26 01:46:16,565:INFO:_display_container: 2
2024-04-26 01:46:16,566:INFO:LinearDiscriminantAnalysis(covariance_estimator=None, n_components=None,
priors=None, shrinkage=None, solver='svd',
store_covariance=False, tol=0.0001)
2024-04-26 01:46:16,566:INFO:create_model() successfully completed......................................
2024-04-26 01:46:16,643:INFO:SubProcess create_model() end ==================================
2024-04-26 01:46:16,643:INFO:Creating metrics dataframe
2024-04-26 01:46:16,651:INFO:Initializing Extra Trees Classifier
2024-04-26 01:46:16,651:INFO:Total runtime is 3.4120222489039103 minutes
2024-04-26 01:46:16,653:INFO:SubProcess create_model() called ==================================
2024-04-26 01:46:16,653:INFO:Initializing create_model()
2024-04-26 01:46:16,653:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, estimator=et, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:46:16,653:INFO:Checking exceptions
2024-04-26 01:46:16,653:INFO:Importing libraries
2024-04-26 01:46:16,654:INFO:Copying training dataset
2024-04-26 01:46:16,660:INFO:Defining folds
2024-04-26 01:46:16,660:INFO:Declaring metric variables
2024-04-26 01:46:16,662:INFO:Importing untrained model
2024-04-26 01:46:16,664:INFO:Extra Trees Classifier Imported successfully
2024-04-26 01:46:16,669:INFO:Starting cross validation
2024-04-26 01:46:16,670:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:46:19,515:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:19,979:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:20,236:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:20,240:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:20,269:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:20,286:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:20,304:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:20,310:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:21,045:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:21,163:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:46:21,277:INFO:Calculating mean and std
2024-04-26 01:46:21,277:INFO:Creating metrics dataframe
2024-04-26 01:46:21,283:INFO:Uploading results into container
2024-04-26 01:46:21,284:INFO:Uploading model into container now
2024-04-26 01:46:21,285:INFO:_master_model_container: 13
2024-04-26 01:46:21,285:INFO:_display_container: 2
2024-04-26 01:46:21,286:INFO:ExtraTreesClassifier(bootstrap=False, 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)
2024-04-26 01:46:21,286:INFO:create_model() successfully completed......................................
2024-04-26 01:46:21,426:INFO:SubProcess create_model() end ==================================
2024-04-26 01:46:21,426:INFO:Creating metrics dataframe
2024-04-26 01:46:21,434:INFO:Initializing Extreme Gradient Boosting
2024-04-26 01:46:21,435:INFO:Total runtime is 3.491747963428497 minutes
2024-04-26 01:46:21,437:INFO:SubProcess create_model() called ==================================
2024-04-26 01:46:21,437:INFO:Initializing create_model()
2024-04-26 01:46:21,437:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, estimator=xgboost, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:46:21,437:INFO:Checking exceptions
2024-04-26 01:46:21,437:INFO:Importing libraries
2024-04-26 01:46:21,438:INFO:Copying training dataset
2024-04-26 01:46:21,450:INFO:Defining folds
2024-04-26 01:46:21,450:INFO:Declaring metric variables
2024-04-26 01:46:21,452:INFO:Importing untrained model
2024-04-26 01:46:21,455:INFO:Extreme Gradient Boosting Imported successfully
2024-04-26 01:46:21,459:INFO:Starting cross validation
2024-04-26 01:46:21,461:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:47:29,393:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:47:29,571:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:47:29,792:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:47:30,229:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:47:30,402:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:47:30,403:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:47:30,487:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:47:30,770:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:48:03,307:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:48:03,745:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:48:03,881:INFO:Calculating mean and std
2024-04-26 01:48:03,888:INFO:Creating metrics dataframe
2024-04-26 01:48:03,905:INFO:Uploading results into container
2024-04-26 01:48:03,907:INFO:Uploading model into container now
2024-04-26 01:48:03,909:INFO:_master_model_container: 14
2024-04-26 01:48:03,909:INFO:_display_container: 2
2024-04-26 01:48:03,913:INFO:XGBClassifier(base_score=None, booster='gbtree', callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=100, n_jobs=-1, num_parallel_tree=None,
objective='binary:logistic', predictor=None, ...)
2024-04-26 01:48:03,913:INFO:create_model() successfully completed......................................
2024-04-26 01:48:04,715:INFO:SubProcess create_model() end ==================================
2024-04-26 01:48:04,716:INFO:Creating metrics dataframe
2024-04-26 01:48:04,731:INFO:Initializing Light Gradient Boosting Machine
2024-04-26 01:48:04,731:INFO:Total runtime is 5.213358247280121 minutes
2024-04-26 01:48:04,736:INFO:SubProcess create_model() called ==================================
2024-04-26 01:48:04,737:INFO:Initializing create_model()
2024-04-26 01:48:04,737:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, estimator=lightgbm, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:48:04,737:INFO:Checking exceptions
2024-04-26 01:48:04,737:INFO:Importing libraries
2024-04-26 01:48:04,737:INFO:Copying training dataset
2024-04-26 01:48:04,754:INFO:Defining folds
2024-04-26 01:48:04,754:INFO:Declaring metric variables
2024-04-26 01:48:04,758:INFO:Importing untrained model
2024-04-26 01:48:04,785:INFO:Light Gradient Boosting Machine Imported successfully
2024-04-26 01:48:04,800:INFO:Starting cross validation
2024-04-26 01:48:04,806:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:50:48,830:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:50:49,112:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:50:49,297:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:50:49,309:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:50:49,309:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:50:49,606:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:50:49,713:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:50:55,999:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:51:21,200:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:51:21,313:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:51:21,479:INFO:Calculating mean and std
2024-04-26 01:51:21,613:INFO:Creating metrics dataframe
2024-04-26 01:51:21,664:INFO:Uploading results into container
2024-04-26 01:51:21,674:INFO:Uploading model into container now
2024-04-26 01:51:21,675:INFO:_master_model_container: 15
2024-04-26 01:51:21,676:INFO:_display_container: 2
2024-04-26 01:51:21,682:INFO:LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,
importance_type='split', learning_rate=0.1, max_depth=-1,
min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,
random_state=123, reg_alpha=0.0, reg_lambda=0.0, subsample=1.0,
subsample_for_bin=200000, subsample_freq=0)
2024-04-26 01:51:21,682:INFO:create_model() successfully completed......................................
2024-04-26 01:51:23,165:INFO:SubProcess create_model() end ==================================
2024-04-26 01:51:23,165:INFO:Creating metrics dataframe
2024-04-26 01:51:23,213:INFO:Initializing CatBoost Classifier
2024-04-26 01:51:23,213:INFO:Total runtime is 8.521386484305065 minutes
2024-04-26 01:51:23,216:INFO:SubProcess create_model() called ==================================
2024-04-26 01:51:23,216:INFO:Initializing create_model()
2024-04-26 01:51:23,216:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, estimator=catboost, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:51:23,217:INFO:Checking exceptions
2024-04-26 01:51:23,217:INFO:Importing libraries
2024-04-26 01:51:23,225:INFO:Copying training dataset
2024-04-26 01:51:23,250:INFO:Defining folds
2024-04-26 01:51:23,250:INFO:Declaring metric variables
2024-04-26 01:51:23,253:INFO:Importing untrained model
2024-04-26 01:51:23,301:INFO:CatBoost Classifier Imported successfully
2024-04-26 01:51:23,306:INFO:Starting cross validation
2024-04-26 01:51:23,307:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:54:33,594:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:54:35,005:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:54:35,793:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:54:36,140:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:54:36,518:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:54:36,770:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:54:37,685:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:54:39,630:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:55:35,584:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:55:36,078:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:55:36,206:INFO:Calculating mean and std
2024-04-26 01:55:36,227:INFO:Creating metrics dataframe
2024-04-26 01:55:36,241:INFO:Uploading results into container
2024-04-26 01:55:36,242:INFO:Uploading model into container now
2024-04-26 01:55:36,243:INFO:_master_model_container: 16
2024-04-26 01:55:36,243:INFO:_display_container: 2
2024-04-26 01:55:36,243:INFO:<catboost.core.CatBoostClassifier object at 0x28c5622c0>
2024-04-26 01:55:36,243:INFO:create_model() successfully completed......................................
2024-04-26 01:55:36,604:INFO:SubProcess create_model() end ==================================
2024-04-26 01:55:36,605:INFO:Creating metrics dataframe
2024-04-26 01:55:36,646:INFO:Initializing Dummy Classifier
2024-04-26 01:55:36,646:INFO:Total runtime is 12.745269334316255 minutes
2024-04-26 01:55:36,649:INFO:SubProcess create_model() called ==================================
2024-04-26 01:55:36,649:INFO:Initializing create_model()
2024-04-26 01:55:36,649:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, estimator=dummy, 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 0x28a879720>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:55:36,649:INFO:Checking exceptions
2024-04-26 01:55:36,650:INFO:Importing libraries
2024-04-26 01:55:36,651:INFO:Copying training dataset
2024-04-26 01:55:36,669:INFO:Defining folds
2024-04-26 01:55:36,669:INFO:Declaring metric variables
2024-04-26 01:55:36,672:INFO:Importing untrained model
2024-04-26 01:55:36,676:INFO:Dummy Classifier Imported successfully
2024-04-26 01:55:36,682:INFO:Starting cross validation
2024-04-26 01:55:36,684:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 01:55:37,106:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:55:37,130:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:55:37,268:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:55:37,285:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:55:37,339:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:55:37,369:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:55:37,382:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:55:37,384:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:55:37,421:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:55:37,441:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:55:37,469:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:55:37,487:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:55:37,735:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:55:37,745:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:55:37,805:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:55:37,826:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:55:37,835:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:55:37,846:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:55:38,127:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 01:55:38,135:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 01:55:38,160:INFO:Calculating mean and std
2024-04-26 01:55:38,182:INFO:Creating metrics dataframe
2024-04-26 01:55:38,215:INFO:Uploading results into container
2024-04-26 01:55:38,217:INFO:Uploading model into container now
2024-04-26 01:55:38,220:INFO:_master_model_container: 17
2024-04-26 01:55:38,220:INFO:_display_container: 2
2024-04-26 01:55:38,221:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-26 01:55:38,221:INFO:create_model() successfully completed......................................
2024-04-26 01:55:39,498:INFO:SubProcess create_model() end ==================================
2024-04-26 01:55:39,499:INFO:Creating metrics dataframe
2024-04-26 01:55:39,522:INFO:Initializing create_model()
2024-04-26 01:55:39,523:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, estimator=<catboost.core.CatBoostClassifier object at 0x28c5622c0>, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 01:55:39,523:INFO:Checking exceptions
2024-04-26 01:55:39,525:INFO:Importing libraries
2024-04-26 01:55:39,526:INFO:Copying training dataset
2024-04-26 01:55:39,536:INFO:Defining folds
2024-04-26 01:55:39,537:INFO:Declaring metric variables
2024-04-26 01:55:39,537:INFO:Importing untrained model
2024-04-26 01:55:39,537:INFO:Declaring custom model
2024-04-26 01:55:39,539:INFO:CatBoost Classifier Imported successfully
2024-04-26 01:55:39,540:INFO:Cross validation set to False
2024-04-26 01:55:39,540:INFO:Fitting Model
2024-04-26 01:56:14,464:INFO:<catboost.core.CatBoostClassifier object at 0x289f4c070>
2024-04-26 01:56:14,465:INFO:create_model() successfully completed......................................
2024-04-26 01:56:14,661:INFO:_master_model_container: 17
2024-04-26 01:56:14,661:INFO:_display_container: 2
2024-04-26 01:56:14,661:INFO:<catboost.core.CatBoostClassifier object at 0x289f4c070>
2024-04-26 01:56:14,662:INFO:compare_models() successfully completed......................................
2024-04-26 01:56:14,663:INFO:Initializing tune_model()
2024-04-26 01:56:14,663:INFO:tune_model(estimator=<catboost.core.CatBoostClassifier object at 0x289f4c070>, fold=None, round=4, n_iter=10, custom_grid=None, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}, self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>)
2024-04-26 01:56:14,663:INFO:Checking exceptions
2024-04-26 01:56:14,677:INFO:Copying training dataset
2024-04-26 01:56:14,684:INFO:Checking base model
2024-04-26 01:56:14,684:INFO:Base model : CatBoost Classifier
2024-04-26 01:56:14,686:INFO:Declaring metric variables
2024-04-26 01:56:14,688:INFO:Defining Hyperparameters
2024-04-26 01:56:14,776:INFO:Tuning with n_jobs=-1
2024-04-26 01:56:14,776:INFO:Initializing RandomizedSearchCV
2024-04-26 02:01:05,487:INFO:best_params: {'actual_estimator__random_strength': 0.2, 'actual_estimator__n_estimators': 180, 'actual_estimator__l2_leaf_reg': 30, 'actual_estimator__eta': 0.4, 'actual_estimator__depth': 8}
2024-04-26 02:01:05,523:INFO:Hyperparameter search completed
2024-04-26 02:01:05,523:INFO:SubProcess create_model() called ==================================
2024-04-26 02:01:05,531:INFO:Initializing create_model()
2024-04-26 02:01:05,532:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, estimator=<catboost.core.CatBoostClassifier object at 0x28a99fe50>, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x287e0bb50>, model_only=True, return_train_score=False, error_score=0.0, kwargs={'random_strength': 0.2, 'n_estimators': 180, 'l2_leaf_reg': 30, 'eta': 0.4, 'depth': 8})
2024-04-26 02:01:05,532:INFO:Checking exceptions
2024-04-26 02:01:05,534:INFO:Importing libraries
2024-04-26 02:01:05,537:INFO:Copying training dataset
2024-04-26 02:01:05,597:INFO:Defining folds
2024-04-26 02:01:05,597:INFO:Declaring metric variables
2024-04-26 02:01:05,625:INFO:Importing untrained model
2024-04-26 02:01:05,625:INFO:Declaring custom model
2024-04-26 02:01:05,635:INFO:CatBoost Classifier Imported successfully
2024-04-26 02:01:05,641:INFO:Starting cross validation
2024-04-26 02:01:05,647:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:03:31,794:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:03:33,301:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:03:33,857:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:03:34,157:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:03:34,560:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:03:35,525:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:03:36,301:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:03:36,337:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:04:04,829:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:04:04,860:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:04:04,991:INFO:Calculating mean and std
2024-04-26 02:04:05,034:INFO:Creating metrics dataframe
2024-04-26 02:04:05,064:INFO:Finalizing model
2024-04-26 02:04:21,625:INFO:Uploading results into container
2024-04-26 02:04:21,631:INFO:Uploading model into container now
2024-04-26 02:04:21,643:INFO:_master_model_container: 18
2024-04-26 02:04:21,644:INFO:_display_container: 3
2024-04-26 02:04:21,644:INFO:<catboost.core.CatBoostClassifier object at 0x289c40a00>
2024-04-26 02:04:21,644:INFO:create_model() successfully completed......................................
2024-04-26 02:04:22,259:INFO:SubProcess create_model() end ==================================
2024-04-26 02:04:22,260:INFO:choose_better activated
2024-04-26 02:04:22,267:INFO:SubProcess create_model() called ==================================
2024-04-26 02:04:22,268:INFO:Initializing create_model()
2024-04-26 02:04:22,268:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, estimator=<catboost.core.CatBoostClassifier object at 0x289f4c070>, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:04:22,268:INFO:Checking exceptions
2024-04-26 02:04:22,270:INFO:Importing libraries
2024-04-26 02:04:22,271:INFO:Copying training dataset
2024-04-26 02:04:22,291:INFO:Defining folds
2024-04-26 02:04:22,292:INFO:Declaring metric variables
2024-04-26 02:04:22,292:INFO:Importing untrained model
2024-04-26 02:04:22,292:INFO:Declaring custom model
2024-04-26 02:04:22,292:INFO:CatBoost Classifier Imported successfully
2024-04-26 02:04:22,292:INFO:Starting cross validation
2024-04-26 02:04:22,294:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:07:57,400:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:07:57,920:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:07:58,287:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:07:59,053:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:07:59,117:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:07:59,561:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:07:59,580:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:08:00,015:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:08:41,152:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:08:41,557:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:08:41,680:INFO:Calculating mean and std
2024-04-26 02:08:41,681:INFO:Creating metrics dataframe
2024-04-26 02:08:41,687:INFO:Finalizing model
2024-04-26 02:09:02,359:INFO:Uploading results into container
2024-04-26 02:09:02,360:INFO:Uploading model into container now
2024-04-26 02:09:02,361:INFO:_master_model_container: 19
2024-04-26 02:09:02,361:INFO:_display_container: 4
2024-04-26 02:09:02,361:INFO:<catboost.core.CatBoostClassifier object at 0x28c582950>
2024-04-26 02:09:02,361:INFO:create_model() successfully completed......................................
2024-04-26 02:09:02,596:INFO:SubProcess create_model() end ==================================
2024-04-26 02:09:02,597:INFO:<catboost.core.CatBoostClassifier object at 0x28c582950> result for Accuracy is 0.9065
2024-04-26 02:09:02,597:INFO:<catboost.core.CatBoostClassifier object at 0x289c40a00> result for Accuracy is 0.8994
2024-04-26 02:09:02,597:INFO:<catboost.core.CatBoostClassifier object at 0x28c582950> is best model
2024-04-26 02:09:02,597:INFO:choose_better completed
2024-04-26 02:09:02,601:INFO:Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).
2024-04-26 02:09:02,624:INFO:_master_model_container: 19
2024-04-26 02:09:02,624:INFO:_display_container: 3
2024-04-26 02:09:02,624:INFO:<catboost.core.CatBoostClassifier object at 0x28c582950>
2024-04-26 02:09:02,624:INFO:tune_model() successfully completed......................................
2024-04-26 02:09:02,712:INFO:Initializing finalize_model()
2024-04-26 02:09:02,712:INFO:finalize_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, estimator=<catboost.core.CatBoostClassifier object at 0x28c582950>, fit_kwargs=None, groups=None, model_only=False, experiment_custom_tags=None)
2024-04-26 02:09:02,713:INFO:Finalizing <catboost.core.CatBoostClassifier object at 0x28c582950>
2024-04-26 02:09:02,722:INFO:Initializing create_model()
2024-04-26 02:09:02,722:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2844e0250>, estimator=<catboost.core.CatBoostClassifier object at 0x28c582950>, fold=None, round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=False, metrics=None, display=None, model_only=False, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:09:02,722:INFO:Checking exceptions
2024-04-26 02:09:02,723:INFO:Importing libraries
2024-04-26 02:09:02,723:INFO:Copying training dataset
2024-04-26 02:09:02,723:INFO:Defining folds
2024-04-26 02:09:02,723:INFO:Declaring metric variables
2024-04-26 02:09:02,723:INFO:Importing untrained model
2024-04-26 02:09:02,724:INFO:Declaring custom model
2024-04-26 02:09:02,724:INFO:CatBoost Classifier Imported successfully
2024-04-26 02:09:02,725:INFO:Cross validation set to False
2024-04-26 02:09:02,725:INFO:Fitting Model
2024-04-26 02:09:23,626:INFO:Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'MTRANS_Public_Transportation',
'MTRANS_Walking', 'BMI', 'Ag...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x28a99de40>)],
verbose=False)
2024-04-26 02:09:23,626:INFO:create_model() successfully completed......................................
2024-04-26 02:09:23,753:INFO:_master_model_container: 19
2024-04-26 02:09:23,753:INFO:_display_container: 3
2024-04-26 02:09:23,757:INFO:Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'MTRANS_Public_Transportation',
'MTRANS_Walking', 'BMI', 'Ag...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x28a99de40>)],
verbose=False)
2024-04-26 02:09:23,757:INFO:finalize_model() successfully completed......................................
2024-04-26 02:09:23,835:INFO:Initializing save_model()
2024-04-26 02:09:23,835:INFO:save_model(model=Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'MTRANS_Public_Transportation',
'MTRANS_Walking', 'BMI', 'Ag...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x28a99de40>)],
verbose=False), model_name=model_name, prep_pipe_=Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'M...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False), verbose=True, use_case=MLUsecase.CLASSIFICATION, kwargs={})
2024-04-26 02:09:23,835:INFO:Adding model into prep_pipe
2024-04-26 02:09:23,835:WARNING:Only Model saved as it was a pipeline.
2024-04-26 02:09:23,842:INFO:model_name.pkl saved in current working directory
2024-04-26 02:09:23,847:INFO:Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'MTRANS_Public_Transportation',
'MTRANS_Walking', 'BMI', 'Ag...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x28a99de40>)],
verbose=False)
2024-04-26 02:09:23,847:INFO:save_model() successfully completed......................................
2024-04-26 02:11:21,083:INFO:PyCaret ClassificationExperiment
2024-04-26 02:11:21,083:INFO:Logging name: clf-default-name
2024-04-26 02:11:21,083:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2024-04-26 02:11:21,083:INFO:version 3.3.0
2024-04-26 02:11:21,083:INFO:Initializing setup()
2024-04-26 02:11:21,083:INFO:self.USI: cc89
2024-04-26 02:11:21,084:INFO:self._variable_keys: {'X', 'html_param', 'X_train', 'pipeline', 'fold_generator', 'fold_groups_param', 'idx', 'y', 'target_param', 'exp_name_log', '_ml_usecase', '_available_plots', 'is_multiclass', 'USI', 'log_plots_param', 'data', 'fold_shuffle_param', 'logging_param', 'seed', 'memory', 'y_train', 'gpu_n_jobs_param', 'fix_imbalance', 'y_test', 'n_jobs_param', 'gpu_param', 'X_test', 'exp_id'}
2024-04-26 02:11:21,084:INFO:Checking environment
2024-04-26 02:11:21,084:INFO:python_version: 3.10.13
2024-04-26 02:11:21,084:INFO:python_build: ('main', 'Sep 11 2023 08:16:02')
2024-04-26 02:11:21,084:INFO:machine: arm64
2024-04-26 02:11:21,084:INFO:platform: macOS-14.0-arm64-arm-64bit
2024-04-26 02:11:21,085:INFO:Memory: svmem(total=8589934592, available=1552547840, percent=81.9, used=3090677760, free=35307520, active=1527463936, inactive=1394950144, wired=1563213824)
2024-04-26 02:11:21,085:INFO:Physical Core: 8
2024-04-26 02:11:21,085:INFO:Logical Core: 8
2024-04-26 02:11:21,085:INFO:Checking libraries
2024-04-26 02:11:21,085:INFO:System:
2024-04-26 02:11:21,085:INFO: python: 3.10.13 (main, Sep 11 2023, 08:16:02) [Clang 14.0.6 ]
2024-04-26 02:11:21,085:INFO:executable: /Users/arham/anaconda3/envs/DataScience/bin/python
2024-04-26 02:11:21,085:INFO: machine: macOS-14.0-arm64-arm-64bit
2024-04-26 02:11:21,085:INFO:PyCaret required dependencies:
2024-04-26 02:11:21,085:INFO: pip: 23.3
2024-04-26 02:11:21,085:INFO: setuptools: 60.2.0
2024-04-26 02:11:21,085:INFO: pycaret: 3.3.0
2024-04-26 02:11:21,085:INFO: IPython: 8.15.0
2024-04-26 02:11:21,085:INFO: ipywidgets: 8.0.4
2024-04-26 02:11:21,085:INFO: tqdm: 4.65.2
2024-04-26 02:11:21,085:INFO: numpy: 1.25.2
2024-04-26 02:11:21,085:INFO: pandas: 1.5.3
2024-04-26 02:11:21,085:INFO: jinja2: 3.1.3
2024-04-26 02:11:21,085:INFO: scipy: 1.11.4
2024-04-26 02:11:21,085:INFO: joblib: 1.2.0
2024-04-26 02:11:21,085:INFO: sklearn: 1.4.0
2024-04-26 02:11:21,085:INFO: pyod: 1.1.3
2024-04-26 02:11:21,085:INFO: imblearn: 0.12.2
2024-04-26 02:11:21,085:INFO: category_encoders: 2.6.3
2024-04-26 02:11:21,085:INFO: lightgbm: 4.1.0
2024-04-26 02:11:21,085:INFO: numba: 0.58.1
2024-04-26 02:11:21,085:INFO: requests: 2.31.0
2024-04-26 02:11:21,085:INFO: matplotlib: 3.8.4
2024-04-26 02:11:21,085:INFO: scikitplot: 0.3.7
2024-04-26 02:11:21,085:INFO: yellowbrick: 1.5
2024-04-26 02:11:21,085:INFO: plotly: 5.17.0
2024-04-26 02:11:21,085:INFO: plotly-resampler: Not installed
2024-04-26 02:11:21,085:INFO: kaleido: 0.2.1
2024-04-26 02:11:21,085:INFO: schemdraw: 0.15
2024-04-26 02:11:21,086:INFO: statsmodels: 0.13.5
2024-04-26 02:11:21,086:INFO: sktime: 0.26.1
2024-04-26 02:11:21,086:INFO: tbats: 1.1.3
2024-04-26 02:11:21,086:INFO: pmdarima: 2.0.4
2024-04-26 02:11:21,086:INFO: psutil: 5.9.0
2024-04-26 02:11:21,086:INFO: markupsafe: 2.1.1
2024-04-26 02:11:21,086:INFO: pickle5: Not installed
2024-04-26 02:11:21,086:INFO: cloudpickle: 2.2.1
2024-04-26 02:11:21,086:INFO: deprecation: 2.1.0
2024-04-26 02:11:21,086:INFO: xxhash: 3.4.1
2024-04-26 02:11:21,086:INFO: wurlitzer: 3.0.2
2024-04-26 02:11:21,086:INFO:PyCaret optional dependencies:
2024-04-26 02:11:21,086:INFO: shap: 0.44.0
2024-04-26 02:11:21,086:INFO: interpret: Not installed
2024-04-26 02:11:21,086:INFO: umap: Not installed
2024-04-26 02:11:21,086:INFO: ydata_profiling: 0.0.dev0
2024-04-26 02:11:21,086:INFO: explainerdashboard: Not installed
2024-04-26 02:11:21,086:INFO: autoviz: Not installed
2024-04-26 02:11:21,086:INFO: fairlearn: Not installed
2024-04-26 02:11:21,086:INFO: deepchecks: Not installed
2024-04-26 02:11:21,086:INFO: xgboost: 1.7.3
2024-04-26 02:11:21,086:INFO: catboost: 1.1.1
2024-04-26 02:11:21,086:INFO: kmodes: Not installed
2024-04-26 02:11:21,086:INFO: mlxtend: Not installed
2024-04-26 02:11:21,086:INFO: statsforecast: 1.4.0
2024-04-26 02:11:21,086:INFO: tune_sklearn: Not installed
2024-04-26 02:11:21,086:INFO: ray: 2.10.0
2024-04-26 02:11:21,086:INFO: hyperopt: 0.2.7
2024-04-26 02:11:21,086:INFO: optuna: 3.5.0
2024-04-26 02:11:21,086:INFO: skopt: Not installed
2024-04-26 02:11:21,086:INFO: mlflow: 2.10.2
2024-04-26 02:11:21,086:INFO: gradio: 3.48.0
2024-04-26 02:11:21,086:INFO: fastapi: 0.109.2
2024-04-26 02:11:21,086:INFO: uvicorn: 0.27.1
2024-04-26 02:11:21,086:INFO: m2cgen: Not installed
2024-04-26 02:11:21,086:INFO: evidently: Not installed
2024-04-26 02:11:21,086:INFO: fugue: Not installed
2024-04-26 02:11:21,086:INFO: streamlit: 1.27.2
2024-04-26 02:11:21,086:INFO: prophet: Not installed
2024-04-26 02:11:21,086:INFO:None
2024-04-26 02:11:21,086:INFO:Set up data.
2024-04-26 02:11:21,101:INFO:Set up folding strategy.
2024-04-26 02:11:21,101:INFO:Set up train/test split.
2024-04-26 02:11:21,112:INFO:Set up index.
2024-04-26 02:11:21,112:INFO:Assigning column types.
2024-04-26 02:11:21,116:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2024-04-26 02:11:21,145:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-26 02:11:21,146:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:11:21,164:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:11:21,166:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:11:21,198:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-26 02:11:21,198:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:11:21,216:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:11:21,218:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:11:21,218:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2024-04-26 02:11:21,247:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:11:21,265:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:11:21,267:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:11:21,296:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:11:21,314:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:11:21,316:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:11:21,316:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2024-04-26 02:11:21,363:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:11:21,365:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:11:21,414:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:11:21,415:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:11:21,416:INFO:Preparing preprocessing pipeline...
2024-04-26 02:11:21,419:INFO:Set up simple imputation.
2024-04-26 02:11:21,421:INFO:Set up encoding of categorical features.
2024-04-26 02:11:21,421:INFO:Set up column name cleaning.
2024-04-26 02:11:21,470:INFO:Finished creating preprocessing pipeline.
2024-04-26 02:11:21,474:INFO:Pipeline: Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'M...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False)
2024-04-26 02:11:21,474:INFO:Creating final display dataframe.
2024-04-26 02:11:21,597:INFO:Setup _display_container: Description Value
0 Session id 123
1 Target NObeyesdad
2 Target type Multiclass
3 Original data shape (10793, 35)
4 Transformed data shape (10793, 39)
5 Transformed train set shape (7555, 39)
6 Transformed test set shape (3238, 39)
7 Numeric features 33
8 Categorical features 1
9 Preprocess True
10 Imputation type simple
11 Numeric imputation mean
12 Categorical imputation mode
13 Maximum one-hot encoding 25
14 Encoding method None
15 Fold Generator StratifiedKFold
16 Fold Number 10
17 CPU Jobs -1
18 Use GPU False
19 Log Experiment False
20 Experiment Name clf-default-name
21 USI cc89
2024-04-26 02:11:21,651:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:11:21,653:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:11:21,701:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:11:21,702:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:11:21,703:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour.
warnings.warn(
2024-04-26 02:11:21,704:INFO:setup() successfully completed in 0.62s...............
2024-04-26 02:11:21,705:INFO:Initializing compare_models()
2024-04-26 02:11:21,705:INFO:compare_models(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, 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 0x289b52b00>, '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)
2024-04-26 02:11:21,705:INFO:Checking exceptions
2024-04-26 02:11:21,709:INFO:Preparing display monitor
2024-04-26 02:11:21,722:INFO:Initializing Logistic Regression
2024-04-26 02:11:21,722:INFO:Total runtime is 3.735224405924479e-06 minutes
2024-04-26 02:11:21,724:INFO:SubProcess create_model() called ==================================
2024-04-26 02:11:21,725:INFO:Initializing create_model()
2024-04-26 02:11:21,725:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:11:21,725:INFO:Checking exceptions
2024-04-26 02:11:21,725:INFO:Importing libraries
2024-04-26 02:11:21,725:INFO:Copying training dataset
2024-04-26 02:11:21,732:INFO:Defining folds
2024-04-26 02:11:21,732:INFO:Declaring metric variables
2024-04-26 02:11:21,734:INFO:Importing untrained model
2024-04-26 02:11:21,736:INFO:Logistic Regression Imported successfully
2024-04-26 02:11:21,740:INFO:Starting cross validation
2024-04-26 02:11:21,741:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:11:25,886:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 02:11:25,981:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:26,013:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 02:11:26,018:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 02:11:26,052:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 02:11:26,059:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 02:11:26,066:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:26,069:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 02:11:26,096:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:26,108:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 02:11:26,111:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:26,123:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:26,126:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:26,152:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:26,153:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 02:11:26,184:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:27,822:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 02:11:27,824:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: 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(
2024-04-26 02:11:27,841:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:27,845:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:27,854:INFO:Calculating mean and std
2024-04-26 02:11:27,856:INFO:Creating metrics dataframe
2024-04-26 02:11:27,861:INFO:Uploading results into container
2024-04-26 02:11:27,862:INFO:Uploading model into container now
2024-04-26 02:11:27,862:INFO:_master_model_container: 1
2024-04-26 02:11:27,863:INFO:_display_container: 2
2024-04-26 02:11:27,864: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)
2024-04-26 02:11:27,864:INFO:create_model() successfully completed......................................
2024-04-26 02:11:28,039:INFO:SubProcess create_model() end ==================================
2024-04-26 02:11:28,039:INFO:Creating metrics dataframe
2024-04-26 02:11:28,044:INFO:Initializing K Neighbors Classifier
2024-04-26 02:11:28,044:INFO:Total runtime is 0.10536923408508302 minutes
2024-04-26 02:11:28,046:INFO:SubProcess create_model() called ==================================
2024-04-26 02:11:28,047:INFO:Initializing create_model()
2024-04-26 02:11:28,047:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:11:28,047:INFO:Checking exceptions
2024-04-26 02:11:28,047:INFO:Importing libraries
2024-04-26 02:11:28,047:INFO:Copying training dataset
2024-04-26 02:11:28,052:INFO:Defining folds
2024-04-26 02:11:28,052:INFO:Declaring metric variables
2024-04-26 02:11:28,054:INFO:Importing untrained model
2024-04-26 02:11:28,056:INFO:K Neighbors Classifier Imported successfully
2024-04-26 02:11:28,060:INFO:Starting cross validation
2024-04-26 02:11:28,061:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:11:28,190:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,190:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,190:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,200:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,208:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,208:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,208:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,209:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,220:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,220:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,226:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,237:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,246:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,260:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,263:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,279:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,304:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,304:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,317:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,318:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,319:INFO:Calculating mean and std
2024-04-26 02:11:28,321:INFO:Creating metrics dataframe
2024-04-26 02:11:28,324:INFO:Uploading results into container
2024-04-26 02:11:28,325:INFO:Uploading model into container now
2024-04-26 02:11:28,325:INFO:_master_model_container: 2
2024-04-26 02:11:28,325:INFO:_display_container: 2
2024-04-26 02:11:28,326:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform')
2024-04-26 02:11:28,326:INFO:create_model() successfully completed......................................
2024-04-26 02:11:28,483:WARNING:create_model() for KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform') raised an exception or returned all 0.0, trying without fit_kwargs:
2024-04-26 02:11:28,483:WARNING:Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py", line 797, in compare_models
np.sum(
AssertionError
2024-04-26 02:11:28,483:INFO:Initializing create_model()
2024-04-26 02:11:28,483:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:11:28,483:INFO:Checking exceptions
2024-04-26 02:11:28,483:INFO:Importing libraries
2024-04-26 02:11:28,483:INFO:Copying training dataset
2024-04-26 02:11:28,489:INFO:Defining folds
2024-04-26 02:11:28,489:INFO:Declaring metric variables
2024-04-26 02:11:28,491:INFO:Importing untrained model
2024-04-26 02:11:28,494:INFO:K Neighbors Classifier Imported successfully
2024-04-26 02:11:28,498:INFO:Starting cross validation
2024-04-26 02:11:28,499:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:11:28,588:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,599:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,606:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,606:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,624:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,628:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,630:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,638:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,638:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,661:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,662:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,666:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,667:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,684:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,702:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,719:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,731:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,748:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,771:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:28,788:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:1006: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.0. Details:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 137, in __call__
score = scorer._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 328, in predict
y = self.steps[-1][-1].predict(X, **params)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_classification.py", line 271, in predict
neigh_ind = self.kneighbors(X, return_distance=False)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/neighbors/_base.py", line 850, in kneighbors
results = ArgKmin.compute(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 267, in compute
return ArgKmin64.compute(
File "sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx", line 90, in sklearn.metrics._pairwise_distances_reduction._argkmin.ArgKmin64.compute
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/fixes.py", line 94, in threadpool_limits
return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 171, in __init__
self._original_info = self._set_threadpool_limits()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 268, in _set_threadpool_limits
modules = _ThreadpoolInfo(prefixes=self._prefixes,
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 340, in __init__
self._load_modules()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 371, in _load_modules
self._find_modules_with_dyld()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 428, in _find_modules_with_dyld
self._make_module_from_path(filepath)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
module = module_class(filepath, prefix, user_api, internal_api)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 606, in __init__
self.version = self.get_version()
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/threadpoolctl.py", line 646, in get_version
config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'
warnings.warn(
2024-04-26 02:11:28,790:INFO:Calculating mean and std
2024-04-26 02:11:28,792:INFO:Creating metrics dataframe
2024-04-26 02:11:28,795:INFO:Uploading results into container
2024-04-26 02:11:28,796:INFO:Uploading model into container now
2024-04-26 02:11:28,796:INFO:_master_model_container: 3
2024-04-26 02:11:28,797:INFO:_display_container: 2
2024-04-26 02:11:28,797:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform')
2024-04-26 02:11:28,797:INFO:create_model() successfully completed......................................
2024-04-26 02:11:28,921:ERROR:create_model() for KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform') raised an exception or returned all 0.0:
2024-04-26 02:11:28,921:ERROR:Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py", line 797, in compare_models
np.sum(
AssertionError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py", line 818, in compare_models
np.sum(
AssertionError
2024-04-26 02:11:28,921:INFO:Initializing Naive Bayes
2024-04-26 02:11:28,921:INFO:Total runtime is 0.11998396714528403 minutes
2024-04-26 02:11:28,924:INFO:SubProcess create_model() called ==================================
2024-04-26 02:11:28,924:INFO:Initializing create_model()
2024-04-26 02:11:28,924:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:11:28,924:INFO:Checking exceptions
2024-04-26 02:11:28,924:INFO:Importing libraries
2024-04-26 02:11:28,924:INFO:Copying training dataset
2024-04-26 02:11:28,930:INFO:Defining folds
2024-04-26 02:11:28,930:INFO:Declaring metric variables
2024-04-26 02:11:28,932:INFO:Importing untrained model
2024-04-26 02:11:28,935:INFO:Naive Bayes Imported successfully
2024-04-26 02:11:28,939:INFO:Starting cross validation
2024-04-26 02:11:28,940:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:11:29,032:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,033:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,047:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,047:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,053:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,072:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,079:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,097:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,122:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,132:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,142:INFO:Calculating mean and std
2024-04-26 02:11:29,143:INFO:Creating metrics dataframe
2024-04-26 02:11:29,147:INFO:Uploading results into container
2024-04-26 02:11:29,147:INFO:Uploading model into container now
2024-04-26 02:11:29,148:INFO:_master_model_container: 4
2024-04-26 02:11:29,148:INFO:_display_container: 2
2024-04-26 02:11:29,148:INFO:GaussianNB(priors=None, var_smoothing=1e-09)
2024-04-26 02:11:29,148:INFO:create_model() successfully completed......................................
2024-04-26 02:11:29,260:INFO:SubProcess create_model() end ==================================
2024-04-26 02:11:29,260:INFO:Creating metrics dataframe
2024-04-26 02:11:29,267:INFO:Initializing Decision Tree Classifier
2024-04-26 02:11:29,268:INFO:Total runtime is 0.12575608491897586 minutes
2024-04-26 02:11:29,270:INFO:SubProcess create_model() called ==================================
2024-04-26 02:11:29,270:INFO:Initializing create_model()
2024-04-26 02:11:29,270:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:11:29,270:INFO:Checking exceptions
2024-04-26 02:11:29,271:INFO:Importing libraries
2024-04-26 02:11:29,271:INFO:Copying training dataset
2024-04-26 02:11:29,276:INFO:Defining folds
2024-04-26 02:11:29,276:INFO:Declaring metric variables
2024-04-26 02:11:29,278:INFO:Importing untrained model
2024-04-26 02:11:29,281:INFO:Decision Tree Classifier Imported successfully
2024-04-26 02:11:29,286:INFO:Starting cross validation
2024-04-26 02:11:29,287:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:11:29,637:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,662:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,664:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,668:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,676:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,701:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,706:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,740:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,932:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,936:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:29,943:INFO:Calculating mean and std
2024-04-26 02:11:29,944:INFO:Creating metrics dataframe
2024-04-26 02:11:29,946:INFO:Uploading results into container
2024-04-26 02:11:29,946:INFO:Uploading model into container now
2024-04-26 02:11:29,947:INFO:_master_model_container: 5
2024-04-26 02:11:29,947:INFO:_display_container: 2
2024-04-26 02:11:29,947: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')
2024-04-26 02:11:29,947:INFO:create_model() successfully completed......................................
2024-04-26 02:11:30,032:INFO:SubProcess create_model() end ==================================
2024-04-26 02:11:30,033:INFO:Creating metrics dataframe
2024-04-26 02:11:30,039:INFO:Initializing SVM - Linear Kernel
2024-04-26 02:11:30,039:INFO:Total runtime is 0.13861081600189212 minutes
2024-04-26 02:11:30,041:INFO:SubProcess create_model() called ==================================
2024-04-26 02:11:30,041:INFO:Initializing create_model()
2024-04-26 02:11:30,041:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:11:30,041:INFO:Checking exceptions
2024-04-26 02:11:30,041:INFO:Importing libraries
2024-04-26 02:11:30,041:INFO:Copying training dataset
2024-04-26 02:11:30,046:INFO:Defining folds
2024-04-26 02:11:30,046:INFO:Declaring metric variables
2024-04-26 02:11:30,048:INFO:Importing untrained model
2024-04-26 02:11:30,050:INFO:SVM - Linear Kernel Imported successfully
2024-04-26 02:11:30,054:INFO:Starting cross validation
2024-04-26 02:11:30,055:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:11:30,758:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:30,762:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:11:31,090:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,095:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:11:31,353:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,512:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,517:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:11:31,545:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,551:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:11:31,559:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,563:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:11:31,608:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,649:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,653:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:11:31,702:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,706:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:11:31,715:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,719:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:11:31,726:INFO:Calculating mean and std
2024-04-26 02:11:31,727:INFO:Creating metrics dataframe
2024-04-26 02:11:31,731:INFO:Uploading results into container
2024-04-26 02:11:31,731:INFO:Uploading model into container now
2024-04-26 02:11:31,731:INFO:_master_model_container: 6
2024-04-26 02:11:31,732:INFO:_display_container: 2
2024-04-26 02:11:31,732: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)
2024-04-26 02:11:31,732:INFO:create_model() successfully completed......................................
2024-04-26 02:11:31,826:INFO:SubProcess create_model() end ==================================
2024-04-26 02:11:31,826:INFO:Creating metrics dataframe
2024-04-26 02:11:31,832:INFO:Initializing Ridge Classifier
2024-04-26 02:11:31,833:INFO:Total runtime is 0.16850568453470868 minutes
2024-04-26 02:11:31,834:INFO:SubProcess create_model() called ==================================
2024-04-26 02:11:31,835:INFO:Initializing create_model()
2024-04-26 02:11:31,835:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:11:31,835:INFO:Checking exceptions
2024-04-26 02:11:31,835:INFO:Importing libraries
2024-04-26 02:11:31,835:INFO:Copying training dataset
2024-04-26 02:11:31,840:INFO:Defining folds
2024-04-26 02:11:31,840:INFO:Declaring metric variables
2024-04-26 02:11:31,842:INFO:Importing untrained model
2024-04-26 02:11:31,844:INFO:Ridge Classifier Imported successfully
2024-04-26 02:11:31,847:INFO:Starting cross validation
2024-04-26 02:11:31,848:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:11:31,950:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,950:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,951:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,955:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,956:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,967:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,967:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:31,987:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:32,022:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:32,025:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 344, in _score
response_method = _check_response_method(estimator, self._response_method)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/validation.py", line 2022, in _check_response_method
raise AttributeError(
AttributeError: Pipeline has none of the following attributes: predict_proba.
warnings.warn(
2024-04-26 02:11:32,032:INFO:Calculating mean and std
2024-04-26 02:11:32,033:INFO:Creating metrics dataframe
2024-04-26 02:11:32,036:INFO:Uploading results into container
2024-04-26 02:11:32,036:INFO:Uploading model into container now
2024-04-26 02:11:32,036:INFO:_master_model_container: 7
2024-04-26 02:11:32,037:INFO:_display_container: 2
2024-04-26 02:11:32,037: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)
2024-04-26 02:11:32,037:INFO:create_model() successfully completed......................................
2024-04-26 02:11:32,132:INFO:SubProcess create_model() end ==================================
2024-04-26 02:11:32,132:INFO:Creating metrics dataframe
2024-04-26 02:11:32,139:INFO:Initializing Random Forest Classifier
2024-04-26 02:11:32,140:INFO:Total runtime is 0.17362221876780196 minutes
2024-04-26 02:11:32,142:INFO:SubProcess create_model() called ==================================
2024-04-26 02:11:32,142:INFO:Initializing create_model()
2024-04-26 02:11:32,143:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:11:32,143:INFO:Checking exceptions
2024-04-26 02:11:32,143:INFO:Importing libraries
2024-04-26 02:11:32,143:INFO:Copying training dataset
2024-04-26 02:11:32,148:INFO:Defining folds
2024-04-26 02:11:32,148:INFO:Declaring metric variables
2024-04-26 02:11:32,150:INFO:Importing untrained model
2024-04-26 02:11:32,153:INFO:Random Forest Classifier Imported successfully
2024-04-26 02:11:32,157:INFO:Starting cross validation
2024-04-26 02:11:32,158:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:11:34,750:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:34,832:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:34,877:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:35,125:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:35,284:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:35,342:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:35,569:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:35,573:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:36,105:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:36,108:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:36,120:INFO:Calculating mean and std
2024-04-26 02:11:36,124:INFO:Creating metrics dataframe
2024-04-26 02:11:36,132:INFO:Uploading results into container
2024-04-26 02:11:36,133:INFO:Uploading model into container now
2024-04-26 02:11:36,134:INFO:_master_model_container: 8
2024-04-26 02:11:36,134:INFO:_display_container: 2
2024-04-26 02:11:36,135: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)
2024-04-26 02:11:36,136:INFO:create_model() successfully completed......................................
2024-04-26 02:11:36,332:INFO:SubProcess create_model() end ==================================
2024-04-26 02:11:36,332:INFO:Creating metrics dataframe
2024-04-26 02:11:36,339:INFO:Initializing Quadratic Discriminant Analysis
2024-04-26 02:11:36,339:INFO:Total runtime is 0.24361123641331994 minutes
2024-04-26 02:11:36,341:INFO:SubProcess create_model() called ==================================
2024-04-26 02:11:36,341:INFO:Initializing create_model()
2024-04-26 02:11:36,341:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:11:36,341:INFO:Checking exceptions
2024-04-26 02:11:36,342:INFO:Importing libraries
2024-04-26 02:11:36,342:INFO:Copying training dataset
2024-04-26 02:11:36,347:INFO:Defining folds
2024-04-26 02:11:36,347:INFO:Declaring metric variables
2024-04-26 02:11:36,349:INFO:Importing untrained model
2024-04-26 02:11:36,351:INFO:Quadratic Discriminant Analysis Imported successfully
2024-04-26 02:11:36,355:INFO:Starting cross validation
2024-04-26 02:11:36,356:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:11:36,448:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 02:11:36,448:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 02:11:36,454:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 02:11:36,460:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 02:11:36,467:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 02:11:36,471:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 02:11:36,479:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 02:11:36,482:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 02:11:36,494:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:36,500:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:36,500:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:36,513:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:36,513:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:36,517:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:36,519:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:36,532:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:36,549:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 02:11:36,555:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-04-26 02:11:36,579:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:36,584:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:36,591:INFO:Calculating mean and std
2024-04-26 02:11:36,592:INFO:Creating metrics dataframe
2024-04-26 02:11:36,594:INFO:Uploading results into container
2024-04-26 02:11:36,595:INFO:Uploading model into container now
2024-04-26 02:11:36,595:INFO:_master_model_container: 9
2024-04-26 02:11:36,595:INFO:_display_container: 2
2024-04-26 02:11:36,595:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,
store_covariance=False, tol=0.0001)
2024-04-26 02:11:36,595:INFO:create_model() successfully completed......................................
2024-04-26 02:11:36,687:INFO:SubProcess create_model() end ==================================
2024-04-26 02:11:36,687:INFO:Creating metrics dataframe
2024-04-26 02:11:36,695:INFO:Initializing Ada Boost Classifier
2024-04-26 02:11:36,695:INFO:Total runtime is 0.24954276879628504 minutes
2024-04-26 02:11:36,697:INFO:SubProcess create_model() called ==================================
2024-04-26 02:11:36,697:INFO:Initializing create_model()
2024-04-26 02:11:36,697:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:11:36,697:INFO:Checking exceptions
2024-04-26 02:11:36,697:INFO:Importing libraries
2024-04-26 02:11:36,698:INFO:Copying training dataset
2024-04-26 02:11:36,704:INFO:Defining folds
2024-04-26 02:11:36,704:INFO:Declaring metric variables
2024-04-26 02:11:36,706:INFO:Importing untrained model
2024-04-26 02:11:36,709:INFO:Ada Boost Classifier Imported successfully
2024-04-26 02:11:36,713:INFO:Starting cross validation
2024-04-26 02:11:36,714:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:11:36,772:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 02:11:36,776:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 02:11:36,778:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 02:11:36,779:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 02:11:36,796:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 02:11:36,800:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 02:11:36,822:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 02:11:36,847:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 02:11:38,255:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:38,260:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:11:38,286:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:38,290:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:11:38,409:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:38,411:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 02:11:38,423:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:38,425:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:38,428:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:38,429:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:11:38,433:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:11:38,440:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/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(
2024-04-26 02:11:38,451:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:38,453:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:38,457:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:11:39,206:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:39,208:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:11:39,224:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:11:39,230:INFO:Calculating mean and std
2024-04-26 02:11:39,231:INFO:Creating metrics dataframe
2024-04-26 02:11:39,234:INFO:Uploading results into container
2024-04-26 02:11:39,234:INFO:Uploading model into container now
2024-04-26 02:11:39,235:INFO:_master_model_container: 10
2024-04-26 02:11:39,235:INFO:_display_container: 2
2024-04-26 02:11:39,235:INFO:AdaBoostClassifier(algorithm='SAMME.R', estimator=None, learning_rate=1.0,
n_estimators=50, random_state=123)
2024-04-26 02:11:39,235:INFO:create_model() successfully completed......................................
2024-04-26 02:11:39,317:INFO:SubProcess create_model() end ==================================
2024-04-26 02:11:39,317:INFO:Creating metrics dataframe
2024-04-26 02:11:39,324:INFO:Initializing Gradient Boosting Classifier
2024-04-26 02:11:39,324:INFO:Total runtime is 0.2933688163757325 minutes
2024-04-26 02:11:39,326:INFO:SubProcess create_model() called ==================================
2024-04-26 02:11:39,326:INFO:Initializing create_model()
2024-04-26 02:11:39,326:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:11:39,327:INFO:Checking exceptions
2024-04-26 02:11:39,327:INFO:Importing libraries
2024-04-26 02:11:39,327:INFO:Copying training dataset
2024-04-26 02:11:39,331:INFO:Defining folds
2024-04-26 02:11:39,331:INFO:Declaring metric variables
2024-04-26 02:11:39,333:INFO:Importing untrained model
2024-04-26 02:11:39,335:INFO:Gradient Boosting Classifier Imported successfully
2024-04-26 02:11:39,339:INFO:Starting cross validation
2024-04-26 02:11:39,340:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:12:15,090:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:15,151:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:15,173:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:15,236:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:15,239:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:15,655:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:15,699:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:15,804:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:37,950:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:37,952:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:37,960:INFO:Calculating mean and std
2024-04-26 02:12:37,962:INFO:Creating metrics dataframe
2024-04-26 02:12:37,967:INFO:Uploading results into container
2024-04-26 02:12:37,968:INFO:Uploading model into container now
2024-04-26 02:12:37,968:INFO:_master_model_container: 11
2024-04-26 02:12:37,968:INFO:_display_container: 2
2024-04-26 02:12:37,969: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)
2024-04-26 02:12:37,969:INFO:create_model() successfully completed......................................
2024-04-26 02:12:38,138:INFO:SubProcess create_model() end ==================================
2024-04-26 02:12:38,138:INFO:Creating metrics dataframe
2024-04-26 02:12:38,145:INFO:Initializing Linear Discriminant Analysis
2024-04-26 02:12:38,145:INFO:Total runtime is 1.2737191716829936 minutes
2024-04-26 02:12:38,147:INFO:SubProcess create_model() called ==================================
2024-04-26 02:12:38,148:INFO:Initializing create_model()
2024-04-26 02:12:38,148:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=lda, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:12:38,148:INFO:Checking exceptions
2024-04-26 02:12:38,148:INFO:Importing libraries
2024-04-26 02:12:38,148:INFO:Copying training dataset
2024-04-26 02:12:38,153:INFO:Defining folds
2024-04-26 02:12:38,154:INFO:Declaring metric variables
2024-04-26 02:12:38,155:INFO:Importing untrained model
2024-04-26 02:12:38,157:INFO:Linear Discriminant Analysis Imported successfully
2024-04-26 02:12:38,161:INFO:Starting cross validation
2024-04-26 02:12:38,162:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:12:38,290:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:38,295:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:38,301:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:38,306:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:38,306:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:38,313:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:38,331:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:38,373:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:38,396:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:38,400:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:38,406:INFO:Calculating mean and std
2024-04-26 02:12:38,407:INFO:Creating metrics dataframe
2024-04-26 02:12:38,409:INFO:Uploading results into container
2024-04-26 02:12:38,409:INFO:Uploading model into container now
2024-04-26 02:12:38,410:INFO:_master_model_container: 12
2024-04-26 02:12:38,410:INFO:_display_container: 2
2024-04-26 02:12:38,410:INFO:LinearDiscriminantAnalysis(covariance_estimator=None, n_components=None,
priors=None, shrinkage=None, solver='svd',
store_covariance=False, tol=0.0001)
2024-04-26 02:12:38,411:INFO:create_model() successfully completed......................................
2024-04-26 02:12:38,492:INFO:SubProcess create_model() end ==================================
2024-04-26 02:12:38,492:INFO:Creating metrics dataframe
2024-04-26 02:12:38,499:INFO:Initializing Extra Trees Classifier
2024-04-26 02:12:38,499:INFO:Total runtime is 1.2796141505241394 minutes
2024-04-26 02:12:38,501:INFO:SubProcess create_model() called ==================================
2024-04-26 02:12:38,501:INFO:Initializing create_model()
2024-04-26 02:12:38,501:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=et, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:12:38,501:INFO:Checking exceptions
2024-04-26 02:12:38,502:INFO:Importing libraries
2024-04-26 02:12:38,502:INFO:Copying training dataset
2024-04-26 02:12:38,507:INFO:Defining folds
2024-04-26 02:12:38,507:INFO:Declaring metric variables
2024-04-26 02:12:38,509:INFO:Importing untrained model
2024-04-26 02:12:38,511:INFO:Extra Trees Classifier Imported successfully
2024-04-26 02:12:38,515:INFO:Starting cross validation
2024-04-26 02:12:38,516:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:12:39,258:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:39,352:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:39,395:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:39,424:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:39,430:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:39,436:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:39,453:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:39,463:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:39,667:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:39,672:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:12:39,679:INFO:Calculating mean and std
2024-04-26 02:12:39,682:INFO:Creating metrics dataframe
2024-04-26 02:12:39,687:INFO:Uploading results into container
2024-04-26 02:12:39,687:INFO:Uploading model into container now
2024-04-26 02:12:39,688:INFO:_master_model_container: 13
2024-04-26 02:12:39,688:INFO:_display_container: 2
2024-04-26 02:12:39,688:INFO:ExtraTreesClassifier(bootstrap=False, 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)
2024-04-26 02:12:39,689:INFO:create_model() successfully completed......................................
2024-04-26 02:12:39,783:INFO:SubProcess create_model() end ==================================
2024-04-26 02:12:39,783:INFO:Creating metrics dataframe
2024-04-26 02:12:39,791:INFO:Initializing Extreme Gradient Boosting
2024-04-26 02:12:39,791:INFO:Total runtime is 1.3011484344800313 minutes
2024-04-26 02:12:39,793:INFO:SubProcess create_model() called ==================================
2024-04-26 02:12:39,793:INFO:Initializing create_model()
2024-04-26 02:12:39,793:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=xgboost, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:12:39,794:INFO:Checking exceptions
2024-04-26 02:12:39,794:INFO:Importing libraries
2024-04-26 02:12:39,794:INFO:Copying training dataset
2024-04-26 02:12:39,799:INFO:Defining folds
2024-04-26 02:12:39,800:INFO:Declaring metric variables
2024-04-26 02:12:39,802:INFO:Importing untrained model
2024-04-26 02:12:39,804:INFO:Extreme Gradient Boosting Imported successfully
2024-04-26 02:12:39,808:INFO:Starting cross validation
2024-04-26 02:12:39,809:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:13:14,846:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:13:14,973:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:13:15,048:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:13:15,049:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:13:15,186:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:13:15,275:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:13:15,310:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:13:15,375:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:13:23,480:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:13:23,481:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:13:23,493:INFO:Calculating mean and std
2024-04-26 02:13:23,495:INFO:Creating metrics dataframe
2024-04-26 02:13:23,501:INFO:Uploading results into container
2024-04-26 02:13:23,502:INFO:Uploading model into container now
2024-04-26 02:13:23,503:INFO:_master_model_container: 14
2024-04-26 02:13:23,504:INFO:_display_container: 2
2024-04-26 02:13:23,506:INFO:XGBClassifier(base_score=None, booster='gbtree', callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=100, n_jobs=-1, num_parallel_tree=None,
objective='binary:logistic', predictor=None, ...)
2024-04-26 02:13:23,506:INFO:create_model() successfully completed......................................
2024-04-26 02:13:23,713:INFO:SubProcess create_model() end ==================================
2024-04-26 02:13:23,713:INFO:Creating metrics dataframe
2024-04-26 02:13:23,722:INFO:Initializing Light Gradient Boosting Machine
2024-04-26 02:13:23,722:INFO:Total runtime is 2.0333348671595255 minutes
2024-04-26 02:13:23,725:INFO:SubProcess create_model() called ==================================
2024-04-26 02:13:23,725:INFO:Initializing create_model()
2024-04-26 02:13:23,725:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=lightgbm, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:13:23,725:INFO:Checking exceptions
2024-04-26 02:13:23,725:INFO:Importing libraries
2024-04-26 02:13:23,726:INFO:Copying training dataset
2024-04-26 02:13:23,732:INFO:Defining folds
2024-04-26 02:13:23,732:INFO:Declaring metric variables
2024-04-26 02:13:23,734:INFO:Importing untrained model
2024-04-26 02:13:23,737:INFO:Light Gradient Boosting Machine Imported successfully
2024-04-26 02:13:23,741:INFO:Starting cross validation
2024-04-26 02:13:23,742:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:14:28,631:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:14:28,642:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:14:28,754:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:14:28,844:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:14:28,935:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:14:29,018:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:14:29,052:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:14:29,136:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:14:35,564:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:14:35,655:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:14:35,668:INFO:Calculating mean and std
2024-04-26 02:14:35,671:INFO:Creating metrics dataframe
2024-04-26 02:14:35,678:INFO:Uploading results into container
2024-04-26 02:14:35,679:INFO:Uploading model into container now
2024-04-26 02:14:35,680:INFO:_master_model_container: 15
2024-04-26 02:14:35,680:INFO:_display_container: 2
2024-04-26 02:14:35,681:INFO:LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,
importance_type='split', learning_rate=0.1, max_depth=-1,
min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,
random_state=123, reg_alpha=0.0, reg_lambda=0.0, subsample=1.0,
subsample_for_bin=200000, subsample_freq=0)
2024-04-26 02:14:35,682:INFO:create_model() successfully completed......................................
2024-04-26 02:14:35,890:INFO:SubProcess create_model() end ==================================
2024-04-26 02:14:35,890:INFO:Creating metrics dataframe
2024-04-26 02:14:35,901:INFO:Initializing CatBoost Classifier
2024-04-26 02:14:35,901:INFO:Total runtime is 3.2363196849822997 minutes
2024-04-26 02:14:35,904:INFO:SubProcess create_model() called ==================================
2024-04-26 02:14:35,904:INFO:Initializing create_model()
2024-04-26 02:14:35,904:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=catboost, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:14:35,904:INFO:Checking exceptions
2024-04-26 02:14:35,904:INFO:Importing libraries
2024-04-26 02:14:35,904:INFO:Copying training dataset
2024-04-26 02:14:35,911:INFO:Defining folds
2024-04-26 02:14:35,911:INFO:Declaring metric variables
2024-04-26 02:14:35,914:INFO:Importing untrained model
2024-04-26 02:14:35,920:INFO:CatBoost Classifier Imported successfully
2024-04-26 02:14:35,924:INFO:Starting cross validation
2024-04-26 02:14:35,925:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:16:34,707:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:16:35,274:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:16:35,413:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:16:35,486:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:16:35,723:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:16:35,915:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:16:36,033:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:16:36,584:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:17:04,441:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:17:04,593:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:17:04,605:INFO:Calculating mean and std
2024-04-26 02:17:04,608:INFO:Creating metrics dataframe
2024-04-26 02:17:04,615:INFO:Uploading results into container
2024-04-26 02:17:04,615:INFO:Uploading model into container now
2024-04-26 02:17:04,616:INFO:_master_model_container: 16
2024-04-26 02:17:04,616:INFO:_display_container: 2
2024-04-26 02:17:04,616:INFO:<catboost.core.CatBoostClassifier object at 0x28aadaf50>
2024-04-26 02:17:04,616:INFO:create_model() successfully completed......................................
2024-04-26 02:17:04,759:INFO:SubProcess create_model() end ==================================
2024-04-26 02:17:04,759:INFO:Creating metrics dataframe
2024-04-26 02:17:04,767:INFO:Initializing Dummy Classifier
2024-04-26 02:17:04,767:INFO:Total runtime is 5.717418738206227 minutes
2024-04-26 02:17:04,769:INFO:SubProcess create_model() called ==================================
2024-04-26 02:17:04,770:INFO:Initializing create_model()
2024-04-26 02:17:04,770:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=dummy, 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 0x289ddfb20>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:17:04,770:INFO:Checking exceptions
2024-04-26 02:17:04,770:INFO:Importing libraries
2024-04-26 02:17:04,770:INFO:Copying training dataset
2024-04-26 02:17:04,775:INFO:Defining folds
2024-04-26 02:17:04,775:INFO:Declaring metric variables
2024-04-26 02:17:04,777:INFO:Importing untrained model
2024-04-26 02:17:04,779:INFO:Dummy Classifier Imported successfully
2024-04-26 02:17:04,783:INFO:Starting cross validation
2024-04-26 02:17:04,784:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:17:04,886:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:17:04,896:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:17:04,911:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:17:04,918:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:17:04,920:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:17:04,923:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:17:04,928:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:17:04,931:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:17:04,932:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:17:04,936:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:17:04,968:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:17:04,976:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:17:04,977:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:17:04,981:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:17:04,982:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:17:04,985:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:17:04,985:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:17:04,988:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:17:05,015:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:17:05,017:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1497: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-04-26 02:17:05,022:INFO:Calculating mean and std
2024-04-26 02:17:05,024:INFO:Creating metrics dataframe
2024-04-26 02:17:05,026:INFO:Uploading results into container
2024-04-26 02:17:05,027:INFO:Uploading model into container now
2024-04-26 02:17:05,027:INFO:_master_model_container: 17
2024-04-26 02:17:05,027:INFO:_display_container: 2
2024-04-26 02:17:05,028:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2024-04-26 02:17:05,028:INFO:create_model() successfully completed......................................
2024-04-26 02:17:05,381:INFO:SubProcess create_model() end ==================================
2024-04-26 02:17:05,382:INFO:Creating metrics dataframe
2024-04-26 02:17:05,448:INFO:Initializing create_model()
2024-04-26 02:17:05,448:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=<catboost.core.CatBoostClassifier object at 0x28aadaf50>, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:17:05,448:INFO:Checking exceptions
2024-04-26 02:17:05,450:INFO:Importing libraries
2024-04-26 02:17:05,451:INFO:Copying training dataset
2024-04-26 02:17:05,469:INFO:Defining folds
2024-04-26 02:17:05,470:INFO:Declaring metric variables
2024-04-26 02:17:05,470:INFO:Importing untrained model
2024-04-26 02:17:05,470:INFO:Declaring custom model
2024-04-26 02:17:05,471:INFO:CatBoost Classifier Imported successfully
2024-04-26 02:17:05,473:INFO:Cross validation set to False
2024-04-26 02:17:05,473:INFO:Fitting Model
2024-04-26 02:17:21,058:INFO:<catboost.core.CatBoostClassifier object at 0x28a81bca0>
2024-04-26 02:17:21,059:INFO:create_model() successfully completed......................................
2024-04-26 02:17:21,264:INFO:_master_model_container: 17
2024-04-26 02:17:21,264:INFO:_display_container: 2
2024-04-26 02:17:21,264:INFO:<catboost.core.CatBoostClassifier object at 0x28a81bca0>
2024-04-26 02:17:21,264:INFO:compare_models() successfully completed......................................
2024-04-26 02:17:21,266:INFO:Initializing tune_model()
2024-04-26 02:17:21,266:INFO:tune_model(estimator=<catboost.core.CatBoostClassifier object at 0x28a81bca0>, fold=None, round=4, n_iter=10, custom_grid=None, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}, self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>)
2024-04-26 02:17:21,266:INFO:Checking exceptions
2024-04-26 02:17:21,277:INFO:Copying training dataset
2024-04-26 02:17:21,282:INFO:Checking base model
2024-04-26 02:17:21,282:INFO:Base model : CatBoost Classifier
2024-04-26 02:17:21,284:INFO:Declaring metric variables
2024-04-26 02:17:21,286:INFO:Defining Hyperparameters
2024-04-26 02:17:21,379:INFO:Tuning with n_jobs=-1
2024-04-26 02:17:21,379:INFO:Initializing RandomizedSearchCV
2024-04-26 02:19:33,244:INFO:best_params: {'actual_estimator__random_strength': 0.2, 'actual_estimator__n_estimators': 180, 'actual_estimator__l2_leaf_reg': 30, 'actual_estimator__eta': 0.4, 'actual_estimator__depth': 8}
2024-04-26 02:19:33,249:INFO:Hyperparameter search completed
2024-04-26 02:19:33,250:INFO:SubProcess create_model() called ==================================
2024-04-26 02:19:33,251:INFO:Initializing create_model()
2024-04-26 02:19:33,251:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=<catboost.core.CatBoostClassifier object at 0x28a8ac070>, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=<pycaret.internal.display.display.CommonDisplay object at 0x28c4fa680>, model_only=True, return_train_score=False, error_score=0.0, kwargs={'random_strength': 0.2, 'n_estimators': 180, 'l2_leaf_reg': 30, 'eta': 0.4, 'depth': 8})
2024-04-26 02:19:33,251:INFO:Checking exceptions
2024-04-26 02:19:33,252:INFO:Importing libraries
2024-04-26 02:19:33,252:INFO:Copying training dataset
2024-04-26 02:19:33,264:INFO:Defining folds
2024-04-26 02:19:33,264:INFO:Declaring metric variables
2024-04-26 02:19:33,271:INFO:Importing untrained model
2024-04-26 02:19:33,272:INFO:Declaring custom model
2024-04-26 02:19:33,275:INFO:CatBoost Classifier Imported successfully
2024-04-26 02:19:33,282:INFO:Starting cross validation
2024-04-26 02:19:33,284:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:20:40,943:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:20:41,292:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:20:41,461:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:20:41,522:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:20:41,624:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:20:41,632:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:20:41,699:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:20:42,043:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:21:00,185:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:21:00,543:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:21:00,571:INFO:Calculating mean and std
2024-04-26 02:21:00,576:INFO:Creating metrics dataframe
2024-04-26 02:21:00,588:INFO:Finalizing model
2024-04-26 02:21:14,402:INFO:Uploading results into container
2024-04-26 02:21:14,405:INFO:Uploading model into container now
2024-04-26 02:21:14,407:INFO:_master_model_container: 18
2024-04-26 02:21:14,407:INFO:_display_container: 3
2024-04-26 02:21:14,407:INFO:<catboost.core.CatBoostClassifier object at 0x287e0bac0>
2024-04-26 02:21:14,407:INFO:create_model() successfully completed......................................
2024-04-26 02:21:14,654:INFO:SubProcess create_model() end ==================================
2024-04-26 02:21:14,654:INFO:choose_better activated
2024-04-26 02:21:14,658:INFO:SubProcess create_model() called ==================================
2024-04-26 02:21:14,658:INFO:Initializing create_model()
2024-04-26 02:21:14,658:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=<catboost.core.CatBoostClassifier object at 0x28a81bca0>, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:21:14,659:INFO:Checking exceptions
2024-04-26 02:21:14,660:INFO:Importing libraries
2024-04-26 02:21:14,661:INFO:Copying training dataset
2024-04-26 02:21:14,669:INFO:Defining folds
2024-04-26 02:21:14,669:INFO:Declaring metric variables
2024-04-26 02:21:14,669:INFO:Importing untrained model
2024-04-26 02:21:14,669:INFO:Declaring custom model
2024-04-26 02:21:14,670:INFO:CatBoost Classifier Imported successfully
2024-04-26 02:21:14,670:INFO:Starting cross validation
2024-04-26 02:21:14,671:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:23:28,266:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:23:29,633:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:23:29,653:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:23:29,897:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:23:30,003:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:23:30,064:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:23:30,225:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:23:30,559:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:23:59,690:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:23:59,923:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='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 "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py", line 188, in _score
return super()._score(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 345, in _score
y_pred = method_caller(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/metrics/_scorer.py", line 87, in _cached_call
result, _ = _get_response_values(
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_response.py", line 210, in _get_response_values
y_pred = prediction_method(X)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/pipeline.py", line 341, in predict_proba
Xt = transform.transform(Xt)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 273, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/preprocess/transformers.py", line 248, in transform
args.append(X[self._include])
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/frame.py", line 3813, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6070, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
File "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/indexes/base.py", line 6130, in _raise_if_missing
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Age_Group'], dtype='object')] are in the [columns]"
warnings.warn(
2024-04-26 02:23:59,938:INFO:Calculating mean and std
2024-04-26 02:23:59,939:INFO:Creating metrics dataframe
2024-04-26 02:23:59,944:INFO:Finalizing model
2024-04-26 02:24:17,135:INFO:Uploading results into container
2024-04-26 02:24:17,137:INFO:Uploading model into container now
2024-04-26 02:24:17,138:INFO:_master_model_container: 19
2024-04-26 02:24:17,138:INFO:_display_container: 4
2024-04-26 02:24:17,138:INFO:<catboost.core.CatBoostClassifier object at 0x2844e0310>
2024-04-26 02:24:17,138:INFO:create_model() successfully completed......................................
2024-04-26 02:24:17,402:INFO:SubProcess create_model() end ==================================
2024-04-26 02:24:17,403:INFO:<catboost.core.CatBoostClassifier object at 0x2844e0310> result for Accuracy is 0.9024
2024-04-26 02:24:17,403:INFO:<catboost.core.CatBoostClassifier object at 0x287e0bac0> result for Accuracy is 0.8949
2024-04-26 02:24:17,403:INFO:<catboost.core.CatBoostClassifier object at 0x2844e0310> is best model
2024-04-26 02:24:17,403:INFO:choose_better completed
2024-04-26 02:24:17,403:INFO:Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).
2024-04-26 02:24:17,412:INFO:_master_model_container: 19
2024-04-26 02:24:17,412:INFO:_display_container: 3
2024-04-26 02:24:17,412:INFO:<catboost.core.CatBoostClassifier object at 0x2844e0310>
2024-04-26 02:24:17,412:INFO:tune_model() successfully completed......................................
2024-04-26 02:24:17,499:INFO:Initializing finalize_model()
2024-04-26 02:24:17,499:INFO:finalize_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=<catboost.core.CatBoostClassifier object at 0x2844e0310>, fit_kwargs=None, groups=None, model_only=False, experiment_custom_tags=None)
2024-04-26 02:24:17,501:INFO:Finalizing <catboost.core.CatBoostClassifier object at 0x2844e0310>
2024-04-26 02:24:17,505:INFO:Initializing create_model()
2024-04-26 02:24:17,505:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=<catboost.core.CatBoostClassifier object at 0x2844e0310>, fold=None, round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=False, metrics=None, display=None, model_only=False, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:24:17,505:INFO:Checking exceptions
2024-04-26 02:24:17,506:INFO:Importing libraries
2024-04-26 02:24:17,506:INFO:Copying training dataset
2024-04-26 02:24:17,506:INFO:Defining folds
2024-04-26 02:24:17,506:INFO:Declaring metric variables
2024-04-26 02:24:17,506:INFO:Importing untrained model
2024-04-26 02:24:17,506:INFO:Declaring custom model
2024-04-26 02:24:17,507:INFO:CatBoost Classifier Imported successfully
2024-04-26 02:24:17,507:INFO:Cross validation set to False
2024-04-26 02:24:17,507:INFO:Fitting Model
2024-04-26 02:24:40,624:INFO:Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'MTRANS_Public_Transportation',
'MTRANS_Walking', 'BMI', 'Ag...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x28a8ac190>)],
verbose=False)
2024-04-26 02:24:40,625:INFO:create_model() successfully completed......................................
2024-04-26 02:24:40,798:INFO:_master_model_container: 19
2024-04-26 02:24:40,799:INFO:_display_container: 3
2024-04-26 02:24:40,803:INFO:Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'MTRANS_Public_Transportation',
'MTRANS_Walking', 'BMI', 'Ag...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x28a8ac190>)],
verbose=False)
2024-04-26 02:24:40,803:INFO:finalize_model() successfully completed......................................
2024-04-26 02:24:40,891:INFO:Initializing save_model()
2024-04-26 02:24:40,891:INFO:save_model(model=Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'MTRANS_Public_Transportation',
'MTRANS_Walking', 'BMI', 'Ag...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x28a8ac190>)],
verbose=False), model_name=model_name, prep_pipe_=Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'M...
TransformerWrapper(exclude=None, include=['Age_Group'],
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False), verbose=True, use_case=MLUsecase.CLASSIFICATION, kwargs={})
2024-04-26 02:24:40,891:INFO:Adding model into prep_pipe
2024-04-26 02:24:40,891:WARNING:Only Model saved as it was a pipeline.
2024-04-26 02:24:40,899:INFO:model_name.pkl saved in current working directory
2024-04-26 02:24:40,903:INFO:Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'MTRANS_Public_Transportation',
'MTRANS_Walking', 'BMI', 'Ag...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x28a8ac190>)],
verbose=False)
2024-04-26 02:24:40,904:INFO:save_model() successfully completed......................................
2024-04-26 02:27:46,430:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-04-26 02:27:46,431:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-04-26 02:27:46,431:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-04-26 02:27:46,431:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-04-26 02:27:55,448:INFO:PyCaret ClassificationExperiment
2024-04-26 02:27:55,448:INFO:Logging name: clf-default-name
2024-04-26 02:27:55,448:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2024-04-26 02:27:55,448:INFO:version 3.3.0
2024-04-26 02:27:55,448:INFO:Initializing setup()
2024-04-26 02:27:55,448:INFO:self.USI: 89c7
2024-04-26 02:27:55,448:INFO:self._variable_keys: {'is_multiclass', 'gpu_n_jobs_param', 'html_param', 'y_test', 'fold_groups_param', 'y', 'memory', 'log_plots_param', 'exp_id', 'pipeline', 'fix_imbalance', '_ml_usecase', 'X', 'X_train', 'y_train', 'logging_param', 'seed', 'gpu_param', 'exp_name_log', '_available_plots', 'X_test', 'target_param', 'data', 'fold_generator', 'fold_shuffle_param', 'n_jobs_param', 'idx', 'USI'}
2024-04-26 02:27:55,448:INFO:Checking environment
2024-04-26 02:27:55,448:INFO:python_version: 3.10.13
2024-04-26 02:27:55,448:INFO:python_build: ('main', 'Sep 11 2023 08:16:02')
2024-04-26 02:27:55,448:INFO:machine: arm64
2024-04-26 02:27:55,448:INFO:platform: macOS-14.0-arm64-arm-64bit
2024-04-26 02:27:55,448:INFO:Memory: svmem(total=8589934592, available=1459617792, percent=83.0, used=2932342784, free=46956544, active=1425047552, inactive=1391411200, wired=1507295232)
2024-04-26 02:27:55,448:INFO:Physical Core: 8
2024-04-26 02:27:55,448:INFO:Logical Core: 8
2024-04-26 02:27:55,448:INFO:Checking libraries
2024-04-26 02:27:55,448:INFO:System:
2024-04-26 02:27:55,448:INFO: python: 3.10.13 (main, Sep 11 2023, 08:16:02) [Clang 14.0.6 ]
2024-04-26 02:27:55,448:INFO:executable: /Users/arham/anaconda3/envs/DataScience/bin/python
2024-04-26 02:27:55,448:INFO: machine: macOS-14.0-arm64-arm-64bit
2024-04-26 02:27:55,448:INFO:PyCaret required dependencies:
2024-04-26 02:27:55,450:INFO: pip: 23.3
2024-04-26 02:27:55,450:INFO: setuptools: 60.2.0
2024-04-26 02:27:55,450:INFO: pycaret: 3.3.0
2024-04-26 02:27:55,450:INFO: IPython: 8.15.0
2024-04-26 02:27:55,450:INFO: ipywidgets: 8.0.4
2024-04-26 02:27:55,450:INFO: tqdm: 4.65.2
2024-04-26 02:27:55,450:INFO: numpy: 1.25.2
2024-04-26 02:27:55,450:INFO: pandas: 1.5.3
2024-04-26 02:27:55,450:INFO: jinja2: 3.1.3
2024-04-26 02:27:55,450:INFO: scipy: 1.11.4
2024-04-26 02:27:55,451:INFO: joblib: 1.2.0
2024-04-26 02:27:55,451:INFO: sklearn: 1.4.0
2024-04-26 02:27:55,451:INFO: pyod: 1.1.3
2024-04-26 02:27:55,451:INFO: imblearn: 0.12.2
2024-04-26 02:27:55,451:INFO: category_encoders: 2.6.3
2024-04-26 02:27:55,451:INFO: lightgbm: 4.1.0
2024-04-26 02:27:55,451:INFO: numba: 0.58.1
2024-04-26 02:27:55,451:INFO: requests: 2.31.0
2024-04-26 02:27:55,451:INFO: matplotlib: 3.8.4
2024-04-26 02:27:55,451:INFO: scikitplot: 0.3.7
2024-04-26 02:27:55,451:INFO: yellowbrick: 1.5
2024-04-26 02:27:55,451:INFO: plotly: 5.17.0
2024-04-26 02:27:55,451:INFO: plotly-resampler: Not installed
2024-04-26 02:27:55,451:INFO: kaleido: 0.2.1
2024-04-26 02:27:55,451:INFO: schemdraw: 0.15
2024-04-26 02:27:55,451:INFO: statsmodels: 0.13.5
2024-04-26 02:27:55,451:INFO: sktime: 0.26.1
2024-04-26 02:27:55,451:INFO: tbats: 1.1.3
2024-04-26 02:27:55,451:INFO: pmdarima: 2.0.4
2024-04-26 02:27:55,451:INFO: psutil: 5.9.0
2024-04-26 02:27:55,451:INFO: markupsafe: 2.1.1
2024-04-26 02:27:55,451:INFO: pickle5: Not installed
2024-04-26 02:27:55,451:INFO: cloudpickle: 2.2.1
2024-04-26 02:27:55,451:INFO: deprecation: 2.1.0
2024-04-26 02:27:55,451:INFO: xxhash: 3.4.1
2024-04-26 02:27:55,451:INFO: wurlitzer: 3.0.2
2024-04-26 02:27:55,451:INFO:PyCaret optional dependencies:
2024-04-26 02:27:55,956:INFO: shap: 0.44.0
2024-04-26 02:27:55,956:INFO: interpret: Not installed
2024-04-26 02:27:55,956:INFO: umap: Not installed
2024-04-26 02:27:55,956:INFO: ydata_profiling: 0.0.dev0
2024-04-26 02:27:55,956:INFO: explainerdashboard: Not installed
2024-04-26 02:27:55,956:INFO: autoviz: Not installed
2024-04-26 02:27:55,956:INFO: fairlearn: Not installed
2024-04-26 02:27:55,956:INFO: deepchecks: Not installed
2024-04-26 02:27:55,956:INFO: xgboost: 1.7.3
2024-04-26 02:27:55,956:INFO: catboost: 1.1.1
2024-04-26 02:27:55,956:INFO: kmodes: Not installed
2024-04-26 02:27:55,956:INFO: mlxtend: Not installed
2024-04-26 02:27:55,957:INFO: statsforecast: 1.4.0
2024-04-26 02:27:55,957:INFO: tune_sklearn: Not installed
2024-04-26 02:27:55,957:INFO: ray: 2.10.0
2024-04-26 02:27:55,957:INFO: hyperopt: 0.2.7
2024-04-26 02:27:55,957:INFO: optuna: 3.5.0
2024-04-26 02:27:55,957:INFO: skopt: Not installed
2024-04-26 02:27:55,957:INFO: mlflow: 2.10.2
2024-04-26 02:27:55,957:INFO: gradio: 3.48.0
2024-04-26 02:27:55,957:INFO: fastapi: 0.109.2
2024-04-26 02:27:55,957:INFO: uvicorn: 0.27.1
2024-04-26 02:27:55,957:INFO: m2cgen: Not installed
2024-04-26 02:27:55,957:INFO: evidently: Not installed
2024-04-26 02:27:55,957:INFO: fugue: Not installed
2024-04-26 02:27:55,957:INFO: streamlit: 1.27.2
2024-04-26 02:27:55,957:INFO: prophet: Not installed
2024-04-26 02:27:55,957:INFO:None
2024-04-26 02:27:55,957:INFO:Set up data.
2024-04-26 02:28:00,980:INFO:PyCaret ClassificationExperiment
2024-04-26 02:28:00,980:INFO:Logging name: clf-default-name
2024-04-26 02:28:00,980:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2024-04-26 02:28:00,980:INFO:version 3.3.0
2024-04-26 02:28:00,980:INFO:Initializing setup()
2024-04-26 02:28:00,980:INFO:self.USI: 370d
2024-04-26 02:28:00,980:INFO:self._variable_keys: {'is_multiclass', 'gpu_n_jobs_param', 'html_param', 'y_test', 'fold_groups_param', 'y', 'memory', 'log_plots_param', 'exp_id', 'pipeline', 'fix_imbalance', '_ml_usecase', 'X', 'X_train', 'y_train', 'logging_param', 'seed', 'gpu_param', 'exp_name_log', '_available_plots', 'X_test', 'target_param', 'data', 'fold_generator', 'fold_shuffle_param', 'n_jobs_param', 'idx', 'USI'}
2024-04-26 02:28:00,980:INFO:Checking environment
2024-04-26 02:28:00,980:INFO:python_version: 3.10.13
2024-04-26 02:28:00,980:INFO:python_build: ('main', 'Sep 11 2023 08:16:02')
2024-04-26 02:28:00,980:INFO:machine: arm64
2024-04-26 02:28:00,980:INFO:platform: macOS-14.0-arm64-arm-64bit
2024-04-26 02:28:00,980:INFO:Memory: svmem(total=8589934592, available=1347829760, percent=84.3, used=2856386560, free=52477952, active=1306181632, inactive=1273020416, wired=1550204928)
2024-04-26 02:28:00,980:INFO:Physical Core: 8
2024-04-26 02:28:00,980:INFO:Logical Core: 8
2024-04-26 02:28:00,980:INFO:Checking libraries
2024-04-26 02:28:00,980:INFO:System:
2024-04-26 02:28:00,980:INFO: python: 3.10.13 (main, Sep 11 2023, 08:16:02) [Clang 14.0.6 ]
2024-04-26 02:28:00,981:INFO:executable: /Users/arham/anaconda3/envs/DataScience/bin/python
2024-04-26 02:28:00,981:INFO: machine: macOS-14.0-arm64-arm-64bit
2024-04-26 02:28:00,981:INFO:PyCaret required dependencies:
2024-04-26 02:28:00,981:INFO: pip: 23.3
2024-04-26 02:28:00,981:INFO: setuptools: 60.2.0
2024-04-26 02:28:00,981:INFO: pycaret: 3.3.0
2024-04-26 02:28:00,981:INFO: IPython: 8.15.0
2024-04-26 02:28:00,981:INFO: ipywidgets: 8.0.4
2024-04-26 02:28:00,981:INFO: tqdm: 4.65.2
2024-04-26 02:28:00,981:INFO: numpy: 1.25.2
2024-04-26 02:28:00,981:INFO: pandas: 1.5.3
2024-04-26 02:28:00,981:INFO: jinja2: 3.1.3
2024-04-26 02:28:00,981:INFO: scipy: 1.11.4
2024-04-26 02:28:00,981:INFO: joblib: 1.2.0
2024-04-26 02:28:00,981:INFO: sklearn: 1.4.0
2024-04-26 02:28:00,981:INFO: pyod: 1.1.3
2024-04-26 02:28:00,981:INFO: imblearn: 0.12.2
2024-04-26 02:28:00,981:INFO: category_encoders: 2.6.3
2024-04-26 02:28:00,981:INFO: lightgbm: 4.1.0
2024-04-26 02:28:00,981:INFO: numba: 0.58.1
2024-04-26 02:28:00,981:INFO: requests: 2.31.0
2024-04-26 02:28:00,981:INFO: matplotlib: 3.8.4
2024-04-26 02:28:00,981:INFO: scikitplot: 0.3.7
2024-04-26 02:28:00,981:INFO: yellowbrick: 1.5
2024-04-26 02:28:00,981:INFO: plotly: 5.17.0
2024-04-26 02:28:00,981:INFO: plotly-resampler: Not installed
2024-04-26 02:28:00,981:INFO: kaleido: 0.2.1
2024-04-26 02:28:00,981:INFO: schemdraw: 0.15
2024-04-26 02:28:00,981:INFO: statsmodels: 0.13.5
2024-04-26 02:28:00,981:INFO: sktime: 0.26.1
2024-04-26 02:28:00,981:INFO: tbats: 1.1.3
2024-04-26 02:28:00,981:INFO: pmdarima: 2.0.4
2024-04-26 02:28:00,981:INFO: psutil: 5.9.0
2024-04-26 02:28:00,981:INFO: markupsafe: 2.1.1
2024-04-26 02:28:00,981:INFO: pickle5: Not installed
2024-04-26 02:28:00,981:INFO: cloudpickle: 2.2.1
2024-04-26 02:28:00,981:INFO: deprecation: 2.1.0
2024-04-26 02:28:00,981:INFO: xxhash: 3.4.1
2024-04-26 02:28:00,981:INFO: wurlitzer: 3.0.2
2024-04-26 02:28:00,981:INFO:PyCaret optional dependencies:
2024-04-26 02:28:00,981:INFO: shap: 0.44.0
2024-04-26 02:28:00,981:INFO: interpret: Not installed
2024-04-26 02:28:00,981:INFO: umap: Not installed
2024-04-26 02:28:00,982:INFO: ydata_profiling: 0.0.dev0
2024-04-26 02:28:00,982:INFO: explainerdashboard: Not installed
2024-04-26 02:28:00,982:INFO: autoviz: Not installed
2024-04-26 02:28:00,982:INFO: fairlearn: Not installed
2024-04-26 02:28:00,982:INFO: deepchecks: Not installed
2024-04-26 02:28:00,982:INFO: xgboost: 1.7.3
2024-04-26 02:28:00,982:INFO: catboost: 1.1.1
2024-04-26 02:28:00,982:INFO: kmodes: Not installed
2024-04-26 02:28:00,982:INFO: mlxtend: Not installed
2024-04-26 02:28:00,982:INFO: statsforecast: 1.4.0
2024-04-26 02:28:00,982:INFO: tune_sklearn: Not installed
2024-04-26 02:28:00,982:INFO: ray: 2.10.0
2024-04-26 02:28:00,982:INFO: hyperopt: 0.2.7
2024-04-26 02:28:00,982:INFO: optuna: 3.5.0
2024-04-26 02:28:00,982:INFO: skopt: Not installed
2024-04-26 02:28:00,982:INFO: mlflow: 2.10.2
2024-04-26 02:28:00,982:INFO: gradio: 3.48.0
2024-04-26 02:28:00,982:INFO: fastapi: 0.109.2
2024-04-26 02:28:00,982:INFO: uvicorn: 0.27.1
2024-04-26 02:28:00,982:INFO: m2cgen: Not installed
2024-04-26 02:28:00,982:INFO: evidently: Not installed
2024-04-26 02:28:00,982:INFO: fugue: Not installed
2024-04-26 02:28:00,982:INFO: streamlit: 1.27.2
2024-04-26 02:28:00,982:INFO: prophet: Not installed
2024-04-26 02:28:00,982:INFO:None
2024-04-26 02:28:00,982:INFO:Set up data.
2024-04-26 02:28:00,996:INFO:Set up folding strategy.
2024-04-26 02:28:00,996:INFO:Set up train/test split.
2024-04-26 02:28:01,004:INFO:Set up index.
2024-04-26 02:28:01,004:INFO:Assigning column types.
2024-04-26 02:28:01,007:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2024-04-26 02:28:01,038:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-26 02:28:01,042:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:28:01,071:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:01,073:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:01,104:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-26 02:28:01,105:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:28:01,122:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:01,124:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:01,124:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2024-04-26 02:28:01,154:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:28:01,171:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:01,173:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:01,203:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:28:01,221:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:01,222:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:01,224:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2024-04-26 02:28:01,270:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:01,272:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:01,320:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:01,321:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:01,325:INFO:Preparing preprocessing pipeline...
2024-04-26 02:28:01,326:INFO:Set up simple imputation.
2024-04-26 02:28:01,327:INFO:Set up column name cleaning.
2024-04-26 02:28:01,348:INFO:Finished creating preprocessing pipeline.
2024-04-26 02:28:01,352:INFO:Pipeline: Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Age_Group',
'MTRANS_Automobile', 'MTRANS_Bike',
'MTRANS_M...
('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'))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False)
2024-04-26 02:28:01,352:INFO:Creating final display dataframe.
2024-04-26 02:28:01,408:INFO:Setup _display_container: Description Value
0 Session id 8526
1 Target NObeyesdad
2 Target type Multiclass
3 Original data shape (10793, 35)
4 Transformed data shape (10793, 35)
5 Transformed train set shape (7555, 35)
6 Transformed test set shape (3238, 35)
7 Numeric features 34
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 370d
2024-04-26 02:28:01,461:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:01,462:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:01,512:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:01,513:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:01,516:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour.
warnings.warn(
2024-04-26 02:28:01,516:INFO:setup() successfully completed in 0.54s...............
2024-04-26 02:28:01,516:INFO:Initializing create_model()
2024-04-26 02:28:01,516:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x28324fa90>, estimator=dt, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:28:01,516:INFO:Checking exceptions
2024-04-26 02:28:01,529:INFO:Importing libraries
2024-04-26 02:28:01,530:INFO:Copying training dataset
2024-04-26 02:28:01,536:INFO:Defining folds
2024-04-26 02:28:01,536:INFO:Declaring metric variables
2024-04-26 02:28:01,538:INFO:Importing untrained model
2024-04-26 02:28:01,540:INFO:Decision Tree Classifier Imported successfully
2024-04-26 02:28:01,544:INFO:Starting cross validation
2024-04-26 02:28:01,545:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:28:08,711:INFO:Calculating mean and std
2024-04-26 02:28:08,715:INFO:Creating metrics dataframe
2024-04-26 02:28:08,727:INFO:Finalizing model
2024-04-26 02:28:08,919:INFO:Uploading results into container
2024-04-26 02:28:08,925:INFO:Uploading model into container now
2024-04-26 02:28:08,932:INFO:_master_model_container: 1
2024-04-26 02:28:08,932:INFO:_display_container: 2
2024-04-26 02:28:08,933: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=8526, splitter='best')
2024-04-26 02:28:08,933:INFO:create_model() successfully completed......................................
2024-04-26 02:28:09,793:INFO:Initializing predict_model()
2024-04-26 02:28:09,797:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x28324fa90>, estimator=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=8526, splitter='best'), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x142c7b400>)
2024-04-26 02:28:09,797:INFO:Checking exceptions
2024-04-26 02:28:09,797:INFO:Preloading libraries
2024-04-26 02:28:09,798:INFO:Set up data.
2024-04-26 02:28:09,805:INFO:Set up index.
2024-04-26 02:28:44,448:INFO:PyCaret ClassificationExperiment
2024-04-26 02:28:44,448:INFO:Logging name: clf-default-name
2024-04-26 02:28:44,448:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2024-04-26 02:28:44,448:INFO:version 3.3.0
2024-04-26 02:28:44,448:INFO:Initializing setup()
2024-04-26 02:28:44,448:INFO:self.USI: e3d8
2024-04-26 02:28:44,448:INFO:self._variable_keys: {'is_multiclass', 'gpu_n_jobs_param', 'html_param', 'y_test', 'fold_groups_param', 'y', 'memory', 'log_plots_param', 'exp_id', 'pipeline', 'fix_imbalance', '_ml_usecase', 'X', 'X_train', 'y_train', 'logging_param', 'seed', 'gpu_param', 'exp_name_log', '_available_plots', 'X_test', 'target_param', 'data', 'fold_generator', 'fold_shuffle_param', 'n_jobs_param', 'idx', 'USI'}
2024-04-26 02:28:44,448:INFO:Checking environment
2024-04-26 02:28:44,448:INFO:python_version: 3.10.13
2024-04-26 02:28:44,448:INFO:python_build: ('main', 'Sep 11 2023 08:16:02')
2024-04-26 02:28:44,448:INFO:machine: arm64
2024-04-26 02:28:44,449:INFO:platform: macOS-14.0-arm64-arm-64bit
2024-04-26 02:28:44,449:INFO:Memory: svmem(total=8589934592, available=1566359552, percent=81.8, used=3050110976, free=30228480, active=1551794176, inactive=1530576896, wired=1498316800)
2024-04-26 02:28:44,449:INFO:Physical Core: 8
2024-04-26 02:28:44,449:INFO:Logical Core: 8
2024-04-26 02:28:44,449:INFO:Checking libraries
2024-04-26 02:28:44,449:INFO:System:
2024-04-26 02:28:44,449:INFO: python: 3.10.13 (main, Sep 11 2023, 08:16:02) [Clang 14.0.6 ]
2024-04-26 02:28:44,449:INFO:executable: /Users/arham/anaconda3/envs/DataScience/bin/python
2024-04-26 02:28:44,449:INFO: machine: macOS-14.0-arm64-arm-64bit
2024-04-26 02:28:44,449:INFO:PyCaret required dependencies:
2024-04-26 02:28:44,449:INFO: pip: 23.3
2024-04-26 02:28:44,449:INFO: setuptools: 60.2.0
2024-04-26 02:28:44,449:INFO: pycaret: 3.3.0
2024-04-26 02:28:44,449:INFO: IPython: 8.15.0
2024-04-26 02:28:44,449:INFO: ipywidgets: 8.0.4
2024-04-26 02:28:44,449:INFO: tqdm: 4.65.2
2024-04-26 02:28:44,449:INFO: numpy: 1.25.2
2024-04-26 02:28:44,449:INFO: pandas: 1.5.3
2024-04-26 02:28:44,449:INFO: jinja2: 3.1.3
2024-04-26 02:28:44,449:INFO: scipy: 1.11.4
2024-04-26 02:28:44,449:INFO: joblib: 1.2.0
2024-04-26 02:28:44,449:INFO: sklearn: 1.4.0
2024-04-26 02:28:44,449:INFO: pyod: 1.1.3
2024-04-26 02:28:44,449:INFO: imblearn: 0.12.2
2024-04-26 02:28:44,449:INFO: category_encoders: 2.6.3
2024-04-26 02:28:44,449:INFO: lightgbm: 4.1.0
2024-04-26 02:28:44,449:INFO: numba: 0.58.1
2024-04-26 02:28:44,449:INFO: requests: 2.31.0
2024-04-26 02:28:44,449:INFO: matplotlib: 3.8.4
2024-04-26 02:28:44,449:INFO: scikitplot: 0.3.7
2024-04-26 02:28:44,449:INFO: yellowbrick: 1.5
2024-04-26 02:28:44,449:INFO: plotly: 5.17.0
2024-04-26 02:28:44,449:INFO: plotly-resampler: Not installed
2024-04-26 02:28:44,449:INFO: kaleido: 0.2.1
2024-04-26 02:28:44,449:INFO: schemdraw: 0.15
2024-04-26 02:28:44,449:INFO: statsmodels: 0.13.5
2024-04-26 02:28:44,450:INFO: sktime: 0.26.1
2024-04-26 02:28:44,450:INFO: tbats: 1.1.3
2024-04-26 02:28:44,450:INFO: pmdarima: 2.0.4
2024-04-26 02:28:44,450:INFO: psutil: 5.9.0
2024-04-26 02:28:44,450:INFO: markupsafe: 2.1.1
2024-04-26 02:28:44,450:INFO: pickle5: Not installed
2024-04-26 02:28:44,450:INFO: cloudpickle: 2.2.1
2024-04-26 02:28:44,450:INFO: deprecation: 2.1.0
2024-04-26 02:28:44,450:INFO: xxhash: 3.4.1
2024-04-26 02:28:44,450:INFO: wurlitzer: 3.0.2
2024-04-26 02:28:44,450:INFO:PyCaret optional dependencies:
2024-04-26 02:28:44,450:INFO: shap: 0.44.0
2024-04-26 02:28:44,450:INFO: interpret: Not installed
2024-04-26 02:28:44,450:INFO: umap: Not installed
2024-04-26 02:28:44,450:INFO: ydata_profiling: 0.0.dev0
2024-04-26 02:28:44,450:INFO: explainerdashboard: Not installed
2024-04-26 02:28:44,450:INFO: autoviz: Not installed
2024-04-26 02:28:44,450:INFO: fairlearn: Not installed
2024-04-26 02:28:44,450:INFO: deepchecks: Not installed
2024-04-26 02:28:44,450:INFO: xgboost: 1.7.3
2024-04-26 02:28:44,450:INFO: catboost: 1.1.1
2024-04-26 02:28:44,450:INFO: kmodes: Not installed
2024-04-26 02:28:44,450:INFO: mlxtend: Not installed
2024-04-26 02:28:44,450:INFO: statsforecast: 1.4.0
2024-04-26 02:28:44,450:INFO: tune_sklearn: Not installed
2024-04-26 02:28:44,450:INFO: ray: 2.10.0
2024-04-26 02:28:44,450:INFO: hyperopt: 0.2.7
2024-04-26 02:28:44,450:INFO: optuna: 3.5.0
2024-04-26 02:28:44,450:INFO: skopt: Not installed
2024-04-26 02:28:44,450:INFO: mlflow: 2.10.2
2024-04-26 02:28:44,450:INFO: gradio: 3.48.0
2024-04-26 02:28:44,450:INFO: fastapi: 0.109.2
2024-04-26 02:28:44,450:INFO: uvicorn: 0.27.1
2024-04-26 02:28:44,450:INFO: m2cgen: Not installed
2024-04-26 02:28:44,450:INFO: evidently: Not installed
2024-04-26 02:28:44,450:INFO: fugue: Not installed
2024-04-26 02:28:44,450:INFO: streamlit: 1.27.2
2024-04-26 02:28:44,450:INFO: prophet: Not installed
2024-04-26 02:28:44,450:INFO:None
2024-04-26 02:28:44,450:INFO:Set up data.
2024-04-26 02:28:44,460:INFO:Set up folding strategy.
2024-04-26 02:28:44,460:INFO:Set up train/test split.
2024-04-26 02:28:44,467:INFO:Set up index.
2024-04-26 02:28:44,467:INFO:Assigning column types.
2024-04-26 02:28:44,471:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2024-04-26 02:28:44,500:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-26 02:28:44,501:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:28:44,523:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:44,526:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:44,557:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-26 02:28:44,557:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:28:44,575:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:44,577:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:44,577:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2024-04-26 02:28:44,607:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:28:44,625:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:44,626:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:44,656:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:28:44,674:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:44,675:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:44,676:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2024-04-26 02:28:44,723:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:44,725:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:44,773:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:44,775:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:44,776:INFO:Preparing preprocessing pipeline...
2024-04-26 02:28:44,777:INFO:Set up simple imputation.
2024-04-26 02:28:44,778:INFO:Set up column name cleaning.
2024-04-26 02:28:44,797:INFO:Finished creating preprocessing pipeline.
2024-04-26 02:28:44,800:INFO:Pipeline: Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Age_Group',
'MTRANS_Automobile', 'MTRANS_Bike',
'MTRANS_M...
('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'))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False)
2024-04-26 02:28:44,800:INFO:Creating final display dataframe.
2024-04-26 02:28:44,853:INFO:Setup _display_container: Description Value
0 Session id 8111
1 Target NObeyesdad
2 Target type Multiclass
3 Original data shape (10793, 35)
4 Transformed data shape (10793, 35)
5 Transformed train set shape (7555, 35)
6 Transformed test set shape (3238, 35)
7 Numeric features 34
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 e3d8
2024-04-26 02:28:44,965:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:44,967:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:45,015:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:28:45,018:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:28:45,019:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour.
warnings.warn(
2024-04-26 02:28:45,019:INFO:setup() successfully completed in 0.58s...............
2024-04-26 02:28:45,020:INFO:Initializing create_model()
2024-04-26 02:28:45,020:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x1506420b0>, estimator=dt, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:28:45,020:INFO:Checking exceptions
2024-04-26 02:28:45,030:INFO:Importing libraries
2024-04-26 02:28:45,030:INFO:Copying training dataset
2024-04-26 02:28:45,036:INFO:Defining folds
2024-04-26 02:28:45,037:INFO:Declaring metric variables
2024-04-26 02:28:45,039:INFO:Importing untrained model
2024-04-26 02:28:45,041:INFO:Decision Tree Classifier Imported successfully
2024-04-26 02:28:45,045:INFO:Starting cross validation
2024-04-26 02:28:45,045:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:28:45,442:INFO:Calculating mean and std
2024-04-26 02:28:45,443:INFO:Creating metrics dataframe
2024-04-26 02:28:45,446:INFO:Finalizing model
2024-04-26 02:28:45,599:INFO:Uploading results into container
2024-04-26 02:28:45,600:INFO:Uploading model into container now
2024-04-26 02:28:45,607:INFO:_master_model_container: 1
2024-04-26 02:28:45,607:INFO:_display_container: 2
2024-04-26 02:28:45,608: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=8111, splitter='best')
2024-04-26 02:28:45,608:INFO:create_model() successfully completed......................................
2024-04-26 02:28:45,954:INFO:Initializing predict_model()
2024-04-26 02:28:45,954:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x1506420b0>, estimator=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=8111, splitter='best'), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x142c36830>)
2024-04-26 02:28:45,954:INFO:Checking exceptions
2024-04-26 02:28:45,954:INFO:Preloading libraries
2024-04-26 02:28:45,955:INFO:Set up data.
2024-04-26 02:28:45,964:INFO:Set up index.
2024-04-26 02:29:21,714:INFO:PyCaret ClassificationExperiment
2024-04-26 02:29:21,714:INFO:Logging name: clf-default-name
2024-04-26 02:29:21,714:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2024-04-26 02:29:21,714:INFO:version 3.3.0
2024-04-26 02:29:21,714:INFO:Initializing setup()
2024-04-26 02:29:21,714:INFO:self.USI: e779
2024-04-26 02:29:21,714:INFO:self._variable_keys: {'is_multiclass', 'gpu_n_jobs_param', 'html_param', 'y_test', 'fold_groups_param', 'y', 'memory', 'log_plots_param', 'exp_id', 'pipeline', 'fix_imbalance', '_ml_usecase', 'X', 'X_train', 'y_train', 'logging_param', 'seed', 'gpu_param', 'exp_name_log', '_available_plots', 'X_test', 'target_param', 'data', 'fold_generator', 'fold_shuffle_param', 'n_jobs_param', 'idx', 'USI'}
2024-04-26 02:29:21,714:INFO:Checking environment
2024-04-26 02:29:21,714:INFO:python_version: 3.10.13
2024-04-26 02:29:21,714:INFO:python_build: ('main', 'Sep 11 2023 08:16:02')
2024-04-26 02:29:21,714:INFO:machine: arm64
2024-04-26 02:29:21,715:INFO:platform: macOS-14.0-arm64-arm-64bit
2024-04-26 02:29:21,715:INFO:Memory: svmem(total=8589934592, available=1578631168, percent=81.6, used=3034644480, free=57688064, active=1539538944, inactive=1501822976, wired=1495105536)
2024-04-26 02:29:21,715:INFO:Physical Core: 8
2024-04-26 02:29:21,715:INFO:Logical Core: 8
2024-04-26 02:29:21,715:INFO:Checking libraries
2024-04-26 02:29:21,715:INFO:System:
2024-04-26 02:29:21,715:INFO: python: 3.10.13 (main, Sep 11 2023, 08:16:02) [Clang 14.0.6 ]
2024-04-26 02:29:21,715:INFO:executable: /Users/arham/anaconda3/envs/DataScience/bin/python
2024-04-26 02:29:21,715:INFO: machine: macOS-14.0-arm64-arm-64bit
2024-04-26 02:29:21,715:INFO:PyCaret required dependencies:
2024-04-26 02:29:21,715:INFO: pip: 23.3
2024-04-26 02:29:21,715:INFO: setuptools: 60.2.0
2024-04-26 02:29:21,715:INFO: pycaret: 3.3.0
2024-04-26 02:29:21,715:INFO: IPython: 8.15.0
2024-04-26 02:29:21,715:INFO: ipywidgets: 8.0.4
2024-04-26 02:29:21,715:INFO: tqdm: 4.65.2
2024-04-26 02:29:21,715:INFO: numpy: 1.25.2
2024-04-26 02:29:21,715:INFO: pandas: 1.5.3
2024-04-26 02:29:21,715:INFO: jinja2: 3.1.3
2024-04-26 02:29:21,715:INFO: scipy: 1.11.4
2024-04-26 02:29:21,715:INFO: joblib: 1.2.0
2024-04-26 02:29:21,715:INFO: sklearn: 1.4.0
2024-04-26 02:29:21,715:INFO: pyod: 1.1.3
2024-04-26 02:29:21,715:INFO: imblearn: 0.12.2
2024-04-26 02:29:21,715:INFO: category_encoders: 2.6.3
2024-04-26 02:29:21,715:INFO: lightgbm: 4.1.0
2024-04-26 02:29:21,715:INFO: numba: 0.58.1
2024-04-26 02:29:21,716:INFO: requests: 2.31.0
2024-04-26 02:29:21,716:INFO: matplotlib: 3.8.4
2024-04-26 02:29:21,716:INFO: scikitplot: 0.3.7
2024-04-26 02:29:21,716:INFO: yellowbrick: 1.5
2024-04-26 02:29:21,716:INFO: plotly: 5.17.0
2024-04-26 02:29:21,716:INFO: plotly-resampler: Not installed
2024-04-26 02:29:21,716:INFO: kaleido: 0.2.1
2024-04-26 02:29:21,716:INFO: schemdraw: 0.15
2024-04-26 02:29:21,716:INFO: statsmodels: 0.13.5
2024-04-26 02:29:21,716:INFO: sktime: 0.26.1
2024-04-26 02:29:21,716:INFO: tbats: 1.1.3
2024-04-26 02:29:21,716:INFO: pmdarima: 2.0.4
2024-04-26 02:29:21,716:INFO: psutil: 5.9.0
2024-04-26 02:29:21,716:INFO: markupsafe: 2.1.1
2024-04-26 02:29:21,716:INFO: pickle5: Not installed
2024-04-26 02:29:21,716:INFO: cloudpickle: 2.2.1
2024-04-26 02:29:21,716:INFO: deprecation: 2.1.0
2024-04-26 02:29:21,716:INFO: xxhash: 3.4.1
2024-04-26 02:29:21,716:INFO: wurlitzer: 3.0.2
2024-04-26 02:29:21,716:INFO:PyCaret optional dependencies:
2024-04-26 02:29:21,716:INFO: shap: 0.44.0
2024-04-26 02:29:21,716:INFO: interpret: Not installed
2024-04-26 02:29:21,716:INFO: umap: Not installed
2024-04-26 02:29:21,716:INFO: ydata_profiling: 0.0.dev0
2024-04-26 02:29:21,716:INFO: explainerdashboard: Not installed
2024-04-26 02:29:21,716:INFO: autoviz: Not installed
2024-04-26 02:29:21,716:INFO: fairlearn: Not installed
2024-04-26 02:29:21,716:INFO: deepchecks: Not installed
2024-04-26 02:29:21,716:INFO: xgboost: 1.7.3
2024-04-26 02:29:21,716:INFO: catboost: 1.1.1
2024-04-26 02:29:21,716:INFO: kmodes: Not installed
2024-04-26 02:29:21,716:INFO: mlxtend: Not installed
2024-04-26 02:29:21,716:INFO: statsforecast: 1.4.0
2024-04-26 02:29:21,716:INFO: tune_sklearn: Not installed
2024-04-26 02:29:21,716:INFO: ray: 2.10.0
2024-04-26 02:29:21,716:INFO: hyperopt: 0.2.7
2024-04-26 02:29:21,716:INFO: optuna: 3.5.0
2024-04-26 02:29:21,716:INFO: skopt: Not installed
2024-04-26 02:29:21,716:INFO: mlflow: 2.10.2
2024-04-26 02:29:21,716:INFO: gradio: 3.48.0
2024-04-26 02:29:21,717:INFO: fastapi: 0.109.2
2024-04-26 02:29:21,717:INFO: uvicorn: 0.27.1
2024-04-26 02:29:21,717:INFO: m2cgen: Not installed
2024-04-26 02:29:21,717:INFO: evidently: Not installed
2024-04-26 02:29:21,717:INFO: fugue: Not installed
2024-04-26 02:29:21,717:INFO: streamlit: 1.27.2
2024-04-26 02:29:21,717:INFO: prophet: Not installed
2024-04-26 02:29:21,717:INFO:None
2024-04-26 02:29:21,717:INFO:Set up data.
2024-04-26 02:29:21,728:INFO:Set up folding strategy.
2024-04-26 02:29:21,728:INFO:Set up train/test split.
2024-04-26 02:29:21,736:INFO:Set up index.
2024-04-26 02:29:21,736:INFO:Assigning column types.
2024-04-26 02:29:21,740:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2024-04-26 02:29:21,769:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-26 02:29:21,770:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:29:21,789:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:29:21,791:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:29:21,821:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-26 02:29:21,822:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:29:21,840:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:29:21,841:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:29:21,842:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2024-04-26 02:29:21,871:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:29:21,890:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:29:21,892:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:29:21,922:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:29:21,940:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:29:21,942:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:29:21,942:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2024-04-26 02:29:21,990:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:29:21,991:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:29:22,039:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:29:22,041:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:29:22,042:INFO:Preparing preprocessing pipeline...
2024-04-26 02:29:22,043:INFO:Set up simple imputation.
2024-04-26 02:29:22,044:INFO:Set up column name cleaning.
2024-04-26 02:29:22,062:INFO:Finished creating preprocessing pipeline.
2024-04-26 02:29:22,065:INFO:Pipeline: Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Age_Group',
'MTRANS_Automobile', 'MTRANS_Bike',
'MTRANS_M...
('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'))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False)
2024-04-26 02:29:22,065:INFO:Creating final display dataframe.
2024-04-26 02:29:22,118:INFO:Setup _display_container: Description Value
0 Session id 6784
1 Target NObeyesdad
2 Target type Multiclass
3 Original data shape (10793, 35)
4 Transformed data shape (10793, 35)
5 Transformed train set shape (7555, 35)
6 Transformed test set shape (3238, 35)
7 Numeric features 34
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 e779
2024-04-26 02:29:22,174:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:29:22,176:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:29:22,226:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:29:22,228:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:29:22,229:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour.
warnings.warn(
2024-04-26 02:29:22,229:INFO:setup() successfully completed in 0.52s...............
2024-04-26 02:29:22,230:INFO:Initializing create_model()
2024-04-26 02:29:22,230:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x151ac0550>, estimator=dt, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:29:22,230:INFO:Checking exceptions
2024-04-26 02:29:22,240:INFO:Importing libraries
2024-04-26 02:29:22,240:INFO:Copying training dataset
2024-04-26 02:29:22,246:INFO:Defining folds
2024-04-26 02:29:22,247:INFO:Declaring metric variables
2024-04-26 02:29:22,249:INFO:Importing untrained model
2024-04-26 02:29:22,251:INFO:Decision Tree Classifier Imported successfully
2024-04-26 02:29:22,254:INFO:Starting cross validation
2024-04-26 02:29:22,255:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:29:22,835:INFO:Calculating mean and std
2024-04-26 02:29:22,835:INFO:Creating metrics dataframe
2024-04-26 02:29:22,838:INFO:Finalizing model
2024-04-26 02:29:22,984:INFO:Uploading results into container
2024-04-26 02:29:22,985:INFO:Uploading model into container now
2024-04-26 02:29:22,991:INFO:_master_model_container: 1
2024-04-26 02:29:22,992:INFO:_display_container: 2
2024-04-26 02:29:22,992: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=6784, splitter='best')
2024-04-26 02:29:22,992:INFO:create_model() successfully completed......................................
2024-04-26 02:29:23,320:INFO:Initializing predict_model()
2024-04-26 02:29:23,321:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x151ac0550>, estimator=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=6784, splitter='best'), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x14267de10>)
2024-04-26 02:29:23,321:INFO:Checking exceptions
2024-04-26 02:29:23,321:INFO:Preloading libraries
2024-04-26 02:29:23,322:INFO:Set up data.
2024-04-26 02:29:23,329:INFO:Set up index.
2024-04-26 02:30:42,928:INFO:PyCaret ClassificationExperiment
2024-04-26 02:30:42,929:INFO:Logging name: clf-default-name
2024-04-26 02:30:42,929:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2024-04-26 02:30:42,929:INFO:version 3.3.0
2024-04-26 02:30:42,929:INFO:Initializing setup()
2024-04-26 02:30:42,929:INFO:self.USI: 2ccb
2024-04-26 02:30:42,929:INFO:self._variable_keys: {'is_multiclass', 'gpu_n_jobs_param', 'html_param', 'y_test', 'fold_groups_param', 'y', 'memory', 'log_plots_param', 'exp_id', 'pipeline', 'fix_imbalance', '_ml_usecase', 'X', 'X_train', 'y_train', 'logging_param', 'seed', 'gpu_param', 'exp_name_log', '_available_plots', 'X_test', 'target_param', 'data', 'fold_generator', 'fold_shuffle_param', 'n_jobs_param', 'idx', 'USI'}
2024-04-26 02:30:42,929:INFO:Checking environment
2024-04-26 02:30:42,929:INFO:python_version: 3.10.13
2024-04-26 02:30:42,929:INFO:python_build: ('main', 'Sep 11 2023 08:16:02')
2024-04-26 02:30:42,929:INFO:machine: arm64
2024-04-26 02:30:42,929:INFO:platform: macOS-14.0-arm64-arm-64bit
2024-04-26 02:30:42,929:INFO:Memory: svmem(total=8589934592, available=1533837312, percent=82.1, used=2957246464, free=44580864, active=1505312768, inactive=1487241216, wired=1451933696)
2024-04-26 02:30:42,929:INFO:Physical Core: 8
2024-04-26 02:30:42,929:INFO:Logical Core: 8
2024-04-26 02:30:42,929:INFO:Checking libraries
2024-04-26 02:30:42,929:INFO:System:
2024-04-26 02:30:42,929:INFO: python: 3.10.13 (main, Sep 11 2023, 08:16:02) [Clang 14.0.6 ]
2024-04-26 02:30:42,929:INFO:executable: /Users/arham/anaconda3/envs/DataScience/bin/python
2024-04-26 02:30:42,929:INFO: machine: macOS-14.0-arm64-arm-64bit
2024-04-26 02:30:42,929:INFO:PyCaret required dependencies:
2024-04-26 02:30:42,930:INFO: pip: 23.3
2024-04-26 02:30:42,930:INFO: setuptools: 60.2.0
2024-04-26 02:30:42,930:INFO: pycaret: 3.3.0
2024-04-26 02:30:42,930:INFO: IPython: 8.15.0
2024-04-26 02:30:42,930:INFO: ipywidgets: 8.0.4
2024-04-26 02:30:42,930:INFO: tqdm: 4.65.2
2024-04-26 02:30:42,930:INFO: numpy: 1.25.2
2024-04-26 02:30:42,930:INFO: pandas: 1.5.3
2024-04-26 02:30:42,930:INFO: jinja2: 3.1.3
2024-04-26 02:30:42,930:INFO: scipy: 1.11.4
2024-04-26 02:30:42,930:INFO: joblib: 1.2.0
2024-04-26 02:30:42,930:INFO: sklearn: 1.4.0
2024-04-26 02:30:42,930:INFO: pyod: 1.1.3
2024-04-26 02:30:42,930:INFO: imblearn: 0.12.2
2024-04-26 02:30:42,930:INFO: category_encoders: 2.6.3
2024-04-26 02:30:42,930:INFO: lightgbm: 4.1.0
2024-04-26 02:30:42,930:INFO: numba: 0.58.1
2024-04-26 02:30:42,930:INFO: requests: 2.31.0
2024-04-26 02:30:42,930:INFO: matplotlib: 3.8.4
2024-04-26 02:30:42,930:INFO: scikitplot: 0.3.7
2024-04-26 02:30:42,930:INFO: yellowbrick: 1.5
2024-04-26 02:30:42,930:INFO: plotly: 5.17.0
2024-04-26 02:30:42,930:INFO: plotly-resampler: Not installed
2024-04-26 02:30:42,930:INFO: kaleido: 0.2.1
2024-04-26 02:30:42,930:INFO: schemdraw: 0.15
2024-04-26 02:30:42,930:INFO: statsmodels: 0.13.5
2024-04-26 02:30:42,930:INFO: sktime: 0.26.1
2024-04-26 02:30:42,930:INFO: tbats: 1.1.3
2024-04-26 02:30:42,930:INFO: pmdarima: 2.0.4
2024-04-26 02:30:42,930:INFO: psutil: 5.9.0
2024-04-26 02:30:42,930:INFO: markupsafe: 2.1.1
2024-04-26 02:30:42,930:INFO: pickle5: Not installed
2024-04-26 02:30:42,930:INFO: cloudpickle: 2.2.1
2024-04-26 02:30:42,930:INFO: deprecation: 2.1.0
2024-04-26 02:30:42,930:INFO: xxhash: 3.4.1
2024-04-26 02:30:42,930:INFO: wurlitzer: 3.0.2
2024-04-26 02:30:42,930:INFO:PyCaret optional dependencies:
2024-04-26 02:30:42,930:INFO: shap: 0.44.0
2024-04-26 02:30:42,930:INFO: interpret: Not installed
2024-04-26 02:30:42,930:INFO: umap: Not installed
2024-04-26 02:30:42,930:INFO: ydata_profiling: 0.0.dev0
2024-04-26 02:30:42,930:INFO: explainerdashboard: Not installed
2024-04-26 02:30:42,930:INFO: autoviz: Not installed
2024-04-26 02:30:42,931:INFO: fairlearn: Not installed
2024-04-26 02:30:42,931:INFO: deepchecks: Not installed
2024-04-26 02:30:42,931:INFO: xgboost: 1.7.3
2024-04-26 02:30:42,931:INFO: catboost: 1.1.1
2024-04-26 02:30:42,931:INFO: kmodes: Not installed
2024-04-26 02:30:42,931:INFO: mlxtend: Not installed
2024-04-26 02:30:42,931:INFO: statsforecast: 1.4.0
2024-04-26 02:30:42,931:INFO: tune_sklearn: Not installed
2024-04-26 02:30:42,931:INFO: ray: 2.10.0
2024-04-26 02:30:42,931:INFO: hyperopt: 0.2.7
2024-04-26 02:30:42,931:INFO: optuna: 3.5.0
2024-04-26 02:30:42,931:INFO: skopt: Not installed
2024-04-26 02:30:42,931:INFO: mlflow: 2.10.2
2024-04-26 02:30:42,931:INFO: gradio: 3.48.0
2024-04-26 02:30:42,931:INFO: fastapi: 0.109.2
2024-04-26 02:30:42,931:INFO: uvicorn: 0.27.1
2024-04-26 02:30:42,931:INFO: m2cgen: Not installed
2024-04-26 02:30:42,931:INFO: evidently: Not installed
2024-04-26 02:30:42,931:INFO: fugue: Not installed
2024-04-26 02:30:42,931:INFO: streamlit: 1.27.2
2024-04-26 02:30:42,931:INFO: prophet: Not installed
2024-04-26 02:30:42,931:INFO:None
2024-04-26 02:30:42,931:INFO:Set up data.
2024-04-26 02:30:42,941:INFO:Set up folding strategy.
2024-04-26 02:30:42,941:INFO:Set up train/test split.
2024-04-26 02:30:42,950:INFO:Set up index.
2024-04-26 02:30:42,951:INFO:Assigning column types.
2024-04-26 02:30:42,954:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2024-04-26 02:30:42,983:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-26 02:30:42,984:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:30:43,003:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:30:43,005:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:30:43,036:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-04-26 02:30:43,036:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:30:43,055:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:30:43,057:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:30:43,057:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2024-04-26 02:30:43,086:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:30:43,104:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:30:43,107:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:30:43,137:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-04-26 02:30:43,155:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:30:43,157:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:30:43,157:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2024-04-26 02:30:43,204:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:30:43,206:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:30:43,255:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:30:43,257:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:30:43,258:INFO:Preparing preprocessing pipeline...
2024-04-26 02:30:43,259:INFO:Set up simple imputation.
2024-04-26 02:30:43,260:INFO:Set up column name cleaning.
2024-04-26 02:30:43,279:INFO:Finished creating preprocessing pipeline.
2024-04-26 02:30:43,282:INFO:Pipeline: Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'Age_Group',
'MTRANS_Automobile', 'MTRANS_Bike',
'MTRANS_M...
('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'))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+')))],
verbose=False)
2024-04-26 02:30:43,282:INFO:Creating final display dataframe.
2024-04-26 02:30:43,335:INFO:Setup _display_container: Description Value
0 Session id 5119
1 Target NObeyesdad
2 Target type Multiclass
3 Original data shape (10793, 35)
4 Transformed data shape (10793, 35)
5 Transformed train set shape (7555, 35)
6 Transformed test set shape (3238, 35)
7 Numeric features 34
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 2ccb
2024-04-26 02:30:43,388:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:30:43,390:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:30:43,439:INFO:Soft dependency imported: xgboost: 1.7.3
2024-04-26 02:30:43,440:INFO:Soft dependency imported: catboost: 1.1.1
2024-04-26 02:30:43,441:WARNING:/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pycaret/internal/metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour.
warnings.warn(
2024-04-26 02:30:43,442:INFO:setup() successfully completed in 0.52s...............
2024-04-26 02:30:43,443:INFO:Initializing create_model()
2024-04-26 02:30:43,443:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x153080bb0>, estimator=dt, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-04-26 02:30:43,443:INFO:Checking exceptions
2024-04-26 02:30:43,453:INFO:Importing libraries
2024-04-26 02:30:43,454:INFO:Copying training dataset
2024-04-26 02:30:43,458:INFO:Defining folds
2024-04-26 02:30:43,458:INFO:Declaring metric variables
2024-04-26 02:30:43,460:INFO:Importing untrained model
2024-04-26 02:30:43,462:INFO:Decision Tree Classifier Imported successfully
2024-04-26 02:30:43,466:INFO:Starting cross validation
2024-04-26 02:30:43,467:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-04-26 02:30:43,857:INFO:Calculating mean and std
2024-04-26 02:30:43,857:INFO:Creating metrics dataframe
2024-04-26 02:30:43,861:INFO:Finalizing model
2024-04-26 02:30:44,007:INFO:Uploading results into container
2024-04-26 02:30:44,008:INFO:Uploading model into container now
2024-04-26 02:30:44,014:INFO:_master_model_container: 1
2024-04-26 02:30:44,014:INFO:_display_container: 2
2024-04-26 02:30:44,014: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=5119, splitter='best')
2024-04-26 02:30:44,014:INFO:create_model() successfully completed......................................
2024-04-26 02:30:44,349:INFO:Initializing predict_model()
2024-04-26 02:30:44,349:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x153080bb0>, estimator=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=5119, splitter='best'), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x140bde830>)
2024-04-26 02:30:44,349:INFO:Checking exceptions
2024-04-26 02:30:44,349:INFO:Preloading libraries
2024-04-26 02:30:44,351:INFO:Set up data.
2024-04-26 02:30:44,358:INFO:Set up index.
2024-04-26 02:33:56,632:INFO:Initializing load_model()
2024-04-26 02:33:56,634:INFO:load_model(model_name=model_name, platform=None, authentication=None, verbose=True)
2024-04-26 02:33:56,663:INFO:Initializing predict_model()
2024-04-26 02:33:56,663:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'MTRANS_Public_Transportation',
'MTRANS_Walking', 'BMI', 'Ag...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x28a8ac190>)],
verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x2844cb2e0>)
2024-04-26 02:33:56,663:INFO:Checking exceptions
2024-04-26 02:33:56,663:INFO:Preloading libraries
2024-04-26 02:33:56,666:INFO:Set up data.
2024-04-26 02:35:01,437:INFO:Initializing load_model()
2024-04-26 02:35:01,438:INFO:load_model(model_name=model_name, platform=None, authentication=None, verbose=True)
2024-04-26 02:35:01,457:INFO:Initializing predict_model()
2024-04-26 02:35:01,457:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'MTRANS_Public_Transportation',
'MTRANS_Walking', 'BMI', 'Ag...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x28a8ac190>)],
verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x289e1eef0>)
2024-04-26 02:35:01,457:INFO:Checking exceptions
2024-04-26 02:35:01,457:INFO:Preloading libraries
2024-04-26 02:35:01,465:INFO:Set up data.
2024-04-26 02:35:01,476:INFO:Set up index.
2024-04-26 02:35:55,263:INFO:Initializing load_model()
2024-04-26 02:35:55,265:INFO:load_model(model_name=model_name, platform=None, authentication=None, verbose=True)
2024-04-26 02:35:55,283:INFO:Initializing predict_model()
2024-04-26 02:35:55,283:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'MTRANS_Public_Transportation',
'MTRANS_Walking', 'BMI', 'Ag...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x28a8ac190>)],
verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x289cfa830>)
2024-04-26 02:35:55,283:INFO:Checking exceptions
2024-04-26 02:35:55,283:INFO:Preloading libraries
2024-04-26 02:35:55,286:INFO:Set up data.
2024-04-26 02:35:55,297:INFO:Set up index.
2024-04-26 02:36:09,484:INFO:Initializing load_model()
2024-04-26 02:36:09,485:INFO:load_model(model_name=model_name, platform=None, authentication=None, verbose=True)
2024-04-26 02:36:09,497:INFO:Initializing predict_model()
2024-04-26 02:36:09,497:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'MTRANS_Public_Transportation',
'MTRANS_Walking', 'BMI', 'Ag...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x28a8ac190>)],
verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x28f887490>)
2024-04-26 02:36:09,497:INFO:Checking exceptions
2024-04-26 02:36:09,497:INFO:Preloading libraries
2024-04-26 02:36:09,499:INFO:Set up data.
2024-04-26 02:36:09,509:INFO:Set up index.
2024-04-26 02:36:47,114:INFO:Initializing load_model()
2024-04-26 02:36:47,115:INFO:load_model(model_name=model_name, platform=None, authentication=None, verbose=True)
2024-04-26 02:36:47,130:INFO:Initializing predict_model()
2024-04-26 02:36:47,130:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'MTRANS_Public_Transportation',
'MTRANS_Walking', 'BMI', 'Ag...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x28a8ac190>)],
verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x289e1eef0>)
2024-04-26 02:36:47,130:INFO:Checking exceptions
2024-04-26 02:36:47,130:INFO:Preloading libraries
2024-04-26 02:36:47,132:INFO:Set up data.
2024-04-26 02:36:47,142:INFO:Set up index.
2024-04-26 02:37:00,645:INFO:Initializing load_model()
2024-04-26 02:37:00,646:INFO:load_model(model_name=model_name, platform=None, authentication=None, verbose=True)
2024-04-26 02:37:00,656:INFO:Initializing predict_model()
2024-04-26 02:37:00,656:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'MTRANS_Public_Transportation',
'MTRANS_Walking', 'BMI', 'Ag...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x28a8ac190>)],
verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x2844cb2e0>)
2024-04-26 02:37:00,656:INFO:Checking exceptions
2024-04-26 02:37:00,656:INFO:Preloading libraries
2024-04-26 02:37:00,657:INFO:Set up data.
2024-04-26 02:37:00,664:INFO:Set up index.
2024-04-26 02:37:09,800:INFO:Initializing load_model()
2024-04-26 02:37:09,801:INFO:load_model(model_name=model_name, platform=None, authentication=None, verbose=True)
2024-04-26 02:37:09,811:INFO:Initializing predict_model()
2024-04-26 02:37:09,811:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'MTRANS_Public_Transportation',
'MTRANS_Walking', 'BMI', 'Ag...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x28a8ac190>)],
verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x297fe4310>)
2024-04-26 02:37:09,811:INFO:Checking exceptions
2024-04-26 02:37:09,811:INFO:Preloading libraries
2024-04-26 02:37:09,813:INFO:Set up data.
2024-04-26 02:37:09,820:INFO:Set up index.
2024-04-26 02:37:59,872:INFO:Initializing load_model()
2024-04-26 02:37:59,874:INFO:load_model(model_name=model_name, platform=None, authentication=None, verbose=True)
2024-04-26 02:37:59,887:INFO:Initializing predict_model()
2024-04-26 02:37:59,887:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'M...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x299c94fa0>)],
verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x28f8868c0>)
2024-04-26 02:37:59,887:INFO:Checking exceptions
2024-04-26 02:37:59,887:INFO:Preloading libraries
2024-04-26 02:37:59,889:INFO:Set up data.
2024-04-26 02:37:59,899:INFO:Set up index.
2024-04-26 02:38:39,722:INFO:Initializing load_model()
2024-04-26 02:38:39,723:INFO:load_model(model_name=model_name, platform=None, authentication=None, verbose=True)
2024-04-26 02:38:39,732:INFO:Initializing predict_model()
2024-04-26 02:38:39,733:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x289b52b00>, estimator=Pipeline(memory=FastMemory(location=/var/folders/2t/c7s0z0zs4698zw0k9pj4f2r80000gn/T/joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None,
include=['Gender', 'Age', 'Height',
'Weight',
'family_history_with_overweight',
'FAVC', 'FCVC', 'NCP', 'CAEC',
'SMOKE', 'CH2O', 'SCC', 'FAF',
'TUE', 'CALC', 'MTRANS_Automobile',
'MTRANS_Bike', 'MTRANS_Motorbike',
'M...
transformer=OneHotEncoder(cols=['Age_Group'],
drop_invariant=False,
handle_missing='return_nan',
handle_unknown='value',
return_df=True,
use_cat_names=True,
verbose=0))),
('clean_column_names',
TransformerWrapper(exclude=None, include=None,
transformer=CleanColumnNames(match='[\\]\\[\\,\\{\\}\\"\\:]+'))),
('actual_estimator',
<catboost.core.CatBoostClassifier object at 0x299c95960>)],
verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x29985fbe0>)
2024-04-26 02:38:39,733:INFO:Checking exceptions
2024-04-26 02:38:39,733:INFO:Preloading libraries
2024-04-26 02:38:39,734:INFO:Set up data.
2024-04-26 02:38:39,741:INFO:Set up index.