2024-04-20 12:40:35,514:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:40:35,515:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:40:35,515:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:40:35,515:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:40:45,594:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:40:45,597:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:40:45,597:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:40:45,597:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:14,455:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:14,455:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:14,455:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:14,455:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:18,060:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:18,060:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:18,061:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:18,061:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:26,630:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:26,630:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:26,631:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:26,631:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:30,399:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:30,399:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:30,400:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:30,400:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:35,282:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:35,282:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:35,282:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:35,282:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:45,549:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:45,549:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:45,549:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:41:45,549:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:48:56,783:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:48:56,783:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:48:56,783:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:48:56,783:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:05,428:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:05,428:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:05,428:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:05,428:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:08,671:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:08,671:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:08,671:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:08,671:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:12,029:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:12,029:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:12,029:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:12,029:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:15,216:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:15,217:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:15,217:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:15,217:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:26,034:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:26,034:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:26,034:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:26,034:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:43,943:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:43,943:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:43,943:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:43,943:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:49,276:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:49,276:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:49,276:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:49,276:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:57,281:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:57,281:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:57,281:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:50:57,281:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:03,436:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:03,436:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:03,436:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:03,436:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:06,777:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:06,778:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:06,778:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:06,778:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:10,067:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:10,068:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:10,068:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:10,068:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:13,332:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:13,332:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:13,332:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:13,333:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:21,411:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:21,412:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:21,412:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:21,412:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:35,768:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:35,769:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:35,769:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:35,769:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:39,224:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:39,224:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:39,224:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:51:39,224:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:52:00,379:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:52:00,379:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:52:00,379:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:52:00,379:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 12:52:15,997:INFO:PyCaret ClassificationExperiment 2024-04-20 12:52:15,997:INFO:Logging name: clf-default-name 2024-04-20 12:52:15,997:INFO:ML Usecase: MLUsecase.CLASSIFICATION 2024-04-20 12:52:15,997:INFO:version 3.3.0 2024-04-20 12:52:15,997:INFO:Initializing setup() 2024-04-20 12:52:15,997:INFO:self.USI: 5ebc 2024-04-20 12:52:15,997:INFO:self._variable_keys: {'_ml_usecase', 'idx', 'X', 'gpu_n_jobs_param', 'X_train', 'is_multiclass', 'target_param', 'fold_groups_param', 'html_param', 'pipeline', 'exp_name_log', 'fold_shuffle_param', 'logging_param', 'exp_id', 'data', 'USI', 'y_train', 'n_jobs_param', 'fold_generator', 'y_test', '_available_plots', 'fix_imbalance', 'y', 'X_test', 'memory', 'seed', 'gpu_param', 'log_plots_param'} 2024-04-20 12:52:15,997:INFO:Checking environment 2024-04-20 12:52:15,997:INFO:python_version: 3.11.5 2024-04-20 12:52:15,998:INFO:python_build: ('main', 'Sep 11 2023 13:26:23') 2024-04-20 12:52:15,998:INFO:machine: AMD64 2024-04-20 12:52:16,013:INFO:platform: Windows-10-10.0.22631-SP0 2024-04-20 12:52:16,018:INFO:Memory: svmem(total=16782184448, available=4099518464, percent=75.6, used=12682665984, free=4099518464) 2024-04-20 12:52:16,018:INFO:Physical Core: 10 2024-04-20 12:52:16,018:INFO:Logical Core: 16 2024-04-20 12:52:16,018:INFO:Checking libraries 2024-04-20 12:52:16,018:INFO:System: 2024-04-20 12:52:16,018:INFO: python: 3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:26:23) [MSC v.1916 64 bit (AMD64)] 2024-04-20 12:52:16,018:INFO:executable: C:\Users\arpit\anaconda3\envs\arpit-test\python.exe 2024-04-20 12:52:16,019:INFO: machine: Windows-10-10.0.22631-SP0 2024-04-20 12:52:16,019:INFO:PyCaret required dependencies: 2024-04-20 12:52:16,080:INFO: pip: 23.3 2024-04-20 12:52:16,080:INFO: setuptools: 68.0.0 2024-04-20 12:52:16,080:INFO: pycaret: 3.3.0 2024-04-20 12:52:16,080:INFO: IPython: 8.16.1 2024-04-20 12:52:16,080:INFO: ipywidgets: 8.1.2 2024-04-20 12:52:16,080:INFO: tqdm: 4.66.1 2024-04-20 12:52:16,080:INFO: numpy: 1.23.5 2024-04-20 12:52:16,080:INFO: pandas: 1.5.3 2024-04-20 12:52:16,080:INFO: jinja2: 3.1.2 2024-04-20 12:52:16,080:INFO: scipy: 1.11.4 2024-04-20 12:52:16,080:INFO: joblib: 1.3.2 2024-04-20 12:52:16,080:INFO: sklearn: 1.4.1.post1 2024-04-20 12:52:16,080:INFO: pyod: 1.1.3 2024-04-20 12:52:16,080:INFO: imblearn: 0.12.0 2024-04-20 12:52:16,080:INFO: category_encoders: 2.6.3 2024-04-20 12:52:16,080:INFO: lightgbm: 4.3.0 2024-04-20 12:52:16,080:INFO: numba: 0.58.1 2024-04-20 12:52:16,080:INFO: requests: 2.31.0 2024-04-20 12:52:16,080:INFO: matplotlib: 3.7.5 2024-04-20 12:52:16,080:INFO: scikitplot: 0.3.7 2024-04-20 12:52:16,080:INFO: yellowbrick: 1.5 2024-04-20 12:52:16,080:INFO: plotly: 5.18.0 2024-04-20 12:52:16,080:INFO: plotly-resampler: Not installed 2024-04-20 12:52:16,081:INFO: kaleido: 0.2.1 2024-04-20 12:52:16,081:INFO: schemdraw: 0.15 2024-04-20 12:52:16,081:INFO: statsmodels: 0.14.1 2024-04-20 12:52:16,081:INFO: sktime: 0.26.0 2024-04-20 12:52:16,081:INFO: tbats: 1.1.3 2024-04-20 12:52:16,081:INFO: pmdarima: 2.0.4 2024-04-20 12:52:16,081:INFO: psutil: 5.9.6 2024-04-20 12:52:16,081:INFO: markupsafe: 2.1.3 2024-04-20 12:52:16,081:INFO: pickle5: Not installed 2024-04-20 12:52:16,081:INFO: cloudpickle: 3.0.0 2024-04-20 12:52:16,081:INFO: deprecation: 2.1.0 2024-04-20 12:52:16,081:INFO: xxhash: 3.4.1 2024-04-20 12:52:16,081:INFO: wurlitzer: Not installed 2024-04-20 12:52:16,081:INFO:PyCaret optional dependencies: 2024-04-20 12:52:16,086:INFO: shap: Not installed 2024-04-20 12:52:16,086:INFO: interpret: Not installed 2024-04-20 12:52:16,086:INFO: umap: 0.5.5 2024-04-20 12:52:16,086:INFO: ydata_profiling: 4.6.5 2024-04-20 12:52:16,086:INFO: explainerdashboard: Not installed 2024-04-20 12:52:16,086:INFO: autoviz: Not installed 2024-04-20 12:52:16,086:INFO: fairlearn: Not installed 2024-04-20 12:52:16,086:INFO: deepchecks: Not installed 2024-04-20 12:52:16,086:INFO: xgboost: Not installed 2024-04-20 12:52:16,086:INFO: catboost: Not installed 2024-04-20 12:52:16,086:INFO: kmodes: Not installed 2024-04-20 12:52:16,086:INFO: mlxtend: Not installed 2024-04-20 12:52:16,086:INFO: statsforecast: Not installed 2024-04-20 12:52:16,086:INFO: tune_sklearn: Not installed 2024-04-20 12:52:16,086:INFO: ray: Not installed 2024-04-20 12:52:16,086:INFO: hyperopt: Not installed 2024-04-20 12:52:16,086:INFO: optuna: Not installed 2024-04-20 12:52:16,086:INFO: skopt: Not installed 2024-04-20 12:52:16,086:INFO: mlflow: Not installed 2024-04-20 12:52:16,087:INFO: gradio: Not installed 2024-04-20 12:52:16,087:INFO: fastapi: 0.110.1 2024-04-20 12:52:16,087:INFO: uvicorn: 0.29.0 2024-04-20 12:52:16,087:INFO: m2cgen: Not installed 2024-04-20 12:52:16,087:INFO: evidently: Not installed 2024-04-20 12:52:16,087:INFO: fugue: Not installed 2024-04-20 12:52:16,087:INFO: streamlit: 1.29.0 2024-04-20 12:52:16,087:INFO: prophet: Not installed 2024-04-20 12:52:16,087:INFO:None 2024-04-20 12:52:16,087:INFO:Set up data. 2024-04-20 12:52:16,093:INFO:Set up folding strategy. 2024-04-20 12:52:16,093:INFO:Set up train/test split. 2024-04-20 12:52:16,097:INFO:Set up index. 2024-04-20 12:52:16,098:INFO:Assigning column types. 2024-04-20 12:52:16,099:INFO:Engine successfully changes for model 'lr' to 'sklearn'. 2024-04-20 12:52:16,127:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. 2024-04-20 12:52:16,130:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-20 12:52:16,155:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,155:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,185:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. 2024-04-20 12:52:16,186:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-20 12:52:16,204:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,204:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,204:INFO:Engine successfully changes for model 'knn' to 'sklearn'. 2024-04-20 12:52:16,232:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-20 12:52:16,248:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,249:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,274:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-20 12:52:16,290:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,290:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,290:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'. 2024-04-20 12:52:16,332:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,332:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,376:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,376:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,380:INFO:Preparing preprocessing pipeline... 2024-04-20 12:52:16,381:INFO:Set up label encoding. 2024-04-20 12:52:16,381:INFO:Set up simple imputation. 2024-04-20 12:52:16,406:INFO:Finished creating preprocessing pipeline. 2024-04-20 12:52:16,409:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\arpit\AppData\Local\Temp\joblib), steps=[('label_encoding', TransformerWrapperWithInverse(exclude=None, include=None, transformer=LabelEncoder())), ('numerical_imputer', TransformerWrapper(exclude=None, include=['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'], transformer=SimpleImputer(add_indicator=False, copy=True, fill_value=None, keep_empty_features=False, missing_values=nan, strategy='mean'))), ('categorical_imputer', TransformerWrapper(exclude=None, include=[], transformer=SimpleImputer(add_indicator=False, copy=True, fill_value=None, keep_empty_features=False, missing_values=nan, strategy='most_frequent')))], verbose=False) 2024-04-20 12:52:16,409:INFO:Creating final display dataframe. 2024-04-20 12:52:16,466:INFO:Setup _display_container: Description Value 0 Session id 2356 1 Target Species 2 Target type Multiclass 3 Target mapping Iris-setosa: 0, Iris-versicolor: 1, Iris-virgi... 4 Original data shape (150, 6) 5 Transformed data shape (150, 6) 6 Transformed train set shape (105, 6) 7 Transformed test set shape (45, 6) 8 Numeric features 5 9 Preprocess True 10 Imputation type simple 11 Numeric imputation mean 12 Categorical imputation mode 13 Fold Generator StratifiedKFold 14 Fold Number 10 15 CPU Jobs -1 16 Use GPU False 17 Log Experiment False 18 Experiment Name clf-default-name 19 USI 5ebc 2024-04-20 12:52:16,514:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,514:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,561:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,561:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 12:52:16,564:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\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( 2024-04-20 12:52:16,565:INFO:setup() successfully completed in 0.58s............... 2024-04-20 12:52:16,565:INFO:Initializing get_config() 2024-04-20 12:52:16,565:INFO:get_config(self=, variable=X_train) 2024-04-20 12:52:16,565:INFO:Variable: 'X_train' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_train_transformed' instead. 2024-04-20 12:52:16,567:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pycaret_experiment\pycaret_experiment.py:321: UserWarning: Variable: 'X_train' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_train_transformed' instead. warnings.warn(msg) # print on screen 2024-04-20 12:52:16,574:INFO:Variable: returned as Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm 124 125 6.7 3.3 5.7 2.1 55 56 5.7 2.8 4.5 1.3 31 32 5.4 3.4 1.5 0.4 22 23 4.6 3.6 1.0 0.2 83 84 6.0 2.7 5.1 1.6 .. ... ... ... ... ... 56 57 6.3 3.3 4.7 1.6 111 112 6.4 2.7 5.3 1.9 14 15 5.8 4.0 1.2 0.2 148 149 6.2 3.4 5.4 2.3 7 8 5.0 3.4 1.5 0.2 [105 rows x 5 columns] 2024-04-20 12:52:16,574:INFO:get_config() successfully completed...................................... 2024-04-20 12:52:16,574:INFO:Initializing get_config() 2024-04-20 12:52:16,574:INFO:get_config(self=, variable=X_test) 2024-04-20 12:52:16,574:INFO:Variable: 'X_test' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_test_transformed' instead. 2024-04-20 12:52:16,574:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pycaret_experiment\pycaret_experiment.py:321: UserWarning: Variable: 'X_test' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_test_transformed' instead. warnings.warn(msg) # print on screen 2024-04-20 12:52:16,584:INFO:Variable: returned as Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm 47 48 4.6 3.2 1.4 0.2 43 44 5.0 3.5 1.6 0.6 30 31 4.8 3.1 1.6 0.2 122 123 7.7 2.8 6.7 2.0 139 140 6.9 3.1 5.4 2.1 88 89 5.6 3.0 4.1 1.3 18 19 5.7 3.8 1.7 0.3 60 61 5.0 2.0 3.5 1.0 97 98 6.2 2.9 4.3 1.3 4 5 5.0 3.6 1.4 0.2 129 130 7.2 3.0 5.8 1.6 77 78 6.7 3.0 5.0 1.7 113 114 5.7 2.5 5.0 2.0 54 55 6.5 2.8 4.6 1.5 73 74 6.1 2.8 4.7 1.2 74 75 6.4 2.9 4.3 1.3 90 91 5.5 2.6 4.4 1.2 102 103 7.1 3.0 5.9 2.1 89 90 5.5 2.5 4.0 1.3 101 102 5.8 2.7 5.1 1.9 38 39 4.4 3.0 1.3 0.2 145 146 6.7 3.0 5.2 2.3 45 46 4.8 3.0 1.4 0.3 26 27 5.0 3.4 1.6 0.4 6 7 4.6 3.4 1.4 0.3 57 58 4.9 2.4 3.3 1.0 92 93 5.8 2.6 4.0 1.2 119 120 6.0 2.2 5.0 1.5 0 1 5.1 3.5 1.4 0.2 144 145 6.7 3.3 5.7 2.5 37 38 4.9 3.1 1.5 0.1 1 2 4.9 3.0 1.4 0.2 75 76 6.6 3.0 4.4 1.4 61 62 5.9 3.0 4.2 1.5 110 111 6.5 3.2 5.1 2.0 17 18 5.1 3.5 1.4 0.3 128 129 6.4 2.8 5.6 2.1 134 135 6.1 2.6 5.6 1.4 135 136 7.7 3.0 6.1 2.3 123 124 6.3 2.7 4.9 1.8 146 147 6.3 2.5 5.0 1.9 51 52 6.4 3.2 4.5 1.5 21 22 5.1 3.7 1.5 0.4 96 97 5.7 2.9 4.2 1.3 19 20 5.1 3.8 1.5 0.3 2024-04-20 12:52:16,584:INFO:get_config() successfully completed...................................... 2024-04-20 12:52:16,584:INFO:Initializing compare_models() 2024-04-20 12:52:16,584:INFO:compare_models(self=, include=None, exclude=['lightgbm', 'catboost', 'xgboost'], 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': , 'include': None, 'exclude': ['lightgbm', 'catboost', 'xgboost'], '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__': }) 2024-04-20 12:52:16,584:INFO:Checking exceptions 2024-04-20 12:52:16,586:INFO:Preparing display monitor 2024-04-20 12:52:16,592:INFO:Initializing Logistic Regression 2024-04-20 12:52:16,592:INFO:Total runtime is 0.0 minutes 2024-04-20 12:52:16,592:INFO:SubProcess create_model() called ================================== 2024-04-20 12:52:16,592:INFO:Initializing create_model() 2024-04-20 12:52:16,593:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 12:52:16,593:INFO:Checking exceptions 2024-04-20 12:52:16,593:INFO:Importing libraries 2024-04-20 12:52:16,593:INFO:Copying training dataset 2024-04-20 12:52:16,595:INFO:Defining folds 2024-04-20 12:52:16,595:INFO:Declaring metric variables 2024-04-20 12:52:16,595:INFO:Importing untrained model 2024-04-20 12:52:16,595:INFO:Logistic Regression Imported successfully 2024-04-20 12:52:16,596:INFO:Starting cross validation 2024-04-20 12:52:16,596:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:21,618:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:21,620:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:21,622:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:21,623:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:21,628:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:21,630:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:21,638:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:21,639:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:21,642:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:21,646:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,646:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:21,648:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,649:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,649:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,650:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:21,651:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:21,652:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,654:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,654:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,655:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,655:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,657:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,658:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,660:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,660:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,660:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,661:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,662:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,663:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:21,666:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,667:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:21,668:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,669:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:21,672:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,672:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:21,677:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,682:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:21,684:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,686:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:21,687:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:21,687:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,691:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,692:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,693:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,695:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,697:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,698:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,698:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,703:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,715:INFO:Calculating mean and std 2024-04-20 12:52:21,717:INFO:Creating metrics dataframe 2024-04-20 12:52:21,721:INFO:Uploading results into container 2024-04-20 12:52:21,722:INFO:Uploading model into container now 2024-04-20 12:52:21,722:INFO:_master_model_container: 1 2024-04-20 12:52:21,723:INFO:_display_container: 2 2024-04-20 12:52:21,723: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=2356, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-20 12:52:21,723:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:21,800:INFO:SubProcess create_model() end ================================== 2024-04-20 12:52:21,800:INFO:Creating metrics dataframe 2024-04-20 12:52:21,803:INFO:Initializing K Neighbors Classifier 2024-04-20 12:52:21,803:INFO:Total runtime is 0.08685739437739054 minutes 2024-04-20 12:52:21,803:INFO:SubProcess create_model() called ================================== 2024-04-20 12:52:21,803:INFO:Initializing create_model() 2024-04-20 12:52:21,803:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 12:52:21,803:INFO:Checking exceptions 2024-04-20 12:52:21,803:INFO:Importing libraries 2024-04-20 12:52:21,803:INFO:Copying training dataset 2024-04-20 12:52:21,805:INFO:Defining folds 2024-04-20 12:52:21,805:INFO:Declaring metric variables 2024-04-20 12:52:21,805:INFO:Importing untrained model 2024-04-20 12:52:21,805:INFO:K Neighbors Classifier Imported successfully 2024-04-20 12:52:21,805:INFO:Starting cross validation 2024-04-20 12:52:21,805:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:21,863:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:21,864:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:21,864:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:21,865:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:21,866:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:21,866:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:21,866:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:21,867:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:21,867:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,868:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,869:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,871:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,871:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,873:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,873:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,873:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,875:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,877:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:21,879:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,496:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,503:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,504:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,504:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,507:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:24,509:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,512:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:24,512:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,512:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:24,512:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:24,515:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,515:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,515:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,516:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,517:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,519:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,519:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,520:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,520:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,520:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,520:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,520:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,529:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:24,531:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:24,532:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,535:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,535:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,538:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,539:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,543:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,553:INFO:Calculating mean and std 2024-04-20 12:52:24,554:INFO:Creating metrics dataframe 2024-04-20 12:52:24,561:INFO:Uploading results into container 2024-04-20 12:52:24,562:INFO:Uploading model into container now 2024-04-20 12:52:24,562:INFO:_master_model_container: 2 2024-04-20 12:52:24,563:INFO:_display_container: 2 2024-04-20 12:52:24,563:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=-1, n_neighbors=5, p=2, weights='uniform') 2024-04-20 12:52:24,563:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:24,701:INFO:SubProcess create_model() end ================================== 2024-04-20 12:52:24,701:INFO:Creating metrics dataframe 2024-04-20 12:52:24,734:INFO:Initializing Naive Bayes 2024-04-20 12:52:24,734:INFO:Total runtime is 0.1356996734937032 minutes 2024-04-20 12:52:24,734:INFO:SubProcess create_model() called ================================== 2024-04-20 12:52:24,734:INFO:Initializing create_model() 2024-04-20 12:52:24,734:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 12:52:24,734:INFO:Checking exceptions 2024-04-20 12:52:24,734:INFO:Importing libraries 2024-04-20 12:52:24,734:INFO:Copying training dataset 2024-04-20 12:52:24,777:INFO:Defining folds 2024-04-20 12:52:24,777:INFO:Declaring metric variables 2024-04-20 12:52:24,777:INFO:Importing untrained model 2024-04-20 12:52:24,778:INFO:Naive Bayes Imported successfully 2024-04-20 12:52:24,778:INFO:Starting cross validation 2024-04-20 12:52:24,780:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:24,814:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,816:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:24,817:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,818:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,818:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:24,818:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,819:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,819:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:24,821:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,822:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,822:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,822:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,823:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,823:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:24,824:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:24,824:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,826:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,826:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,826:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,827:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,827:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,827:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:24,828:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,828:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,829:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,830:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,830:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:24,830:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:24,830:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:24,830:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,831:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,831:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,831:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,832:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,832:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,833:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:24,834:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,834:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,834:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,835:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:24,836:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,836:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,837:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,839:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,839:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,840:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,842:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:24,857:INFO:Calculating mean and std 2024-04-20 12:52:24,857:INFO:Creating metrics dataframe 2024-04-20 12:52:24,863:INFO:Uploading results into container 2024-04-20 12:52:24,864:INFO:Uploading model into container now 2024-04-20 12:52:24,865:INFO:_master_model_container: 3 2024-04-20 12:52:24,865:INFO:_display_container: 2 2024-04-20 12:52:24,865:INFO:GaussianNB(priors=None, var_smoothing=1e-09) 2024-04-20 12:52:24,865:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:24,966:INFO:SubProcess create_model() end ================================== 2024-04-20 12:52:24,967:INFO:Creating metrics dataframe 2024-04-20 12:52:24,974:INFO:Initializing Decision Tree Classifier 2024-04-20 12:52:24,974:INFO:Total runtime is 0.13970758517583212 minutes 2024-04-20 12:52:24,974:INFO:SubProcess create_model() called ================================== 2024-04-20 12:52:24,975:INFO:Initializing create_model() 2024-04-20 12:52:24,975:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 12:52:24,975:INFO:Checking exceptions 2024-04-20 12:52:24,975:INFO:Importing libraries 2024-04-20 12:52:24,975:INFO:Copying training dataset 2024-04-20 12:52:24,978:INFO:Defining folds 2024-04-20 12:52:24,978:INFO:Declaring metric variables 2024-04-20 12:52:24,978:INFO:Importing untrained model 2024-04-20 12:52:24,979:INFO:Decision Tree Classifier Imported successfully 2024-04-20 12:52:24,980:INFO:Starting cross validation 2024-04-20 12:52:24,981:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:25,006:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,007:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:25,007:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,009:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,011:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,016:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,017:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:25,018:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,018:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,019:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:25,020:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,020:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,020:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,022:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:25,022:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,022:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,023:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,023:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:25,024:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,024:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,026:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,027:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:25,027:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,027:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,028:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,028:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,029:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:25,029:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,030:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,030:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:25,030:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,030:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,031:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:25,031:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,031:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,031:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,032:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:25,032:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,033:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,034:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,034:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,034:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,035:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,035:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,036:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,036:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,037:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,038:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,038:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,042:INFO:Calculating mean and std 2024-04-20 12:52:25,044:INFO:Creating metrics dataframe 2024-04-20 12:52:25,049:INFO:Uploading results into container 2024-04-20 12:52:25,049:INFO:Uploading model into container now 2024-04-20 12:52:25,050:INFO:_master_model_container: 4 2024-04-20 12:52:25,051:INFO:_display_container: 2 2024-04-20 12:52:25,051: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=2356, splitter='best') 2024-04-20 12:52:25,051:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:25,129:INFO:SubProcess create_model() end ================================== 2024-04-20 12:52:25,129:INFO:Creating metrics dataframe 2024-04-20 12:52:25,135:INFO:Initializing SVM - Linear Kernel 2024-04-20 12:52:25,135:INFO:Total runtime is 0.14239041407903036 minutes 2024-04-20 12:52:25,135:INFO:SubProcess create_model() called ================================== 2024-04-20 12:52:25,135:INFO:Initializing create_model() 2024-04-20 12:52:25,135:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 12:52:25,135:INFO:Checking exceptions 2024-04-20 12:52:25,135:INFO:Importing libraries 2024-04-20 12:52:25,136:INFO:Copying training dataset 2024-04-20 12:52:25,139:INFO:Defining folds 2024-04-20 12:52:25,139:INFO:Declaring metric variables 2024-04-20 12:52:25,139:INFO:Importing untrained model 2024-04-20 12:52:25,140:INFO:SVM - Linear Kernel Imported successfully 2024-04-20 12:52:25,140:INFO:Starting cross validation 2024-04-20 12:52:25,141:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:25,188:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,189:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,190:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,190:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,190:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,193:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,193:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,193:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,193:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,193:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,194:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,194:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,194:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,195:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:25,195:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,196:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,197:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,198:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,198:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,198:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,198:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:25,198:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,198:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,199:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,199:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,199:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,200:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:25,200:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,202:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,202:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,203:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,203:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,203:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,204:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,204:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,205:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:25,206:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,206:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:25,206:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,207:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,207:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,207:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,208:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:25,209:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,210:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,211:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:25,213:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,223:INFO:Calculating mean and std 2024-04-20 12:52:25,224:INFO:Creating metrics dataframe 2024-04-20 12:52:25,228:INFO:Uploading results into container 2024-04-20 12:52:25,229:INFO:Uploading model into container now 2024-04-20 12:52:25,229:INFO:_master_model_container: 5 2024-04-20 12:52:25,229:INFO:_display_container: 2 2024-04-20 12:52:25,230: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=2356, shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0, warm_start=False) 2024-04-20 12:52:25,230:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:25,294:INFO:SubProcess create_model() end ================================== 2024-04-20 12:52:25,294:INFO:Creating metrics dataframe 2024-04-20 12:52:25,343:INFO:Initializing Ridge Classifier 2024-04-20 12:52:25,343:INFO:Total runtime is 0.14585185845692952 minutes 2024-04-20 12:52:25,343:INFO:SubProcess create_model() called ================================== 2024-04-20 12:52:25,344:INFO:Initializing create_model() 2024-04-20 12:52:25,344:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 12:52:25,344:INFO:Checking exceptions 2024-04-20 12:52:25,344:INFO:Importing libraries 2024-04-20 12:52:25,344:INFO:Copying training dataset 2024-04-20 12:52:25,349:INFO:Defining folds 2024-04-20 12:52:25,349:INFO:Declaring metric variables 2024-04-20 12:52:25,349:INFO:Importing untrained model 2024-04-20 12:52:25,349:INFO:Ridge Classifier Imported successfully 2024-04-20 12:52:25,350:INFO:Starting cross validation 2024-04-20 12:52:25,350:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:25,383:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,383:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,384:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,384:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,384:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,384:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,385:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,386:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,386:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,386:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,387:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,387:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,387:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,387:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,388:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,388:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,389:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,389:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,390:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,390:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,390:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,391:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,391:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,392:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,392:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,393:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 12:52:25,393:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,394:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,394:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,395:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,396:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,396:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,396:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,396:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,397:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,399:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,414:INFO:Calculating mean and std 2024-04-20 12:52:25,414:INFO:Creating metrics dataframe 2024-04-20 12:52:25,418:INFO:Uploading results into container 2024-04-20 12:52:25,419:INFO:Uploading model into container now 2024-04-20 12:52:25,419:INFO:_master_model_container: 6 2024-04-20 12:52:25,419:INFO:_display_container: 2 2024-04-20 12:52:25,420:INFO:RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True, max_iter=None, positive=False, random_state=2356, solver='auto', tol=0.0001) 2024-04-20 12:52:25,420:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:25,509:INFO:SubProcess create_model() end ================================== 2024-04-20 12:52:25,509:INFO:Creating metrics dataframe 2024-04-20 12:52:25,514:INFO:Initializing Random Forest Classifier 2024-04-20 12:52:25,514:INFO:Total runtime is 0.14871371984481813 minutes 2024-04-20 12:52:25,516:INFO:SubProcess create_model() called ================================== 2024-04-20 12:52:25,516:INFO:Initializing create_model() 2024-04-20 12:52:25,516:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 12:52:25,516:INFO:Checking exceptions 2024-04-20 12:52:25,516:INFO:Importing libraries 2024-04-20 12:52:25,516:INFO:Copying training dataset 2024-04-20 12:52:25,520:INFO:Defining folds 2024-04-20 12:52:25,520:INFO:Declaring metric variables 2024-04-20 12:52:25,520:INFO:Importing untrained model 2024-04-20 12:52:25,520:INFO:Random Forest Classifier Imported successfully 2024-04-20 12:52:25,520:INFO:Starting cross validation 2024-04-20 12:52:25,520:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:25,777:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,778:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:25,780:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,781:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,783:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,783:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:25,784:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,784:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,786:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:25,786:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,786:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:25,788:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,789:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,789:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,790:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:25,794:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,794:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:25,795:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,795:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,796:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,796:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,796:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,796:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,797:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:25,797:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,800:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,802:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:25,802:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,802:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,803:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,803:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,804:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,805:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,805:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,805:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:25,805:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:25,806:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,806:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,806:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,807:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,808:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,809:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,811:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,811:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,812:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,815:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,816:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,816:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,823:INFO:Calculating mean and std 2024-04-20 12:52:25,825:INFO:Creating metrics dataframe 2024-04-20 12:52:25,829:INFO:Uploading results into container 2024-04-20 12:52:25,830:INFO:Uploading model into container now 2024-04-20 12:52:25,830:INFO:_master_model_container: 7 2024-04-20 12:52:25,831:INFO:_display_container: 2 2024-04-20 12:52:25,831: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=2356, verbose=0, warm_start=False) 2024-04-20 12:52:25,831:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:25,922:INFO:SubProcess create_model() end ================================== 2024-04-20 12:52:25,922:INFO:Creating metrics dataframe 2024-04-20 12:52:25,931:INFO:Initializing Quadratic Discriminant Analysis 2024-04-20 12:52:25,931:INFO:Total runtime is 0.15564914941787722 minutes 2024-04-20 12:52:25,931:INFO:SubProcess create_model() called ================================== 2024-04-20 12:52:25,932:INFO:Initializing create_model() 2024-04-20 12:52:25,932:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 12:52:25,932:INFO:Checking exceptions 2024-04-20 12:52:25,932:INFO:Importing libraries 2024-04-20 12:52:25,932:INFO:Copying training dataset 2024-04-20 12:52:25,937:INFO:Defining folds 2024-04-20 12:52:25,937:INFO:Declaring metric variables 2024-04-20 12:52:25,937:INFO:Importing untrained model 2024-04-20 12:52:25,938:INFO:Quadratic Discriminant Analysis Imported successfully 2024-04-20 12:52:25,939:INFO:Starting cross validation 2024-04-20 12:52:25,940:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:25,978:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,979:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,979:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,979:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,979:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,980:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,980:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,980:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:25,981:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:25,982:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,982:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:25,982:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,982:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:25,982:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:25,983:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,983:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:25,983:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,984:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:25,984:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,984:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,984:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,985:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:25,985:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,985:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,986:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,986:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,987:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:25,988:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,988:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,989:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,989:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,989:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,990:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,990:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,991:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,991:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,991:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,991:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,992:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,992:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,993:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,993:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,993:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,995:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,996:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,996:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:25,997:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,010:INFO:Calculating mean and std 2024-04-20 12:52:26,011:INFO:Creating metrics dataframe 2024-04-20 12:52:26,016:INFO:Uploading results into container 2024-04-20 12:52:26,016:INFO:Uploading model into container now 2024-04-20 12:52:26,017:INFO:_master_model_container: 8 2024-04-20 12:52:26,017:INFO:_display_container: 2 2024-04-20 12:52:26,018:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0, store_covariance=False, tol=0.0001) 2024-04-20 12:52:26,018:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:26,108:INFO:SubProcess create_model() end ================================== 2024-04-20 12:52:26,108:INFO:Creating metrics dataframe 2024-04-20 12:52:26,120:INFO:Initializing Ada Boost Classifier 2024-04-20 12:52:26,120:INFO:Total runtime is 0.1588139891624451 minutes 2024-04-20 12:52:26,122:INFO:SubProcess create_model() called ================================== 2024-04-20 12:52:26,122:INFO:Initializing create_model() 2024-04-20 12:52:26,122:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 12:52:26,122:INFO:Checking exceptions 2024-04-20 12:52:26,122:INFO:Importing libraries 2024-04-20 12:52:26,123:INFO:Copying training dataset 2024-04-20 12:52:26,128:INFO:Defining folds 2024-04-20 12:52:26,129:INFO:Declaring metric variables 2024-04-20 12:52:26,129:INFO:Importing untrained model 2024-04-20 12:52:26,129:INFO:Ada Boost Classifier Imported successfully 2024-04-20 12:52:26,130:INFO:Starting cross validation 2024-04-20 12:52:26,131:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:26,151:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 12:52:26,151:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 12:52:26,153:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 12:52:26,156:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 12:52:26,156:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 12:52:26,160:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 12:52:26,160:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 12:52:26,162:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 12:52:26,164:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 12:52:26,166:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 12:52:26,260:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,262:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:26,265:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,268:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,270:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,271:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,273:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:26,275:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,276:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,277:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,277:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:26,278:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,278:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:26,278:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,279:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,279:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,279:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:26,280:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,280:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:26,281:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,282:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,283:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,283:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,284:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,286:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,287:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,287:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,287:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,287:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,288:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,288:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,288:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,288:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:26,289:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:26,289:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,289:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:26,290:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,290:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:26,290:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,292:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,292:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,293:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,294:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,294:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,294:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,296:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,297:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,297:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,308:INFO:Calculating mean and std 2024-04-20 12:52:26,309:INFO:Creating metrics dataframe 2024-04-20 12:52:26,315:INFO:Uploading results into container 2024-04-20 12:52:26,316:INFO:Uploading model into container now 2024-04-20 12:52:26,316:INFO:_master_model_container: 9 2024-04-20 12:52:26,316:INFO:_display_container: 2 2024-04-20 12:52:26,317:INFO:AdaBoostClassifier(algorithm='SAMME.R', estimator=None, learning_rate=1.0, n_estimators=50, random_state=2356) 2024-04-20 12:52:26,317:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:26,427:INFO:SubProcess create_model() end ================================== 2024-04-20 12:52:26,427:INFO:Creating metrics dataframe 2024-04-20 12:52:26,436:INFO:Initializing Gradient Boosting Classifier 2024-04-20 12:52:26,436:INFO:Total runtime is 0.16407881577809655 minutes 2024-04-20 12:52:26,436:INFO:SubProcess create_model() called ================================== 2024-04-20 12:52:26,436:INFO:Initializing create_model() 2024-04-20 12:52:26,436:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 12:52:26,436:INFO:Checking exceptions 2024-04-20 12:52:26,436:INFO:Importing libraries 2024-04-20 12:52:26,436:INFO:Copying training dataset 2024-04-20 12:52:26,440:INFO:Defining folds 2024-04-20 12:52:26,440:INFO:Declaring metric variables 2024-04-20 12:52:26,440:INFO:Importing untrained model 2024-04-20 12:52:26,441:INFO:Gradient Boosting Classifier Imported successfully 2024-04-20 12:52:26,441:INFO:Starting cross validation 2024-04-20 12:52:26,441:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:26,756:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,758:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,758:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:26,758:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,759:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,759:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:26,760:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,760:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:26,761:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:26,761:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,762:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,762:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:26,763:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,763:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,763:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,764:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,764:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,765:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,765:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:26,766:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,767:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,767:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:26,767:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,767:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,768:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,770:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,770:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,770:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,770:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,771:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,772:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,772:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,773:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:26,773:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,774:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,775:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:26,775:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,776:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,776:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,776:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,778:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,779:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,780:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,780:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,786:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:26,787:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:26,789:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,792:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,794:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:26,801:INFO:Calculating mean and std 2024-04-20 12:52:26,802:INFO:Creating metrics dataframe 2024-04-20 12:52:26,807:INFO:Uploading results into container 2024-04-20 12:52:26,807:INFO:Uploading model into container now 2024-04-20 12:52:26,808:INFO:_master_model_container: 10 2024-04-20 12:52:26,808:INFO:_display_container: 2 2024-04-20 12:52:26,808: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=2356, subsample=1.0, tol=0.0001, validation_fraction=0.1, verbose=0, warm_start=False) 2024-04-20 12:52:26,809:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:26,957:INFO:SubProcess create_model() end ================================== 2024-04-20 12:52:26,958:INFO:Creating metrics dataframe 2024-04-20 12:52:26,963:INFO:Initializing Linear Discriminant Analysis 2024-04-20 12:52:26,963:INFO:Total runtime is 0.17285097837448124 minutes 2024-04-20 12:52:26,963:INFO:SubProcess create_model() called ================================== 2024-04-20 12:52:26,964:INFO:Initializing create_model() 2024-04-20 12:52:26,964:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 12:52:26,964:INFO:Checking exceptions 2024-04-20 12:52:26,964:INFO:Importing libraries 2024-04-20 12:52:26,964:INFO:Copying training dataset 2024-04-20 12:52:26,968:INFO:Defining folds 2024-04-20 12:52:26,968:INFO:Declaring metric variables 2024-04-20 12:52:26,968:INFO:Importing untrained model 2024-04-20 12:52:26,968:INFO:Linear Discriminant Analysis Imported successfully 2024-04-20 12:52:26,969:INFO:Starting cross validation 2024-04-20 12:52:26,969:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:27,006:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,008:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:27,009:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,009:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,009:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,009:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,010:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,010:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,010:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,011:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:27,011:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:27,011:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:27,011:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:27,012:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:27,012:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,012:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:27,012:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:27,013:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,014:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,014:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,014:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,014:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,014:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,015:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,015:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,016:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,018:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:27,018:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,019:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,019:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,019:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,019:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,020:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,020:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,020:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,021:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,021:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:27,022:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,023:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,023:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,023:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,023:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,023:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,023:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,023:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,025:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,028:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,031:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,037:INFO:Calculating mean and std 2024-04-20 12:52:27,038:INFO:Creating metrics dataframe 2024-04-20 12:52:27,042:INFO:Uploading results into container 2024-04-20 12:52:27,043:INFO:Uploading model into container now 2024-04-20 12:52:27,043:INFO:_master_model_container: 11 2024-04-20 12:52:27,043:INFO:_display_container: 2 2024-04-20 12:52:27,044:INFO:LinearDiscriminantAnalysis(covariance_estimator=None, n_components=None, priors=None, shrinkage=None, solver='svd', store_covariance=False, tol=0.0001) 2024-04-20 12:52:27,044:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:27,164:INFO:SubProcess create_model() end ================================== 2024-04-20 12:52:27,164:INFO:Creating metrics dataframe 2024-04-20 12:52:27,187:INFO:Initializing Extra Trees Classifier 2024-04-20 12:52:27,187:INFO:Total runtime is 0.17659064928690596 minutes 2024-04-20 12:52:27,188:INFO:SubProcess create_model() called ================================== 2024-04-20 12:52:27,188:INFO:Initializing create_model() 2024-04-20 12:52:27,188:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 12:52:27,188:INFO:Checking exceptions 2024-04-20 12:52:27,188:INFO:Importing libraries 2024-04-20 12:52:27,189:INFO:Copying training dataset 2024-04-20 12:52:27,192:INFO:Defining folds 2024-04-20 12:52:27,192:INFO:Declaring metric variables 2024-04-20 12:52:27,192:INFO:Importing untrained model 2024-04-20 12:52:27,193:INFO:Extra Trees Classifier Imported successfully 2024-04-20 12:52:27,193:INFO:Starting cross validation 2024-04-20 12:52:27,193:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:27,405:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,407:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,407:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,407:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:27,408:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,408:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,409:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,409:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:27,409:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:27,409:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:27,409:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:27,410:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,410:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,410:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,411:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,412:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,413:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,413:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,413:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,413:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,414:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,416:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:27,416:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,416:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,416:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,416:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,416:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,417:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,418:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,418:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:27,418:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:27,418:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:27,420:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,420:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,420:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,420:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,421:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,421:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,423:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,424:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:27,424:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,424:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,425:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,425:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,426:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,427:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,428:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,428:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,430:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,443:INFO:Calculating mean and std 2024-04-20 12:52:27,444:INFO:Creating metrics dataframe 2024-04-20 12:52:27,449:INFO:Uploading results into container 2024-04-20 12:52:27,450:INFO:Uploading model into container now 2024-04-20 12:52:27,450:INFO:_master_model_container: 12 2024-04-20 12:52:27,450:INFO:_display_container: 2 2024-04-20 12:52:27,451: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=2356, verbose=0, warm_start=False) 2024-04-20 12:52:27,451:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:27,527:INFO:SubProcess create_model() end ================================== 2024-04-20 12:52:27,527:INFO:Creating metrics dataframe 2024-04-20 12:52:27,531:INFO:Initializing Dummy Classifier 2024-04-20 12:52:27,531:INFO:Total runtime is 0.18232087294260665 minutes 2024-04-20 12:52:27,531:INFO:SubProcess create_model() called ================================== 2024-04-20 12:52:27,532:INFO:Initializing create_model() 2024-04-20 12:52:27,532:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 12:52:27,532:INFO:Checking exceptions 2024-04-20 12:52:27,532:INFO:Importing libraries 2024-04-20 12:52:27,532:INFO:Copying training dataset 2024-04-20 12:52:27,533:INFO:Defining folds 2024-04-20 12:52:27,533:INFO:Declaring metric variables 2024-04-20 12:52:27,533:INFO:Importing untrained model 2024-04-20 12:52:27,533:INFO:Dummy Classifier Imported successfully 2024-04-20 12:52:27,534:INFO:Starting cross validation 2024-04-20 12:52:27,534:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:27,550:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,550:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,552:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:27,552:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:27,552:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,553:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,554:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,554:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,556:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:27,556:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,556:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,557:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:27,558:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:27,558:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,558:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:27,559:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,559:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,560:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,562:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,563:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,564:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:27,565:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:27,566:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,567:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,568:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,569:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,569:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,570:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:27,570:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:27,571:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:27,572:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,572:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,572:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,572:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,573:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,573:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:27,574:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,574:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:27,574:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,574:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:27,574:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:27,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:27,576:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,576:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:27,576:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:27,576:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,577:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,577:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,577:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:27,577:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,578:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,578:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:27,578:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,579:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,580:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,581:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 12:52:27,582:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:27,594:INFO:Calculating mean and std 2024-04-20 12:52:27,595:INFO:Creating metrics dataframe 2024-04-20 12:52:27,599:INFO:Uploading results into container 2024-04-20 12:52:27,600:INFO:Uploading model into container now 2024-04-20 12:52:27,600:INFO:_master_model_container: 13 2024-04-20 12:52:27,600:INFO:_display_container: 2 2024-04-20 12:52:27,600:INFO:DummyClassifier(constant=None, random_state=2356, strategy='prior') 2024-04-20 12:52:27,600:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:27,672:INFO:SubProcess create_model() end ================================== 2024-04-20 12:52:27,672:INFO:Creating metrics dataframe 2024-04-20 12:52:27,679:INFO:Initializing create_model() 2024-04-20 12:52:27,679:INFO:create_model(self=, estimator=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=2356, verbose=0, warm_start=False), 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={}) 2024-04-20 12:52:27,679:INFO:Checking exceptions 2024-04-20 12:52:27,679:INFO:Importing libraries 2024-04-20 12:52:27,680:INFO:Copying training dataset 2024-04-20 12:52:27,683:INFO:Defining folds 2024-04-20 12:52:27,683:INFO:Declaring metric variables 2024-04-20 12:52:27,683:INFO:Importing untrained model 2024-04-20 12:52:27,683:INFO:Declaring custom model 2024-04-20 12:52:27,685:INFO:Random Forest Classifier Imported successfully 2024-04-20 12:52:27,685:INFO:Cross validation set to False 2024-04-20 12:52:27,685:INFO:Fitting Model 2024-04-20 12:52:27,923: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=2356, verbose=0, warm_start=False) 2024-04-20 12:52:27,923:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:28,104:INFO:_master_model_container: 13 2024-04-20 12:52:28,105:INFO:_display_container: 2 2024-04-20 12:52:28,105: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=2356, verbose=0, warm_start=False) 2024-04-20 12:52:28,105:INFO:compare_models() successfully completed...................................... 2024-04-20 12:52:28,106:INFO:Initializing create_model() 2024-04-20 12:52:28,106:INFO:create_model(self=, estimator=rf, 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={}) 2024-04-20 12:52:28,106:INFO:Checking exceptions 2024-04-20 12:52:28,107:INFO:Importing libraries 2024-04-20 12:52:28,107:INFO:Copying training dataset 2024-04-20 12:52:28,111:INFO:Defining folds 2024-04-20 12:52:28,111:INFO:Declaring metric variables 2024-04-20 12:52:28,111:INFO:Importing untrained model 2024-04-20 12:52:28,112:INFO:Random Forest Classifier Imported successfully 2024-04-20 12:52:28,112:INFO:Starting cross validation 2024-04-20 12:52:28,113:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:28,375:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:28,376:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:28,377:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:28,377:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:28,379:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,379:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:28,379:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,382:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:28,382:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:28,382:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,383:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,383:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:28,383:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,384:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:28,385:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,386:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,386:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,386:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:28,387:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,388:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,388:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,389:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:28,389:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,389:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:28,390:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,391:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:28,391:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,391:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:28,392:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,394:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,394:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:28,394:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,395:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,396:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:28,397:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:28,397:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,397:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,398:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:28,398:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,399:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:28,399:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:28,400:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,400:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,400:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,402:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,402:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,402:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,403:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,403:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,405:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:28,415:INFO:Calculating mean and std 2024-04-20 12:52:28,416:INFO:Creating metrics dataframe 2024-04-20 12:52:28,419:INFO:Finalizing model 2024-04-20 12:52:28,553:INFO:Uploading results into container 2024-04-20 12:52:28,554:INFO:Uploading model into container now 2024-04-20 12:52:28,570:INFO:_master_model_container: 14 2024-04-20 12:52:28,570:INFO:_display_container: 3 2024-04-20 12:52:28,571: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=2356, verbose=0, warm_start=False) 2024-04-20 12:52:28,571:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:28,653:INFO:Initializing tune_model() 2024-04-20 12:52:28,654:INFO:tune_model(self=, estimator=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=2356, verbose=0, warm_start=False), 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={}) 2024-04-20 12:52:28,654:INFO:Checking exceptions 2024-04-20 12:52:28,657:INFO:Copying training dataset 2024-04-20 12:52:28,658:INFO:Checking base model 2024-04-20 12:52:28,659:INFO:Base model : Random Forest Classifier 2024-04-20 12:52:28,659:INFO:Declaring metric variables 2024-04-20 12:52:28,659:INFO:Defining Hyperparameters 2024-04-20 12:52:28,734:INFO:Tuning with n_jobs=-1 2024-04-20 12:52:28,734:INFO:Initializing RandomizedSearchCV 2024-04-20 12:52:32,885:INFO:best_params: {'actual_estimator__n_estimators': 100, 'actual_estimator__min_samples_split': 5, 'actual_estimator__min_samples_leaf': 6, 'actual_estimator__min_impurity_decrease': 0.002, 'actual_estimator__max_features': 'log2', 'actual_estimator__max_depth': 10, 'actual_estimator__criterion': 'entropy', 'actual_estimator__class_weight': 'balanced', 'actual_estimator__bootstrap': False} 2024-04-20 12:52:32,886:INFO:Hyperparameter search completed 2024-04-20 12:52:32,886:INFO:SubProcess create_model() called ================================== 2024-04-20 12:52:32,886:INFO:Initializing create_model() 2024-04-20 12:52:32,886:INFO:create_model(self=, estimator=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=2356, verbose=0, warm_start=False), 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 6, 'min_impurity_decrease': 0.002, 'max_features': 'log2', 'max_depth': 10, 'criterion': 'entropy', 'class_weight': 'balanced', 'bootstrap': False}) 2024-04-20 12:52:32,886:INFO:Checking exceptions 2024-04-20 12:52:32,886:INFO:Importing libraries 2024-04-20 12:52:32,886:INFO:Copying training dataset 2024-04-20 12:52:32,888:INFO:Defining folds 2024-04-20 12:52:32,888:INFO:Declaring metric variables 2024-04-20 12:52:32,888:INFO:Importing untrained model 2024-04-20 12:52:32,888:INFO:Declaring custom model 2024-04-20 12:52:32,889:INFO:Random Forest Classifier Imported successfully 2024-04-20 12:52:32,889:INFO:Starting cross validation 2024-04-20 12:52:32,890:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:33,091:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,092:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:33,093:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,097:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,100:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,100:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,100:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,101:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:33,102:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:33,103:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,103:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,106:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,106:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,107:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,107:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,108:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,108:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,109:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:33,109:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:33,109:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,109:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:33,110:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,110:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,111:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,111:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,112:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:33,112:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,114:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,114:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,114:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,114:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,115:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:33,116:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,116:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,116:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,116:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,118:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,118:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,118:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:33,120:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,120:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,122:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,122:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:33,124:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,124:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,124:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,125:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,127:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,134:INFO:Calculating mean and std 2024-04-20 12:52:33,135:INFO:Creating metrics dataframe 2024-04-20 12:52:33,136:INFO:Finalizing model 2024-04-20 12:52:33,232:INFO:Uploading results into container 2024-04-20 12:52:33,232:INFO:Uploading model into container now 2024-04-20 12:52:33,232:INFO:_master_model_container: 15 2024-04-20 12:52:33,232:INFO:_display_container: 4 2024-04-20 12:52:33,233:INFO:RandomForestClassifier(bootstrap=False, ccp_alpha=0.0, class_weight='balanced', criterion='entropy', max_depth=10, max_features='log2', max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0.002, min_samples_leaf=6, min_samples_split=5, min_weight_fraction_leaf=0.0, monotonic_cst=None, n_estimators=100, n_jobs=-1, oob_score=False, random_state=2356, verbose=0, warm_start=False) 2024-04-20 12:52:33,233:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:33,294:INFO:SubProcess create_model() end ================================== 2024-04-20 12:52:33,294:INFO:choose_better activated 2024-04-20 12:52:33,294:INFO:SubProcess create_model() called ================================== 2024-04-20 12:52:33,294:INFO:Initializing create_model() 2024-04-20 12:52:33,294:INFO:create_model(self=, estimator=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=2356, verbose=0, warm_start=False), 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={}) 2024-04-20 12:52:33,294:INFO:Checking exceptions 2024-04-20 12:52:33,295:INFO:Importing libraries 2024-04-20 12:52:33,295:INFO:Copying training dataset 2024-04-20 12:52:33,297:INFO:Defining folds 2024-04-20 12:52:33,297:INFO:Declaring metric variables 2024-04-20 12:52:33,297:INFO:Importing untrained model 2024-04-20 12:52:33,297:INFO:Declaring custom model 2024-04-20 12:52:33,298:INFO:Random Forest Classifier Imported successfully 2024-04-20 12:52:33,298:INFO:Starting cross validation 2024-04-20 12:52:33,299:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 12:52:33,548:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,549:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,549:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,550:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:33,550:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:33,551:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,551:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,551:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:33,551:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,553:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,553:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:33,553:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,554:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,555:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:33,555:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,556:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,556:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,556:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,558:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,558:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,558:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,559:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 12:52:33,559:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,560:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:33,560:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,560:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,561:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,561:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,562:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,562:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,562:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:33,562:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,563:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,563:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,565:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:33,565:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,566:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,566:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,567:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,568:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,568:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,568:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,569:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 12:52:33,570:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,570:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,571:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 12:52:33,572:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,574:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 12:52:33,586:INFO:Calculating mean and std 2024-04-20 12:52:33,586:INFO:Creating metrics dataframe 2024-04-20 12:52:33,588:INFO:Finalizing model 2024-04-20 12:52:33,699:INFO:Uploading results into container 2024-04-20 12:52:33,699:INFO:Uploading model into container now 2024-04-20 12:52:33,700:INFO:_master_model_container: 16 2024-04-20 12:52:33,700:INFO:_display_container: 5 2024-04-20 12:52:33,700: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=2356, verbose=0, warm_start=False) 2024-04-20 12:52:33,700:INFO:create_model() successfully completed...................................... 2024-04-20 12:52:33,756:INFO:SubProcess create_model() end ================================== 2024-04-20 12:52:33,757: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=2356, verbose=0, warm_start=False) result for Accuracy is 1.0 2024-04-20 12:52:33,757:INFO:RandomForestClassifier(bootstrap=False, ccp_alpha=0.0, class_weight='balanced', criterion='entropy', max_depth=10, max_features='log2', max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0.002, min_samples_leaf=6, min_samples_split=5, min_weight_fraction_leaf=0.0, monotonic_cst=None, n_estimators=100, n_jobs=-1, oob_score=False, random_state=2356, verbose=0, warm_start=False) result for Accuracy is 1.0 2024-04-20 12:52:33,758: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=2356, verbose=0, warm_start=False) is best model 2024-04-20 12:52:33,758:INFO:choose_better completed 2024-04-20 12:52:33,758: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). 2024-04-20 12:52:33,765:INFO:_master_model_container: 16 2024-04-20 12:52:33,765:INFO:_display_container: 4 2024-04-20 12:52:33,766: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=2356, verbose=0, warm_start=False) 2024-04-20 12:52:33,766:INFO:tune_model() successfully completed...................................... 2024-04-20 12:52:33,831:INFO:Initializing evaluate_model() 2024-04-20 12:52:33,831:INFO:evaluate_model(self=, estimator=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=2356, verbose=0, warm_start=False), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) 2024-04-20 12:52:33,945:INFO:Initializing plot_model() 2024-04-20 12:52:33,945:INFO:plot_model(self=, estimator=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=2356, verbose=0, warm_start=False), plot=pipeline, scale=1, save=False, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) 2024-04-20 12:52:33,945:INFO:Checking exceptions 2024-04-20 12:52:33,961:INFO:Preloading libraries 2024-04-20 12:52:33,966:INFO:Copying training dataset 2024-04-20 12:52:33,966:INFO:Plot type: pipeline 2024-04-20 12:52:36,243:INFO:Visual Rendered Successfully 2024-04-20 12:52:36,311:INFO:plot_model() successfully completed...................................... 2024-04-20 13:06:59,320:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:06:59,320:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:06:59,320:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:06:59,320:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:11,773:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:11,773:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:11,773:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:11,773:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:15,337:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:15,337:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:15,337:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:15,337:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:23,375:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:23,375:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:23,375:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:23,375:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:56,456:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:56,456:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:56,456:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:07:56,456:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:08:00,117:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:08:00,117:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:08:00,117:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:08:00,117:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:08:03,774:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:08:03,774:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:08:03,774:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:08:03,774:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:08:43,443:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:08:43,444:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:08:43,444:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:08:43,444:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-20 13:08:53,152:INFO:PyCaret ClassificationExperiment 2024-04-20 13:08:53,152:INFO:Logging name: clf-default-name 2024-04-20 13:08:53,152:INFO:ML Usecase: MLUsecase.CLASSIFICATION 2024-04-20 13:08:53,152:INFO:version 3.3.0 2024-04-20 13:08:53,152:INFO:Initializing setup() 2024-04-20 13:08:53,152:INFO:self.USI: 7a48 2024-04-20 13:08:53,152:INFO:self._variable_keys: {'log_plots_param', 'html_param', '_available_plots', 'USI', 'exp_name_log', 'X_train', 'seed', 'y_test', 'y_train', 'target_param', 'pipeline', 'fold_groups_param', 'fold_generator', 'fix_imbalance', 'idx', 'fold_shuffle_param', 'gpu_n_jobs_param', '_ml_usecase', 'exp_id', 'memory', 'is_multiclass', 'logging_param', 'y', 'gpu_param', 'n_jobs_param', 'X_test', 'X', 'data'} 2024-04-20 13:08:53,152:INFO:Checking environment 2024-04-20 13:08:53,152:INFO:python_version: 3.11.5 2024-04-20 13:08:53,152:INFO:python_build: ('main', 'Sep 11 2023 13:26:23') 2024-04-20 13:08:53,152:INFO:machine: AMD64 2024-04-20 13:08:53,166:INFO:platform: Windows-10-10.0.22631-SP0 2024-04-20 13:08:53,171:INFO:Memory: svmem(total=16782184448, available=4709572608, percent=71.9, used=12072611840, free=4709572608) 2024-04-20 13:08:53,171:INFO:Physical Core: 10 2024-04-20 13:08:53,171:INFO:Logical Core: 16 2024-04-20 13:08:53,171:INFO:Checking libraries 2024-04-20 13:08:53,171:INFO:System: 2024-04-20 13:08:53,171:INFO: python: 3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:26:23) [MSC v.1916 64 bit (AMD64)] 2024-04-20 13:08:53,171:INFO:executable: c:\Users\arpit\anaconda3\envs\arpit-test\python.exe 2024-04-20 13:08:53,171:INFO: machine: Windows-10-10.0.22631-SP0 2024-04-20 13:08:53,171:INFO:PyCaret required dependencies: 2024-04-20 13:08:53,215:INFO: pip: 23.3 2024-04-20 13:08:53,215:INFO: setuptools: 68.0.0 2024-04-20 13:08:53,215:INFO: pycaret: 3.3.0 2024-04-20 13:08:53,215:INFO: IPython: 8.16.1 2024-04-20 13:08:53,215:INFO: ipywidgets: 8.1.2 2024-04-20 13:08:53,215:INFO: tqdm: 4.66.1 2024-04-20 13:08:53,215:INFO: numpy: 1.23.5 2024-04-20 13:08:53,215:INFO: pandas: 1.5.3 2024-04-20 13:08:53,215:INFO: jinja2: 3.1.2 2024-04-20 13:08:53,215:INFO: scipy: 1.11.4 2024-04-20 13:08:53,215:INFO: joblib: 1.3.2 2024-04-20 13:08:53,215:INFO: sklearn: 1.4.1.post1 2024-04-20 13:08:53,215:INFO: pyod: 1.1.3 2024-04-20 13:08:53,215:INFO: imblearn: 0.12.0 2024-04-20 13:08:53,215:INFO: category_encoders: 2.6.3 2024-04-20 13:08:53,215:INFO: lightgbm: 4.3.0 2024-04-20 13:08:53,215:INFO: numba: 0.58.1 2024-04-20 13:08:53,215:INFO: requests: 2.31.0 2024-04-20 13:08:53,215:INFO: matplotlib: 3.7.5 2024-04-20 13:08:53,216:INFO: scikitplot: 0.3.7 2024-04-20 13:08:53,216:INFO: yellowbrick: 1.5 2024-04-20 13:08:53,216:INFO: plotly: 5.18.0 2024-04-20 13:08:53,216:INFO: plotly-resampler: Not installed 2024-04-20 13:08:53,216:INFO: kaleido: 0.2.1 2024-04-20 13:08:53,216:INFO: schemdraw: 0.15 2024-04-20 13:08:53,216:INFO: statsmodels: 0.14.1 2024-04-20 13:08:53,216:INFO: sktime: 0.26.0 2024-04-20 13:08:53,216:INFO: tbats: 1.1.3 2024-04-20 13:08:53,216:INFO: pmdarima: 2.0.4 2024-04-20 13:08:53,216:INFO: psutil: 5.9.6 2024-04-20 13:08:53,216:INFO: markupsafe: 2.1.3 2024-04-20 13:08:53,216:INFO: pickle5: Not installed 2024-04-20 13:08:53,216:INFO: cloudpickle: 3.0.0 2024-04-20 13:08:53,216:INFO: deprecation: 2.1.0 2024-04-20 13:08:53,216:INFO: xxhash: 3.4.1 2024-04-20 13:08:53,216:INFO: wurlitzer: Not installed 2024-04-20 13:08:53,216:INFO:PyCaret optional dependencies: 2024-04-20 13:08:53,222:INFO: shap: Not installed 2024-04-20 13:08:53,222:INFO: interpret: Not installed 2024-04-20 13:08:53,222:INFO: umap: 0.5.5 2024-04-20 13:08:53,222:INFO: ydata_profiling: 4.6.5 2024-04-20 13:08:53,222:INFO: explainerdashboard: Not installed 2024-04-20 13:08:53,223:INFO: autoviz: Not installed 2024-04-20 13:08:53,223:INFO: fairlearn: Not installed 2024-04-20 13:08:53,223:INFO: deepchecks: Not installed 2024-04-20 13:08:53,223:INFO: xgboost: Not installed 2024-04-20 13:08:53,223:INFO: catboost: Not installed 2024-04-20 13:08:53,223:INFO: kmodes: Not installed 2024-04-20 13:08:53,223:INFO: mlxtend: Not installed 2024-04-20 13:08:53,223:INFO: statsforecast: Not installed 2024-04-20 13:08:53,223:INFO: tune_sklearn: Not installed 2024-04-20 13:08:53,223:INFO: ray: Not installed 2024-04-20 13:08:53,223:INFO: hyperopt: Not installed 2024-04-20 13:08:53,223:INFO: optuna: Not installed 2024-04-20 13:08:53,223:INFO: skopt: Not installed 2024-04-20 13:08:53,223:INFO: mlflow: Not installed 2024-04-20 13:08:53,223:INFO: gradio: Not installed 2024-04-20 13:08:53,223:INFO: fastapi: 0.110.1 2024-04-20 13:08:53,223:INFO: uvicorn: 0.29.0 2024-04-20 13:08:53,223:INFO: m2cgen: Not installed 2024-04-20 13:08:53,223:INFO: evidently: Not installed 2024-04-20 13:08:53,223:INFO: fugue: Not installed 2024-04-20 13:08:53,223:INFO: streamlit: 1.29.0 2024-04-20 13:08:53,223:INFO: prophet: Not installed 2024-04-20 13:08:53,223:INFO:None 2024-04-20 13:08:53,223:INFO:Set up data. 2024-04-20 13:08:53,228:INFO:Set up folding strategy. 2024-04-20 13:08:53,228:INFO:Set up train/test split. 2024-04-20 13:08:53,231:INFO:Set up index. 2024-04-20 13:08:53,231:INFO:Assigning column types. 2024-04-20 13:08:53,233:INFO:Engine successfully changes for model 'lr' to 'sklearn'. 2024-04-20 13:08:53,263:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. 2024-04-20 13:08:53,265:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-20 13:08:53,288:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,288:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,318:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. 2024-04-20 13:08:53,318:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-20 13:08:53,334:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,334:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,334:INFO:Engine successfully changes for model 'knn' to 'sklearn'. 2024-04-20 13:08:53,362:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-20 13:08:53,380:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,380:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,412:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-20 13:08:53,428:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,428:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,428:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'. 2024-04-20 13:08:53,474:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,474:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,521:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,521:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,523:INFO:Preparing preprocessing pipeline... 2024-04-20 13:08:53,523:INFO:Set up label encoding. 2024-04-20 13:08:53,523:INFO:Set up simple imputation. 2024-04-20 13:08:53,540:INFO:Finished creating preprocessing pipeline. 2024-04-20 13:08:53,544:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\arpit\AppData\Local\Temp\joblib), steps=[('label_encoding', TransformerWrapperWithInverse(exclude=None, include=None, transformer=LabelEncoder())), ('numerical_imputer', TransformerWrapper(exclude=None, include=['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'], transformer=SimpleImputer(add_indicator=False, copy=True, fill_value=None, keep_empty_features=False, missing_values=nan, strategy='mean'))), ('categorical_imputer', TransformerWrapper(exclude=None, include=[], transformer=SimpleImputer(add_indicator=False, copy=True, fill_value=None, keep_empty_features=False, missing_values=nan, strategy='most_frequent')))], verbose=False) 2024-04-20 13:08:53,544:INFO:Creating final display dataframe. 2024-04-20 13:08:53,592:INFO:Setup _display_container: Description Value 0 Session id 8818 1 Target Species 2 Target type Multiclass 3 Target mapping Iris-setosa: 0, Iris-versicolor: 1, Iris-virgi... 4 Original data shape (150, 6) 5 Transformed data shape (150, 6) 6 Transformed train set shape (105, 6) 7 Transformed test set shape (45, 6) 8 Numeric features 5 9 Preprocess True 10 Imputation type simple 11 Numeric imputation mean 12 Categorical imputation mode 13 Fold Generator StratifiedKFold 14 Fold Number 10 15 CPU Jobs -1 16 Use GPU False 17 Log Experiment False 18 Experiment Name clf-default-name 19 USI 7a48 2024-04-20 13:08:53,650:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,651:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,705:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,706:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-20 13:08:53,707:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\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( 2024-04-20 13:08:53,707:INFO:setup() successfully completed in 0.56s............... 2024-04-20 13:08:53,707:INFO:Initializing get_config() 2024-04-20 13:08:53,707:INFO:get_config(self=, variable=X_train) 2024-04-20 13:08:53,707:INFO:Variable: 'X_train' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_train_transformed' instead. 2024-04-20 13:08:53,708:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pycaret_experiment\pycaret_experiment.py:321: UserWarning: Variable: 'X_train' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_train_transformed' instead. warnings.warn(msg) # print on screen 2024-04-20 13:08:53,714:INFO:Variable: returned as Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm 144 145 6.7 3.3 5.7 2.5 122 123 7.7 2.8 6.7 2.0 65 66 6.7 3.1 4.4 1.4 52 53 6.9 3.1 4.9 1.5 148 149 6.2 3.4 5.4 2.3 .. ... ... ... ... ... 40 41 5.0 3.5 1.3 0.3 17 18 5.1 3.5 1.4 0.3 71 72 6.1 2.8 4.0 1.3 138 139 6.0 3.0 4.8 1.8 108 109 6.7 2.5 5.8 1.8 [105 rows x 5 columns] 2024-04-20 13:08:53,715:INFO:get_config() successfully completed...................................... 2024-04-20 13:08:53,715:INFO:Initializing get_config() 2024-04-20 13:08:53,715:INFO:get_config(self=, variable=X_test) 2024-04-20 13:08:53,715:INFO:Variable: 'X_test' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_test_transformed' instead. 2024-04-20 13:08:53,716:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pycaret_experiment\pycaret_experiment.py:321: UserWarning: Variable: 'X_test' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_test_transformed' instead. warnings.warn(msg) # print on screen 2024-04-20 13:08:53,724:INFO:Variable: returned as Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm 69 70 5.6 2.5 3.9 1.1 86 87 6.7 3.1 4.7 1.5 149 150 5.9 3.0 5.1 1.8 43 44 5.0 3.5 1.6 0.6 7 8 5.0 3.4 1.5 0.2 11 12 4.8 3.4 1.6 0.2 95 96 5.7 3.0 4.2 1.2 99 100 5.7 2.8 4.1 1.3 50 51 7.0 3.2 4.7 1.4 131 132 7.9 3.8 6.4 2.0 105 106 7.6 3.0 6.6 2.1 117 118 7.7 3.8 6.7 2.2 44 45 5.1 3.8 1.9 0.4 33 34 5.5 4.2 1.4 0.2 90 91 5.5 2.6 4.4 1.2 91 92 6.1 3.0 4.6 1.4 80 81 5.5 2.4 3.8 1.1 75 76 6.6 3.0 4.4 1.4 134 135 6.1 2.6 5.6 1.4 9 10 4.9 3.1 1.5 0.1 68 69 6.2 2.2 4.5 1.5 27 28 5.2 3.5 1.5 0.2 0 1 5.1 3.5 1.4 0.2 25 26 5.0 3.0 1.6 0.2 139 140 6.9 3.1 5.4 2.1 70 71 5.9 3.2 4.8 1.8 15 16 5.7 4.4 1.5 0.4 140 141 6.7 3.1 5.6 2.4 72 73 6.3 2.5 4.9 1.5 2 3 4.7 3.2 1.3 0.2 127 128 6.1 3.0 4.9 1.8 63 64 6.1 2.9 4.7 1.4 141 142 6.9 3.1 5.1 2.3 110 111 6.5 3.2 5.1 2.0 36 37 5.5 3.5 1.3 0.2 57 58 4.9 2.4 3.3 1.0 74 75 6.4 2.9 4.3 1.3 121 122 5.6 2.8 4.9 2.0 41 42 4.5 2.3 1.3 0.3 111 112 6.4 2.7 5.3 1.9 103 104 6.3 2.9 5.6 1.8 130 131 7.4 2.8 6.1 1.9 20 21 5.4 3.4 1.7 0.2 10 11 5.4 3.7 1.5 0.2 132 133 6.4 2.8 5.6 2.2 2024-04-20 13:08:53,724:INFO:get_config() successfully completed...................................... 2024-04-20 13:08:53,725:INFO:Initializing compare_models() 2024-04-20 13:08:53,725:INFO:compare_models(self=, include=None, exclude=['lightgbm', 'catboost', 'xgboost'], 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': , 'include': None, 'exclude': ['lightgbm', 'catboost', 'xgboost'], '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__': }) 2024-04-20 13:08:53,725:INFO:Checking exceptions 2024-04-20 13:08:53,726:INFO:Preparing display monitor 2024-04-20 13:08:53,729:INFO:Initializing Logistic Regression 2024-04-20 13:08:53,729:INFO:Total runtime is 0.0 minutes 2024-04-20 13:08:53,729:INFO:SubProcess create_model() called ================================== 2024-04-20 13:08:53,729:INFO:Initializing create_model() 2024-04-20 13:08:53,729:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 13:08:53,729:INFO:Checking exceptions 2024-04-20 13:08:53,729:INFO:Importing libraries 2024-04-20 13:08:53,729:INFO:Copying training dataset 2024-04-20 13:08:53,731:INFO:Defining folds 2024-04-20 13:08:53,731:INFO:Declaring metric variables 2024-04-20 13:08:53,731:INFO:Importing untrained model 2024-04-20 13:08:53,731:INFO:Logistic Regression Imported successfully 2024-04-20 13:08:53,731:INFO:Starting cross validation 2024-04-20 13:08:53,732:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:08:57,872:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:08:57,882:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:08:57,885:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,888:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,888:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:08:57,890:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,894:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:08:57,897:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:08:57,901:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:08:57,906:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,906:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:08:57,909:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:08:57,910:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,910:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,913:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,914:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,914:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,916:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,917:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,922:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,933:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:08:57,935:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:08:57,938:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:08:57,945:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:08:57,945:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:08:57,947:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:08:57,948:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:08:57,950:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,950:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,952:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,954:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,955:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,956:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,957:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,958:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:08:57,958:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,960:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,960:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:08:57,960:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:08:57,962:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,966:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,969:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,972:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:08:57,972:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:08:57,975:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,975:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,978:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,979:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,980:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,984:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:57,993:INFO:Calculating mean and std 2024-04-20 13:08:57,994:INFO:Creating metrics dataframe 2024-04-20 13:08:57,997:INFO:Uploading results into container 2024-04-20 13:08:57,998:INFO:Uploading model into container now 2024-04-20 13:08:57,999:INFO:_master_model_container: 1 2024-04-20 13:08:57,999:INFO:_display_container: 2 2024-04-20 13:08:57,999: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=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-20 13:08:57,999:INFO:create_model() successfully completed...................................... 2024-04-20 13:08:58,070:INFO:SubProcess create_model() end ================================== 2024-04-20 13:08:58,070:INFO:Creating metrics dataframe 2024-04-20 13:08:58,073:INFO:Initializing K Neighbors Classifier 2024-04-20 13:08:58,073:INFO:Total runtime is 0.0723939855893453 minutes 2024-04-20 13:08:58,073:INFO:SubProcess create_model() called ================================== 2024-04-20 13:08:58,073:INFO:Initializing create_model() 2024-04-20 13:08:58,073:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 13:08:58,073:INFO:Checking exceptions 2024-04-20 13:08:58,073:INFO:Importing libraries 2024-04-20 13:08:58,073:INFO:Copying training dataset 2024-04-20 13:08:58,075:INFO:Defining folds 2024-04-20 13:08:58,075:INFO:Declaring metric variables 2024-04-20 13:08:58,075:INFO:Importing untrained model 2024-04-20 13:08:58,075:INFO:K Neighbors Classifier Imported successfully 2024-04-20 13:08:58,075:INFO:Starting cross validation 2024-04-20 13:08:58,076:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:08:58,130:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:08:58,130:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:08:58,131:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:08:58,133:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:08:58,133:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:58,133:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:08:58,134:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:08:58,136:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:58,136:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:58,137:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:08:58,137:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:58,138:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:58,139:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:58,139:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:58,139:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:58,141:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:58,143:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:08:58,144:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,775:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,777:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,782:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:00,784:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:00,785:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,788:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,788:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,792:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,793:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,793:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,793:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,793:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,794:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,800:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:00,801:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:00,801:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:00,803:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:00,803:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,803:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,804:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,805:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,805:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,806:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,806:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,808:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,808:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,809:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,810:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,811:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,818:INFO:Calculating mean and std 2024-04-20 13:09:00,819:INFO:Creating metrics dataframe 2024-04-20 13:09:00,824:INFO:Uploading results into container 2024-04-20 13:09:00,826:INFO:Uploading model into container now 2024-04-20 13:09:00,826:INFO:_master_model_container: 2 2024-04-20 13:09:00,826:INFO:_display_container: 2 2024-04-20 13:09:00,827:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=-1, n_neighbors=5, p=2, weights='uniform') 2024-04-20 13:09:00,827:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:00,903:INFO:SubProcess create_model() end ================================== 2024-04-20 13:09:00,903:INFO:Creating metrics dataframe 2024-04-20 13:09:00,907:INFO:Initializing Naive Bayes 2024-04-20 13:09:00,907:INFO:Total runtime is 0.11964007218678793 minutes 2024-04-20 13:09:00,907:INFO:SubProcess create_model() called ================================== 2024-04-20 13:09:00,907:INFO:Initializing create_model() 2024-04-20 13:09:00,907:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 13:09:00,907:INFO:Checking exceptions 2024-04-20 13:09:00,907:INFO:Importing libraries 2024-04-20 13:09:00,907:INFO:Copying training dataset 2024-04-20 13:09:00,911:INFO:Defining folds 2024-04-20 13:09:00,911:INFO:Declaring metric variables 2024-04-20 13:09:00,911:INFO:Importing untrained model 2024-04-20 13:09:00,911:INFO:Naive Bayes Imported successfully 2024-04-20 13:09:00,912:INFO:Starting cross validation 2024-04-20 13:09:00,913:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:09:00,947:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,948:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,948:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,948:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,949:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:00,949:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:00,950:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,950:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,950:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:00,951:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,951:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,951:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,951:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:00,951:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,952:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:00,952:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,953:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,953:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,954:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:00,954:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,954:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,954:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,955:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:00,955:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:00,955:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,956:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,956:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,956:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,956:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,957:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,957:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:00,958:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,958:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,958:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,958:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,959:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,959:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,959:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,960:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,960:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:00,961:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,961:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,961:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,961:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,963:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,963:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,966:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,969:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:00,983:INFO:Calculating mean and std 2024-04-20 13:09:00,984:INFO:Creating metrics dataframe 2024-04-20 13:09:00,986:INFO:Uploading results into container 2024-04-20 13:09:00,987:INFO:Uploading model into container now 2024-04-20 13:09:00,987:INFO:_master_model_container: 3 2024-04-20 13:09:00,987:INFO:_display_container: 2 2024-04-20 13:09:00,987:INFO:GaussianNB(priors=None, var_smoothing=1e-09) 2024-04-20 13:09:00,987:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:01,048:INFO:SubProcess create_model() end ================================== 2024-04-20 13:09:01,049:INFO:Creating metrics dataframe 2024-04-20 13:09:01,052:INFO:Initializing Decision Tree Classifier 2024-04-20 13:09:01,052:INFO:Total runtime is 0.12205115159352621 minutes 2024-04-20 13:09:01,052:INFO:SubProcess create_model() called ================================== 2024-04-20 13:09:01,052:INFO:Initializing create_model() 2024-04-20 13:09:01,052:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 13:09:01,053:INFO:Checking exceptions 2024-04-20 13:09:01,053:INFO:Importing libraries 2024-04-20 13:09:01,053:INFO:Copying training dataset 2024-04-20 13:09:01,054:INFO:Defining folds 2024-04-20 13:09:01,054:INFO:Declaring metric variables 2024-04-20 13:09:01,054:INFO:Importing untrained model 2024-04-20 13:09:01,054:INFO:Decision Tree Classifier Imported successfully 2024-04-20 13:09:01,055:INFO:Starting cross validation 2024-04-20 13:09:01,055:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:09:01,078:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,079:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,080:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:01,082:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:01,082:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,082:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,083:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,083:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,083:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,083:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,084:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:01,084:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:01,084:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:01,084:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,085:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:01,085:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,086:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,087:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,088:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,088:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,088:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,089:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,089:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,089:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,091:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,091:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:01,091:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,092:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,091:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,092:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,092:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,092:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,093:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,094:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,094:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,095:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,096:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,096:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:01,096:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,096:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:01,099:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,099:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,099:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,102:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,102:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,103:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,104:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,112:INFO:Calculating mean and std 2024-04-20 13:09:01,113:INFO:Creating metrics dataframe 2024-04-20 13:09:01,115:INFO:Uploading results into container 2024-04-20 13:09:01,116:INFO:Uploading model into container now 2024-04-20 13:09:01,116:INFO:_master_model_container: 4 2024-04-20 13:09:01,116:INFO:_display_container: 2 2024-04-20 13:09:01,116: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=8818, splitter='best') 2024-04-20 13:09:01,116:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:01,180:INFO:SubProcess create_model() end ================================== 2024-04-20 13:09:01,180:INFO:Creating metrics dataframe 2024-04-20 13:09:01,182:INFO:Initializing SVM - Linear Kernel 2024-04-20 13:09:01,183:INFO:Total runtime is 0.1242266058921814 minutes 2024-04-20 13:09:01,183:INFO:SubProcess create_model() called ================================== 2024-04-20 13:09:01,183:INFO:Initializing create_model() 2024-04-20 13:09:01,183:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 13:09:01,183:INFO:Checking exceptions 2024-04-20 13:09:01,183:INFO:Importing libraries 2024-04-20 13:09:01,183:INFO:Copying training dataset 2024-04-20 13:09:01,186:INFO:Defining folds 2024-04-20 13:09:01,186:INFO:Declaring metric variables 2024-04-20 13:09:01,186:INFO:Importing untrained model 2024-04-20 13:09:01,186:INFO:SVM - Linear Kernel Imported successfully 2024-04-20 13:09:01,186:INFO:Starting cross validation 2024-04-20 13:09:01,187:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:09:01,231:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,231:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,233:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,234:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,235:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,237:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,237:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,238:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,239:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( ric.capitalize()} is", len(result)) 2024-04-20 13:09:01,240:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,240:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,241:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,241:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,242:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,242:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:01,243:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,243:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,243:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,243:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,243:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,244:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,244:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,244:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,245:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,245:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,245:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,245:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,246:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:01,247:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,247:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,248:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,248:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:01,249:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,249:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,250:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:01,250:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:01,250:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:01,250:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,251:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,251:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,251:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,251:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,252:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,254:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,266:INFO:Calculating mean and std 2024-04-20 13:09:01,266:INFO:Creating metrics dataframe 2024-04-20 13:09:01,269:INFO:Uploading results into container 2024-04-20 13:09:01,269:INFO:Uploading model into container now 2024-04-20 13:09:01,270:INFO:_master_model_container: 5 2024-04-20 13:09:01,270:INFO:_display_container: 2 2024-04-20 13:09:01,270: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=8818, shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0, warm_start=False) 2024-04-20 13:09:01,270:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:01,331:INFO:SubProcess create_model() end ================================== 2024-04-20 13:09:01,332:INFO:Creating metrics dataframe 2024-04-20 13:09:01,334:INFO:Initializing Ridge Classifier 2024-04-20 13:09:01,334:INFO:Total runtime is 0.12675880988438926 minutes 2024-04-20 13:09:01,336:INFO:SubProcess create_model() called ================================== 2024-04-20 13:09:01,336:INFO:Initializing create_model() 2024-04-20 13:09:01,336:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 13:09:01,336:INFO:Checking exceptions 2024-04-20 13:09:01,336:INFO:Importing libraries 2024-04-20 13:09:01,336:INFO:Copying training dataset 2024-04-20 13:09:01,338:INFO:Defining folds 2024-04-20 13:09:01,338:INFO:Declaring metric variables 2024-04-20 13:09:01,338:INFO:Importing untrained model 2024-04-20 13:09:01,338:INFO:Ridge Classifier Imported successfully 2024-04-20 13:09:01,338:INFO:Starting cross validation 2024-04-20 13:09:01,339:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:09:01,364:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,365:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,366:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,366:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,367:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,367:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,367:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,368:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,369:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,369:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,369:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,369:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,369:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,370:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,370:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,371:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,371:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,371:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,371:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,372:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,372:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-20 13:09:01,373:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,373:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,374:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,374:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,374:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,376:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,376:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,376:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,376:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,377:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,377:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,377:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,378:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,378:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,379:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,380:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,388:INFO:Calculating mean and std 2024-04-20 13:09:01,388:INFO:Creating metrics dataframe 2024-04-20 13:09:01,391:INFO:Uploading results into container 2024-04-20 13:09:01,391:INFO:Uploading model into container now 2024-04-20 13:09:01,391:INFO:_master_model_container: 6 2024-04-20 13:09:01,391:INFO:_display_container: 2 2024-04-20 13:09:01,392:INFO:RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True, max_iter=None, positive=False, random_state=8818, solver='auto', tol=0.0001) 2024-04-20 13:09:01,392:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:01,453:INFO:SubProcess create_model() end ================================== 2024-04-20 13:09:01,453:INFO:Creating metrics dataframe 2024-04-20 13:09:01,456:INFO:Initializing Random Forest Classifier 2024-04-20 13:09:01,456:INFO:Total runtime is 0.12879261573155723 minutes 2024-04-20 13:09:01,456:INFO:SubProcess create_model() called ================================== 2024-04-20 13:09:01,457:INFO:Initializing create_model() 2024-04-20 13:09:01,457:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 13:09:01,457:INFO:Checking exceptions 2024-04-20 13:09:01,457:INFO:Importing libraries 2024-04-20 13:09:01,457:INFO:Copying training dataset 2024-04-20 13:09:01,458:INFO:Defining folds 2024-04-20 13:09:01,458:INFO:Declaring metric variables 2024-04-20 13:09:01,458:INFO:Importing untrained model 2024-04-20 13:09:01,459:INFO:Random Forest Classifier Imported successfully 2024-04-20 13:09:01,459:INFO:Starting cross validation 2024-04-20 13:09:01,459:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:09:01,694:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,694:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,697:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:01,697:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,697:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:01,697:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,698:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:01,699:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,699:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,699:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,699:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:01,699:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,700:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,700:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( o this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,701:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:01,701:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:01,701:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,701:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,703:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:01,703:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,703:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,703:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,704:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,704:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,704:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,704:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,704:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,704:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,707:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:01,707:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,707:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,709:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,709:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,709:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,710:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,710:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,712:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,712:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:01,713:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,714:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,716:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,718:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,718:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,720:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:01,721:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,723:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,725:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,735:INFO:Calculating mean and std 2024-04-20 13:09:01,736:INFO:Creating metrics dataframe 2024-04-20 13:09:01,738:INFO:Uploading results into container 2024-04-20 13:09:01,739:INFO:Uploading model into container now 2024-04-20 13:09:01,739:INFO:_master_model_container: 7 2024-04-20 13:09:01,739:INFO:_display_container: 2 2024-04-20 13:09:01,739: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=8818, verbose=0, warm_start=False) 2024-04-20 13:09:01,739:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:01,805:INFO:SubProcess create_model() end ================================== 2024-04-20 13:09:01,805:INFO:Creating metrics dataframe 2024-04-20 13:09:01,809:INFO:Initializing Quadratic Discriminant Analysis 2024-04-20 13:09:01,809:INFO:Total runtime is 0.13467067082722983 minutes 2024-04-20 13:09:01,809:INFO:SubProcess create_model() called ================================== 2024-04-20 13:09:01,809:INFO:Initializing create_model() 2024-04-20 13:09:01,810:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 13:09:01,810:INFO:Checking exceptions 2024-04-20 13:09:01,810:INFO:Importing libraries 2024-04-20 13:09:01,810:INFO:Copying training dataset 2024-04-20 13:09:01,812:INFO:Defining folds 2024-04-20 13:09:01,812:INFO:Declaring metric variables 2024-04-20 13:09:01,812:INFO:Importing untrained model 2024-04-20 13:09:01,812:INFO:Quadratic Discriminant Analysis Imported successfully 2024-04-20 13:09:01,812:INFO:Starting cross validation 2024-04-20 13:09:01,813:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:09:01,839:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,841:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) \Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:01,841:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,842:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:01,842:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:01,842:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,843:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,843:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,844:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,844:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,846:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:01,846:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:01,847:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,847:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,847:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,848:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,848:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,848:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,848:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,848:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,849:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:01,849:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:01,849:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:01,850:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,850:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,850:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,850:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:01,851:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,852:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,852:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,852:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,852:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,852:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:01,854:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,854:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,855:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,855:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,855:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,856:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,856:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,857:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,857:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,858:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,858:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,859:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,862:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:01,870:INFO:Calculating mean and std 2024-04-20 13:09:01,871:INFO:Creating metrics dataframe 2024-04-20 13:09:01,873:INFO:Uploading results into container 2024-04-20 13:09:01,874:INFO:Uploading model into container now 2024-04-20 13:09:01,874:INFO:_master_model_container: 8 2024-04-20 13:09:01,874:INFO:_display_container: 2 2024-04-20 13:09:01,875:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0, store_covariance=False, tol=0.0001) 2024-04-20 13:09:01,875:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:01,956:INFO:SubProcess create_model() end ================================== 2024-04-20 13:09:01,956:INFO:Creating metrics dataframe 2024-04-20 13:09:01,960:INFO:Initializing Ada Boost Classifier 2024-04-20 13:09:01,960:INFO:Total runtime is 0.13718024094899497 minutes 2024-04-20 13:09:01,960:INFO:SubProcess create_model() called ================================== 2024-04-20 13:09:01,960:INFO:Initializing create_model() 2024-04-20 13:09:01,960:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 13:09:01,960:INFO:Checking exceptions 2024-04-20 13:09:01,960:INFO:Importing libraries 2024-04-20 13:09:01,960:INFO:Copying training dataset 2024-04-20 13:09:01,962:INFO:Defining folds 2024-04-20 13:09:01,963:INFO:Declaring metric variables 2024-04-20 13:09:01,963:INFO:Importing untrained model 2024-04-20 13:09:01,963:INFO:Ada Boost Classifier Imported successfully 2024-04-20 13:09:01,963:INFO:Starting cross validation 2024-04-20 13:09:01,964:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:09:01,979:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 13:09:01,982:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 13:09:01,982:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 13:09:01,982:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 13:09:01,984:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 13:09:01,985:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 13:09:01,986:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 13:09:01,989:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 13:09:01,991:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 13:09:01,991:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-20 13:09:02,082:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,084:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,085:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,088:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,091:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,092:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,092:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,093:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,094:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,096:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,096:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,096:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,097:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,098:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,098:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,098:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,098:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,099:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,099:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,099:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,100:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,100:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,100:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,100:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,100:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,100:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,101:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,102:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,102:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,103:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,103:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,103:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,105:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,105:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,105:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,105:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,106:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,107:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,107:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,107:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,107:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,108:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,108:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,108:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,109:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,109:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,110:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,112:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,125:INFO:Calculating mean and std 2024-04-20 13:09:02,125:INFO:Creating metrics dataframe 2024-04-20 13:09:02,127:INFO:Uploading results into container 2024-04-20 13:09:02,128:INFO:Uploading model into container now 2024-04-20 13:09:02,128:INFO:_master_model_container: 9 2024-04-20 13:09:02,128:INFO:_display_container: 2 2024-04-20 13:09:02,128:INFO:AdaBoostClassifier(algorithm='SAMME.R', estimator=None, learning_rate=1.0, n_estimators=50, random_state=8818) 2024-04-20 13:09:02,128:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:02,186:INFO:SubProcess create_model() end ================================== 2024-04-20 13:09:02,186:INFO:Creating metrics dataframe 2024-04-20 13:09:02,188:INFO:Initializing Gradient Boosting Classifier 2024-04-20 13:09:02,188:INFO:Total runtime is 0.14098578294118247 minutes 2024-04-20 13:09:02,188:INFO:SubProcess create_model() called ================================== 2024-04-20 13:09:02,188:INFO:Initializing create_model() 2024-04-20 13:09:02,189:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 13:09:02,189:INFO:Checking exceptions 2024-04-20 13:09:02,189:INFO:Importing libraries 2024-04-20 13:09:02,189:INFO:Copying training dataset 2024-04-20 13:09:02,190:INFO:Defining folds 2024-04-20 13:09:02,190:INFO:Declaring metric variables 2024-04-20 13:09:02,190:INFO:Importing untrained model 2024-04-20 13:09:02,191:INFO:Gradient Boosting Classifier Imported successfully 2024-04-20 13:09:02,191:INFO:Starting cross validation 2024-04-20 13:09:02,191:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:09:02,448:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,450:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,451:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,452:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,453:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,453:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,454:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,454:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,456:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,456:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,457:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,458:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,458:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,459:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,459:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,459:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,460:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,460:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,460:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,461:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,462:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,462:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,462:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,463:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,464:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,465:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,465:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,466:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,466:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,466:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,467:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,467:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,467:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,467:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,467:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,469:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,469:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,470:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,470:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,472:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,472:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,473:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,474:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,476:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,484:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,485:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,486:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,488:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,491:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,500:INFO:Calculating mean and std 2024-04-20 13:09:02,500:INFO:Creating metrics dataframe 2024-04-20 13:09:02,503:INFO:Uploading results into container 2024-04-20 13:09:02,503:INFO:Uploading model into container now 2024-04-20 13:09:02,503:INFO:_master_model_container: 10 2024-04-20 13:09:02,503:INFO:_display_container: 2 2024-04-20 13:09:02,504: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=8818, subsample=1.0, tol=0.0001, validation_fraction=0.1, verbose=0, warm_start=False) 2024-04-20 13:09:02,504:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:02,562:INFO:SubProcess create_model() end ================================== 2024-04-20 13:09:02,562:INFO:Creating metrics dataframe 2024-04-20 13:09:02,565:INFO:Initializing Linear Discriminant Analysis 2024-04-20 13:09:02,565:INFO:Total runtime is 0.1472667694091797 minutes 2024-04-20 13:09:02,565:INFO:SubProcess create_model() called ================================== 2024-04-20 13:09:02,565:INFO:Initializing create_model() 2024-04-20 13:09:02,565:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 13:09:02,565:INFO:Checking exceptions 2024-04-20 13:09:02,565:INFO:Importing libraries 2024-04-20 13:09:02,565:INFO:Copying training dataset 2024-04-20 13:09:02,567:INFO:Defining folds 2024-04-20 13:09:02,567:INFO:Declaring metric variables 2024-04-20 13:09:02,567:INFO:Importing untrained model 2024-04-20 13:09:02,568:INFO:Linear Discriminant Analysis Imported successfully 2024-04-20 13:09:02,568:INFO:Starting cross validation 2024-04-20 13:09:02,568:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:09:02,587:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,588:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,589:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,590:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,591:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,592:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,593:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,593:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,593:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,594:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,594:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,595:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,595:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,595:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,595:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,596:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,596:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,596:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,597:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,598:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,598:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,599:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,599:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,599:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,599:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,599:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,599:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,599:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,600:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,600:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,601:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,601:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,601:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,601:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,602:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,602:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,603:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,603:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,603:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,603:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,604:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,606:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,606:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,607:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,608:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,608:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,609:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,616:INFO:Calculating mean and std 2024-04-20 13:09:02,618:INFO:Creating metrics dataframe 2024-04-20 13:09:02,620:INFO:Uploading results into container 2024-04-20 13:09:02,620:INFO:Uploading model into container now 2024-04-20 13:09:02,621:INFO:_master_model_container: 11 2024-04-20 13:09:02,621:INFO:_display_container: 2 2024-04-20 13:09:02,621:INFO:LinearDiscriminantAnalysis(covariance_estimator=None, n_components=None, priors=None, shrinkage=None, solver='svd', store_covariance=False, tol=0.0001) 2024-04-20 13:09:02,621:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:02,680:INFO:SubProcess create_model() end ================================== 2024-04-20 13:09:02,680:INFO:Creating metrics dataframe 2024-04-20 13:09:02,683:INFO:Initializing Extra Trees Classifier 2024-04-20 13:09:02,683:INFO:Total runtime is 0.14923770825068158 minutes 2024-04-20 13:09:02,683:INFO:SubProcess create_model() called ================================== 2024-04-20 13:09:02,683:INFO:Initializing create_model() 2024-04-20 13:09:02,683:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 13:09:02,683:INFO:Checking exceptions 2024-04-20 13:09:02,683:INFO:Importing libraries 2024-04-20 13:09:02,683:INFO:Copying training dataset 2024-04-20 13:09:02,685:INFO:Defining folds 2024-04-20 13:09:02,685:INFO:Declaring metric variables 2024-04-20 13:09:02,685:INFO:Importing untrained model 2024-04-20 13:09:02,685:INFO:Extra Trees Classifier Imported successfully 2024-04-20 13:09:02,686:INFO:Starting cross validation 2024-04-20 13:09:02,686:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:09:02,872:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,874:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,874:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,876:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,877:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,877:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,877:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,879:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,879:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,880:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,880:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,880:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,882:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,882:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,883:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,883:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,884:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,884:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:02,884:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,885:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,885:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,885:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,886:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,886:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,886:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,886:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,887:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,888:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,889:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,890:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,890:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,892:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,892:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,894:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,894:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,895:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,896:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,896:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,897:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:02,897:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,898:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,898:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,899:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,899:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,899:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,901:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,902:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,902:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:02,909:INFO:Calculating mean and std 2024-04-20 13:09:02,909:INFO:Creating metrics dataframe 2024-04-20 13:09:02,912:INFO:Uploading results into container 2024-04-20 13:09:02,913:INFO:Uploading model into container now 2024-04-20 13:09:02,913:INFO:_master_model_container: 12 2024-04-20 13:09:02,913:INFO:_display_container: 2 2024-04-20 13:09:02,913: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=8818, verbose=0, warm_start=False) 2024-04-20 13:09:02,913:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:02,973:INFO:SubProcess create_model() end ================================== 2024-04-20 13:09:02,973:INFO:Creating metrics dataframe 2024-04-20 13:09:02,977:INFO:Initializing Dummy Classifier 2024-04-20 13:09:02,977:INFO:Total runtime is 0.1541298270225525 minutes 2024-04-20 13:09:02,977:INFO:SubProcess create_model() called ================================== 2024-04-20 13:09:02,977:INFO:Initializing create_model() 2024-04-20 13:09:02,977:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-20 13:09:02,977:INFO:Checking exceptions 2024-04-20 13:09:02,977:INFO:Importing libraries 2024-04-20 13:09:02,977:INFO:Copying training dataset 2024-04-20 13:09:02,979:INFO:Defining folds 2024-04-20 13:09:02,979:INFO:Declaring metric variables 2024-04-20 13:09:02,979:INFO:Importing untrained model 2024-04-20 13:09:02,979:INFO:Dummy Classifier Imported successfully 2024-04-20 13:09:02,979:INFO:Starting cross validation 2024-04-20 13:09:02,980:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:09:02,998:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:02,999:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:03,001:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:03,002:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,003:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:03,003:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:03,003:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:03,004:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:03,005:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:03,005:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:03,005:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:03,005:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:03,005:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:03,005:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,007:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:03,007:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:03,007:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,008:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,008:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:03,008:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,008:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,008:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:03,009:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,009:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,009:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:03,010:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:03,010:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,010:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:03,011:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,011:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:03,011:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,011:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:03,011:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,012:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,012:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:03,012:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:03,012:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:03,013:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,013:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:03,013:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:03,013:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:03,013:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,013:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,013:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,014:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,014:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,014:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,014:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,015:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,016:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,016:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,017:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:03,018:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:03,018:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) 2024-04-20 13:09:03,019:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,019:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,020:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:03,027:INFO:Calculating mean and std 2024-04-20 13:09:03,027:INFO:Creating metrics dataframe 2024-04-20 13:09:03,031:INFO:Uploading results into container 2024-04-20 13:09:03,031:INFO:Uploading model into container now 2024-04-20 13:09:03,031:INFO:_master_model_container: 13 2024-04-20 13:09:03,031:INFO:_display_container: 2 2024-04-20 13:09:03,032:INFO:DummyClassifier(constant=None, random_state=8818, strategy='prior') 2024-04-20 13:09:03,032:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:03,086:INFO:SubProcess create_model() end ================================== 2024-04-20 13:09:03,086:INFO:Creating metrics dataframe 2024-04-20 13:09:03,089:INFO:Initializing create_model() 2024-04-20 13:09:03,089:INFO:create_model(self=, estimator=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=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), 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={}) 2024-04-20 13:09:03,089:INFO:Checking exceptions 2024-04-20 13:09:03,090:INFO:Importing libraries 2024-04-20 13:09:03,090:INFO:Copying training dataset 2024-04-20 13:09:03,091:INFO:Defining folds 2024-04-20 13:09:03,091:INFO:Declaring metric variables 2024-04-20 13:09:03,092:INFO:Importing untrained model 2024-04-20 13:09:03,092:INFO:Declaring custom model 2024-04-20 13:09:03,092:INFO:Logistic Regression Imported successfully 2024-04-20 13:09:03,092:INFO:Cross validation set to False 2024-04-20 13:09:03,092:INFO:Fitting Model 2024-04-20 13:09:03,125: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=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-20 13:09:03,125:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:03,190:INFO:_master_model_container: 13 2024-04-20 13:09:03,190:INFO:_display_container: 2 2024-04-20 13:09:03,190: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=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-20 13:09:03,190:INFO:compare_models() successfully completed...................................... 2024-04-20 13:09:07,103:INFO:Initializing create_model() 2024-04-20 13:09:07,103:INFO:create_model(self=, estimator=lr, 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={}) 2024-04-20 13:09:07,103:INFO:Checking exceptions 2024-04-20 13:09:07,105:INFO:Importing libraries 2024-04-20 13:09:07,105:INFO:Copying training dataset 2024-04-20 13:09:07,107:INFO:Defining folds 2024-04-20 13:09:07,107:INFO:Declaring metric variables 2024-04-20 13:09:07,107:INFO:Importing untrained model 2024-04-20 13:09:07,107:INFO:Logistic Regression Imported successfully 2024-04-20 13:09:07,107:INFO:Starting cross validation 2024-04-20 13:09:07,107:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:09:07,155:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:07,156:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:07,157:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,159:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,159:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:07,161:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:07,161:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:07,161:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,162:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,162:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:07,163:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:07,163:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,163:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,165:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:07,166:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,167:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,168:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,170:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,170:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:07,170:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,170:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:07,171:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:07,172:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:07,172:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,172:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,174:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,174:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:07,175:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,175:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:07,175:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,178:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,178:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:07,178:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,179:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:07,179:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,180:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,180:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,182:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,183:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,183:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,186:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:07,186:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:07,187:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,189:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,190:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:07,190:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,191:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:07,192:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,193:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,195:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:07,207:INFO:Calculating mean and std 2024-04-20 13:09:07,207:INFO:Creating metrics dataframe 2024-04-20 13:09:07,208:INFO:Finalizing model 2024-04-20 13:09:07,235:INFO:Uploading results into container 2024-04-20 13:09:07,236:INFO:Uploading model into container now 2024-04-20 13:09:07,244:INFO:_master_model_container: 14 2024-04-20 13:09:07,244:INFO:_display_container: 3 2024-04-20 13:09:07,245: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=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-20 13:09:07,245:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:07,319:INFO:Initializing tune_model() 2024-04-20 13:09:07,319:INFO:tune_model(self=, estimator=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=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), 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={}) 2024-04-20 13:09:07,319:INFO:Checking exceptions 2024-04-20 13:09:07,321:INFO:Copying training dataset 2024-04-20 13:09:07,324:INFO:Checking base model 2024-04-20 13:09:07,324:INFO:Base model : Logistic Regression 2024-04-20 13:09:07,324:INFO:Declaring metric variables 2024-04-20 13:09:07,324:INFO:Defining Hyperparameters 2024-04-20 13:09:07,381:INFO:Tuning with n_jobs=-1 2024-04-20 13:09:07,381:INFO:Initializing RandomizedSearchCV 2024-04-20 13:09:08,228:INFO:best_params: {'actual_estimator__class_weight': {}, 'actual_estimator__C': 1.201} 2024-04-20 13:09:08,229:INFO:Hyperparameter search completed 2024-04-20 13:09:08,229:INFO:SubProcess create_model() called ================================== 2024-04-20 13:09:08,229:INFO:Initializing create_model() 2024-04-20 13:09:08,229:INFO:create_model(self=, estimator=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=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'class_weight': {}, 'C': 1.201}) 2024-04-20 13:09:08,229:INFO:Checking exceptions 2024-04-20 13:09:08,229:INFO:Importing libraries 2024-04-20 13:09:08,229:INFO:Copying training dataset 2024-04-20 13:09:08,232:INFO:Defining folds 2024-04-20 13:09:08,232:INFO:Declaring metric variables 2024-04-20 13:09:08,232:INFO:Importing untrained model 2024-04-20 13:09:08,232:INFO:Declaring custom model 2024-04-20 13:09:08,232:INFO:Logistic Regression Imported successfully 2024-04-20 13:09:08,232:INFO:Starting cross validation 2024-04-20 13:09:08,233:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:09:08,306:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,307:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:08,311:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,312:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,314:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:08,314:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,316:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,316:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,317:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,317:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,318:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:08,319:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:08,319:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,320:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,320:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,321:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:08,321:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,324:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,324:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,324:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,325:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,325:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,326:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,326:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,326:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,326:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:08,327:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:08,327:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:08,328:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,328:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,328:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,329:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,329:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,330:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,330:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,332:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,332:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,332:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,335:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,335:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,335:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,337:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:08,337:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:08,338:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,338:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,340:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,340:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,342:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,342:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,353:INFO:Calculating mean and std 2024-04-20 13:09:08,353:INFO:Creating metrics dataframe 2024-04-20 13:09:08,355:INFO:Finalizing model 2024-04-20 13:09:08,387:INFO:Uploading results into container 2024-04-20 13:09:08,387:INFO:Uploading model into container now 2024-04-20 13:09:08,387:INFO:_master_model_container: 15 2024-04-20 13:09:08,387:INFO:_display_container: 4 2024-04-20 13:09:08,388:INFO:LogisticRegression(C=1.201, class_weight={}, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=None, max_iter=1000, multi_class='auto', n_jobs=None, penalty='l2', random_state=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-20 13:09:08,388:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:08,447:INFO:SubProcess create_model() end ================================== 2024-04-20 13:09:08,447:INFO:choose_better activated 2024-04-20 13:09:08,447:INFO:SubProcess create_model() called ================================== 2024-04-20 13:09:08,448:INFO:Initializing create_model() 2024-04-20 13:09:08,448:INFO:create_model(self=, estimator=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=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), 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={}) 2024-04-20 13:09:08,448:INFO:Checking exceptions 2024-04-20 13:09:08,448:INFO:Importing libraries 2024-04-20 13:09:08,448:INFO:Copying training dataset 2024-04-20 13:09:08,450:INFO:Defining folds 2024-04-20 13:09:08,450:INFO:Declaring metric variables 2024-04-20 13:09:08,451:INFO:Importing untrained model 2024-04-20 13:09:08,451:INFO:Declaring custom model 2024-04-20 13:09:08,451:INFO:Logistic Regression Imported successfully 2024-04-20 13:09:08,451:INFO:Starting cross validation 2024-04-20 13:09:08,451:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-20 13:09:08,511:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,512:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,513:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:08,514:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:08,514:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,516:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,518:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,520:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,521:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,528:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,528:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,528:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,529:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,529:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:08,530:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:08,530:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:08,530:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:08,530:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,532:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,532:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,533:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,533:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,534:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,534:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,535:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,535:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,535:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,536:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,536:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:08,536:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-20 13:09:08,537:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,537:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,537:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,538:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,539:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,539:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,542:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,545:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,547:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,548:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:08,549:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,551:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,553:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-20 13:09:08,553:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-20 13:09:08,554:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,555:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,556:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,558:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-20 13:09:08,563:INFO:Calculating mean and std 2024-04-20 13:09:08,563:INFO:Creating metrics dataframe 2024-04-20 13:09:08,564:INFO:Finalizing model 2024-04-20 13:09:08,593:INFO:Uploading results into container 2024-04-20 13:09:08,594:INFO:Uploading model into container now 2024-04-20 13:09:08,594:INFO:_master_model_container: 16 2024-04-20 13:09:08,594:INFO:_display_container: 5 2024-04-20 13:09:08,594: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=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-20 13:09:08,594:INFO:create_model() successfully completed...................................... 2024-04-20 13:09:08,648:INFO:SubProcess create_model() end ================================== 2024-04-20 13:09:08,648: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=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) result for Accuracy is 1.0 2024-04-20 13:09:08,649:INFO:LogisticRegression(C=1.201, class_weight={}, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=None, max_iter=1000, multi_class='auto', n_jobs=None, penalty='l2', random_state=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) result for Accuracy is 1.0 2024-04-20 13:09:08,649: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=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) is best model 2024-04-20 13:09:08,649:INFO:choose_better completed 2024-04-20 13:09:08,649: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). 2024-04-20 13:09:08,656:INFO:_master_model_container: 16 2024-04-20 13:09:08,657:INFO:_display_container: 4 2024-04-20 13:09:08,657: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=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-20 13:09:08,657:INFO:tune_model() successfully completed...................................... 2024-04-20 13:09:08,719:INFO:Initializing evaluate_model() 2024-04-20 13:09:08,719:INFO:evaluate_model(self=, estimator=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=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) 2024-04-20 13:09:08,802:INFO:Initializing plot_model() 2024-04-20 13:09:08,802:INFO:plot_model(self=, estimator=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=8818, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), plot=pipeline, scale=1, save=False, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) 2024-04-20 13:09:08,802:INFO:Checking exceptions 2024-04-20 13:09:08,803:INFO:Preloading libraries 2024-04-20 13:09:08,803:INFO:Copying training dataset 2024-04-20 13:09:08,803:INFO:Plot type: pipeline 2024-04-20 13:09:11,016:INFO:Visual Rendered Successfully 2024-04-20 13:09:11,084:INFO:plot_model() successfully completed...................................... 2024-04-21 17:01:01,315:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:01:01,315:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:01:01,315:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:01:01,315:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:11:32,471:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:11:32,471:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:11:32,471:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:11:32,471:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:11:40,410:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:11:40,410:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:11:40,410:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:11:40,410:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:12:26,944:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:12:26,944:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:12:26,944:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:12:26,944:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:12:31,941:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:12:31,941:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:12:31,941:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:12:31,941:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:12:35,796:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:12:35,796:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:12:35,796:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:12:35,796:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:13:23,949:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:13:23,949:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:13:23,949:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:13:23,949:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:42,946:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:42,946:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:42,946:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:42,946:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:46,639:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:46,639:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:46,639:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:46,639:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:50,130:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:50,130:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:50,130:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:50,130:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:53,882:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:53,882:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:53,882:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:14:53,882:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:15:08,310:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:15:08,310:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:15:08,310:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:15:08,310:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:33,300:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:33,307:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:33,307:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:33,307:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:39,099:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:39,099:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:39,099:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:39,099:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:47,123:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:47,123:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:47,123:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:47,123:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:50,478:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:50,478:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:50,480:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:16:50,480:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:17:00,748:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:17:00,748:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:17:00,748:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:17:00,748:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:17:08,118:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:17:08,118:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:17:08,118:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:17:08,118:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:17:11,940:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:17:11,940:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:17:11,940:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 17:17:11,940:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:27:54,252:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:27:54,252:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:27:54,252:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:27:54,252:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:27:59,306:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:27:59,306:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:27:59,306:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:27:59,306:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:28:17,818:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:28:17,818:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:28:17,818:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:28:17,818:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:28:22,858:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:28:22,858:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:28:22,859:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:28:22,859:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:28:28,147:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:28:28,147:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:28:28,147:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:28:28,147:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:26,976:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:26,976:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:26,976:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:26,976:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:31,652:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:31,652:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:31,652:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:31,652:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:41,252:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:41,252:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:41,252:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:41,252:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:55,855:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:55,856:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:55,856:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:29:55,856:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:30:45,045:INFO:PyCaret ClassificationExperiment 2024-04-21 18:30:45,046:INFO:Logging name: clf-default-name 2024-04-21 18:30:45,046:INFO:ML Usecase: MLUsecase.CLASSIFICATION 2024-04-21 18:30:45,046:INFO:version 3.3.0 2024-04-21 18:30:45,046:INFO:Initializing setup() 2024-04-21 18:30:45,046:INFO:self.USI: cdea 2024-04-21 18:30:45,046:INFO:self._variable_keys: {'exp_id', 'idx', 'fold_groups_param', 'pipeline', 'target_param', 'data', 'fold_generator', 'fold_shuffle_param', 'gpu_param', 'seed', 'fix_imbalance', 'is_multiclass', 'X_test', 'log_plots_param', 'X_train', 'X', '_available_plots', 'memory', 'exp_name_log', 'y_test', 'gpu_n_jobs_param', '_ml_usecase', 'USI', 'html_param', 'y_train', 'y', 'n_jobs_param', 'logging_param'} 2024-04-21 18:30:45,046:INFO:Checking environment 2024-04-21 18:30:45,046:INFO:python_version: 3.11.5 2024-04-21 18:30:45,046:INFO:python_build: ('main', 'Sep 11 2023 13:26:23') 2024-04-21 18:30:45,046:INFO:machine: AMD64 2024-04-21 18:30:45,068:INFO:platform: Windows-10-10.0.22631-SP0 2024-04-21 18:30:45,076:INFO:Memory: svmem(total=16782184448, available=492728320, percent=97.1, used=16289456128, free=492728320) 2024-04-21 18:30:45,076:INFO:Physical Core: 10 2024-04-21 18:30:45,076:INFO:Logical Core: 16 2024-04-21 18:30:45,076:INFO:Checking libraries 2024-04-21 18:30:45,076:INFO:System: 2024-04-21 18:30:45,076:INFO: python: 3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:26:23) [MSC v.1916 64 bit (AMD64)] 2024-04-21 18:30:45,076:INFO:executable: C:\Users\arpit\anaconda3\envs\arpit-test\python.exe 2024-04-21 18:30:45,076:INFO: machine: Windows-10-10.0.22631-SP0 2024-04-21 18:30:45,077:INFO:PyCaret required dependencies: 2024-04-21 18:30:45,157:INFO: pip: 23.3 2024-04-21 18:30:45,157:INFO: setuptools: 68.0.0 2024-04-21 18:30:45,157:INFO: pycaret: 3.3.0 2024-04-21 18:30:45,157:INFO: IPython: 8.16.1 2024-04-21 18:30:45,157:INFO: ipywidgets: 8.1.2 2024-04-21 18:30:45,157:INFO: tqdm: 4.66.1 2024-04-21 18:30:45,157:INFO: numpy: 1.23.5 2024-04-21 18:30:45,157:INFO: pandas: 1.5.3 2024-04-21 18:30:45,157:INFO: jinja2: 3.1.2 2024-04-21 18:30:45,157:INFO: scipy: 1.11.4 2024-04-21 18:30:45,157:INFO: joblib: 1.3.2 2024-04-21 18:30:45,158:INFO: sklearn: 1.4.1.post1 2024-04-21 18:30:45,158:INFO: pyod: 1.1.3 2024-04-21 18:30:45,158:INFO: imblearn: 0.12.0 2024-04-21 18:30:45,158:INFO: category_encoders: 2.6.3 2024-04-21 18:30:45,158:INFO: lightgbm: 4.3.0 2024-04-21 18:30:45,158:INFO: numba: 0.58.1 2024-04-21 18:30:45,158:INFO: requests: 2.31.0 2024-04-21 18:30:45,158:INFO: matplotlib: 3.7.5 2024-04-21 18:30:45,158:INFO: scikitplot: 0.3.7 2024-04-21 18:30:45,158:INFO: yellowbrick: 1.5 2024-04-21 18:30:45,158:INFO: plotly: 5.18.0 2024-04-21 18:30:45,158:INFO: plotly-resampler: Not installed 2024-04-21 18:30:45,158:INFO: kaleido: 0.2.1 2024-04-21 18:30:45,158:INFO: schemdraw: 0.15 2024-04-21 18:30:45,158:INFO: statsmodels: 0.14.1 2024-04-21 18:30:45,158:INFO: sktime: 0.26.0 2024-04-21 18:30:45,158:INFO: tbats: 1.1.3 2024-04-21 18:30:45,158:INFO: pmdarima: 2.0.4 2024-04-21 18:30:45,158:INFO: psutil: 5.9.6 2024-04-21 18:30:45,158:INFO: markupsafe: 2.1.3 2024-04-21 18:30:45,158:INFO: pickle5: Not installed 2024-04-21 18:30:45,158:INFO: cloudpickle: 3.0.0 2024-04-21 18:30:45,158:INFO: deprecation: 2.1.0 2024-04-21 18:30:45,158:INFO: xxhash: 3.4.1 2024-04-21 18:30:45,158:INFO: wurlitzer: Not installed 2024-04-21 18:30:45,158:INFO:PyCaret optional dependencies: 2024-04-21 18:30:45,163:INFO: shap: Not installed 2024-04-21 18:30:45,163:INFO: interpret: Not installed 2024-04-21 18:30:45,163:INFO: umap: 0.5.5 2024-04-21 18:30:45,163:INFO: ydata_profiling: 4.6.5 2024-04-21 18:30:45,163:INFO: explainerdashboard: Not installed 2024-04-21 18:30:45,163:INFO: autoviz: Not installed 2024-04-21 18:30:45,163:INFO: fairlearn: Not installed 2024-04-21 18:30:45,163:INFO: deepchecks: Not installed 2024-04-21 18:30:45,163:INFO: xgboost: Not installed 2024-04-21 18:30:45,163:INFO: catboost: Not installed 2024-04-21 18:30:45,163:INFO: kmodes: Not installed 2024-04-21 18:30:45,163:INFO: mlxtend: Not installed 2024-04-21 18:30:45,163:INFO: statsforecast: Not installed 2024-04-21 18:30:45,163:INFO: tune_sklearn: Not installed 2024-04-21 18:30:45,163:INFO: ray: Not installed 2024-04-21 18:30:45,163:INFO: hyperopt: Not installed 2024-04-21 18:30:45,163:INFO: optuna: Not installed 2024-04-21 18:30:45,163:INFO: skopt: Not installed 2024-04-21 18:30:45,163:INFO: mlflow: Not installed 2024-04-21 18:30:45,163:INFO: gradio: Not installed 2024-04-21 18:30:45,163:INFO: fastapi: 0.110.1 2024-04-21 18:30:45,163:INFO: uvicorn: 0.29.0 2024-04-21 18:30:45,163:INFO: m2cgen: Not installed 2024-04-21 18:30:45,163:INFO: evidently: Not installed 2024-04-21 18:30:45,163:INFO: fugue: Not installed 2024-04-21 18:30:45,163:INFO: streamlit: 1.29.0 2024-04-21 18:30:45,163:INFO: prophet: Not installed 2024-04-21 18:30:45,163:INFO:None 2024-04-21 18:30:45,163:INFO:Set up data. 2024-04-21 18:30:45,172:INFO:Set up folding strategy. 2024-04-21 18:30:45,172:INFO:Set up train/test split. 2024-04-21 18:30:45,179:INFO:Set up index. 2024-04-21 18:30:45,179:INFO:Assigning column types. 2024-04-21 18:30:45,182:INFO:Engine successfully changes for model 'lr' to 'sklearn'. 2024-04-21 18:30:45,220:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. 2024-04-21 18:30:45,225:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 18:30:45,259:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,260:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,296:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. 2024-04-21 18:30:45,297:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 18:30:45,319:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,320:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,320:INFO:Engine successfully changes for model 'knn' to 'sklearn'. 2024-04-21 18:30:45,357:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 18:30:45,381:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,381:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,423:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 18:30:45,447:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,447:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,448:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'. 2024-04-21 18:30:45,511:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,512:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,579:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,579:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,583:INFO:Preparing preprocessing pipeline... 2024-04-21 18:30:45,585:INFO:Set up label encoding. 2024-04-21 18:30:45,586:INFO:Set up simple imputation. 2024-04-21 18:30:45,649:INFO:Finished creating preprocessing pipeline. 2024-04-21 18:30:45,655:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\arpit\AppData\Local\Temp\joblib), steps=[('label_encoding', TransformerWrapperWithInverse(exclude=None, include=None, transformer=LabelEncoder())), ('numerical_imputer', TransformerWrapper(exclude=None, include=['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'], transformer=SimpleImputer(add_indicator=False, copy=True, fill_value=None, keep_empty_features=False, missing_values=nan, strategy='mean'))), ('categorical_imputer', TransformerWrapper(exclude=None, include=[], transformer=SimpleImputer(add_indicator=False, copy=True, fill_value=None, keep_empty_features=False, missing_values=nan, strategy='most_frequent')))], verbose=False) 2024-04-21 18:30:45,656:INFO:Creating final display dataframe. 2024-04-21 18:30:45,747:INFO:Setup _display_container: Description Value 0 Session id 8415 1 Target Species 2 Target type Multiclass 3 Target mapping Iris-setosa: 0, Iris-versicolor: 1, Iris-virgi... 4 Original data shape (150, 6) 5 Transformed data shape (150, 6) 6 Transformed train set shape (105, 6) 7 Transformed test set shape (45, 6) 8 Numeric features 5 9 Preprocess True 10 Imputation type simple 11 Numeric imputation mean 12 Categorical imputation mode 13 Fold Generator StratifiedKFold 14 Fold Number 10 15 CPU Jobs -1 16 Use GPU False 17 Log Experiment False 18 Experiment Name clf-default-name 19 USI cdea 2024-04-21 18:30:45,825:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,825:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,896:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,896:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:30:45,900:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\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 18:30:45,902:INFO:setup() successfully completed in 0.87s............... 2024-04-21 18:30:45,902:INFO:Initializing get_config() 2024-04-21 18:30:45,902:INFO:get_config(self=, variable=X_train) 2024-04-21 18:30:45,902:INFO:Variable: 'X_train' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_train_transformed' instead. 2024-04-21 18:30:45,904:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pycaret_experiment\pycaret_experiment.py:321: UserWarning: Variable: 'X_train' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_train_transformed' instead. warnings.warn(msg) # print on screen 2024-04-21 18:30:45,915:INFO:Variable: returned as Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm 146 147 6.3 2.5 5.0 1.9 74 75 6.4 2.9 4.3 1.3 63 64 6.1 2.9 4.7 1.4 12 13 4.8 3.0 1.4 0.1 11 12 4.8 3.4 1.6 0.2 .. ... ... ... ... ... 4 5 5.0 3.6 1.4 0.2 27 28 5.2 3.5 1.5 0.2 46 47 5.1 3.8 1.6 0.2 127 128 6.1 3.0 4.9 1.8 58 59 6.6 2.9 4.6 1.3 [105 rows x 5 columns] 2024-04-21 18:30:45,915:INFO:get_config() successfully completed...................................... 2024-04-21 18:30:45,915:INFO:Initializing get_config() 2024-04-21 18:30:45,915:INFO:get_config(self=, variable=X_test) 2024-04-21 18:30:45,916:INFO:Variable: 'X_test' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_test_transformed' instead. 2024-04-21 18:30:45,916:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pycaret_experiment\pycaret_experiment.py:321: UserWarning: Variable: 'X_test' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_test_transformed' instead. warnings.warn(msg) # print on screen 2024-04-21 18:30:45,930:INFO:Variable: returned as Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm 51 52 6.4 3.2 4.5 1.5 92 93 5.8 2.6 4.0 1.2 108 109 6.7 2.5 5.8 1.8 90 91 5.5 2.6 4.4 1.2 124 125 6.7 3.3 5.7 2.1 87 88 6.3 2.3 4.4 1.3 126 127 6.2 2.8 4.8 1.8 3 4 4.6 3.1 1.5 0.2 83 84 6.0 2.7 5.1 1.6 59 60 5.2 2.7 3.9 1.4 114 115 5.8 2.8 5.1 2.4 91 92 6.1 3.0 4.6 1.4 89 90 5.5 2.5 4.0 1.3 71 72 6.1 2.8 4.0 1.3 39 40 5.1 3.4 1.5 0.2 112 113 6.8 3.0 5.5 2.1 82 83 5.8 2.7 3.9 1.2 137 138 6.4 3.1 5.5 1.8 98 99 5.1 2.5 3.0 1.1 31 32 5.4 3.4 1.5 0.4 113 114 5.7 2.5 5.0 2.0 95 96 5.7 3.0 4.2 1.2 33 34 5.5 4.2 1.4 0.2 80 81 5.5 2.4 3.8 1.1 117 118 7.7 3.8 6.7 2.2 22 23 4.6 3.6 1.0 0.2 106 107 4.9 2.5 4.5 1.7 119 120 6.0 2.2 5.0 1.5 8 9 4.4 2.9 1.4 0.2 47 48 4.6 3.2 1.4 0.2 37 38 4.9 3.1 1.5 0.1 104 105 6.5 3.0 5.8 2.2 30 31 4.8 3.1 1.6 0.2 16 17 5.4 3.9 1.3 0.4 136 137 6.3 3.4 5.6 2.4 0 1 5.1 3.5 1.4 0.2 28 29 5.2 3.4 1.4 0.2 10 11 5.4 3.7 1.5 0.2 93 94 5.0 2.3 3.3 1.0 133 134 6.3 2.8 5.1 1.5 25 26 5.0 3.0 1.6 0.2 131 132 7.9 3.8 6.4 2.0 101 102 5.8 2.7 5.1 1.9 7 8 5.0 3.4 1.5 0.2 96 97 5.7 2.9 4.2 1.3 2024-04-21 18:30:45,930:INFO:get_config() successfully completed...................................... 2024-04-21 18:30:45,930:INFO:Initializing compare_models() 2024-04-21 18:30:45,930:INFO:compare_models(self=, include=None, exclude=['lightgbm', 'catboost', 'xgboost'], 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': , 'include': None, 'exclude': ['lightgbm', 'catboost', 'xgboost'], '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__': }) 2024-04-21 18:30:45,930:INFO:Checking exceptions 2024-04-21 18:30:45,933:INFO:Preparing display monitor 2024-04-21 18:30:45,939:INFO:Initializing Logistic Regression 2024-04-21 18:30:45,939:INFO:Total runtime is 0.0 minutes 2024-04-21 18:30:45,939:INFO:SubProcess create_model() called ================================== 2024-04-21 18:30:45,939:INFO:Initializing create_model() 2024-04-21 18:30:45,939:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:30:45,939:INFO:Checking exceptions 2024-04-21 18:30:45,939:INFO:Importing libraries 2024-04-21 18:30:45,939:INFO:Copying training dataset 2024-04-21 18:30:45,942:INFO:Defining folds 2024-04-21 18:30:45,942:INFO:Declaring metric variables 2024-04-21 18:30:45,942:INFO:Importing untrained model 2024-04-21 18:30:45,943:INFO:Logistic Regression Imported successfully 2024-04-21 18:30:45,943:INFO:Starting cross validation 2024-04-21 18:30:45,944:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:30:54,072:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:30:54,094:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:30:54,102:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:54,108:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:54,114:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:54,790:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:30:54,805:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:30:54,811:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:54,817:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:54,824:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:54,933:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:30:54,948:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:30:55,014:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,021:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,029:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:30:55,029:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,036:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:30:55,046:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:30:55,054:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,056:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:30:55,059:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,062:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,067:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,067:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,074:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,088:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:30:55,109:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:30:55,115:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:30:55,127:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,139:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:30:55,144:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,148:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,153:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,175:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:30:55,184:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:30:55,191:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:30:55,197:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,197:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:30:55,201:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,201:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,205:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,205:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,208:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,221:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:30:55,236:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:30:55,241:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,247:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,254:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,267:INFO:Calculating mean and std 2024-04-21 18:30:55,269:INFO:Creating metrics dataframe 2024-04-21 18:30:55,274:INFO:Uploading results into container 2024-04-21 18:30:55,275:INFO:Uploading model into container now 2024-04-21 18:30:55,276:INFO:_master_model_container: 1 2024-04-21 18:30:55,276:INFO:_display_container: 2 2024-04-21 18:30:55,277: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=8415, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 18:30:55,277:INFO:create_model() successfully completed...................................... 2024-04-21 18:30:55,371:INFO:SubProcess create_model() end ================================== 2024-04-21 18:30:55,372:INFO:Creating metrics dataframe 2024-04-21 18:30:55,376:INFO:Initializing K Neighbors Classifier 2024-04-21 18:30:55,376:INFO:Total runtime is 0.15728638569513956 minutes 2024-04-21 18:30:55,376:INFO:SubProcess create_model() called ================================== 2024-04-21 18:30:55,376:INFO:Initializing create_model() 2024-04-21 18:30:55,376:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:30:55,376:INFO:Checking exceptions 2024-04-21 18:30:55,376:INFO:Importing libraries 2024-04-21 18:30:55,376:INFO:Copying training dataset 2024-04-21 18:30:55,376:INFO:Defining folds 2024-04-21 18:30:55,376:INFO:Declaring metric variables 2024-04-21 18:30:55,376:INFO:Importing untrained model 2024-04-21 18:30:55,380:INFO:K Neighbors Classifier Imported successfully 2024-04-21 18:30:55,380:INFO:Starting cross validation 2024-04-21 18:30:55,380:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:30:55,451:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:30:55,451:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:30:55,451:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:30:55,461:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:30:55,461:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:30:55,461:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:30:55,461:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:30:55,463:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,464:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,465:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,467:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,469:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,469:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,469:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,472:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,473:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:30:55,474:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,136:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,152:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:00,153:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,153:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,153:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,156:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,156:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,162:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:00,164:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:00,167:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,167:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,167:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:00,167:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,167:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:00,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,179:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,179:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,180:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,198:INFO:Calculating mean and std 2024-04-21 18:31:00,200:INFO:Creating metrics dataframe 2024-04-21 18:31:00,202:INFO:Uploading results into container 2024-04-21 18:31:00,202:INFO:Uploading model into container now 2024-04-21 18:31:00,206:INFO:_master_model_container: 2 2024-04-21 18:31:00,206:INFO:_display_container: 2 2024-04-21 18:31:00,206: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 18:31:00,206:INFO:create_model() successfully completed...................................... 2024-04-21 18:31:00,309:INFO:SubProcess create_model() end ================================== 2024-04-21 18:31:00,309:INFO:Creating metrics dataframe 2024-04-21 18:31:00,314:INFO:Initializing Naive Bayes 2024-04-21 18:31:00,314:INFO:Total runtime is 0.23958607117335 minutes 2024-04-21 18:31:00,314:INFO:SubProcess create_model() called ================================== 2024-04-21 18:31:00,314:INFO:Initializing create_model() 2024-04-21 18:31:00,314:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:31:00,315:INFO:Checking exceptions 2024-04-21 18:31:00,315:INFO:Importing libraries 2024-04-21 18:31:00,315:INFO:Copying training dataset 2024-04-21 18:31:00,318:INFO:Defining folds 2024-04-21 18:31:00,318:INFO:Declaring metric variables 2024-04-21 18:31:00,318:INFO:Importing untrained model 2024-04-21 18:31:00,318:INFO:Naive Bayes Imported successfully 2024-04-21 18:31:00,318:INFO:Starting cross validation 2024-04-21 18:31:00,320:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:31:00,351:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,351:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:00,354:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,355:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,356:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,356:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,356:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:00,357:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,357:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:00,357:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,357:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:00,357:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:00,357:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,357:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,357:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,357:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:00,357:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,357:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,357:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,361:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,361:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,361:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,361:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:00,361:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,361:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,361:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,361:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,361:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:00,365:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,366:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,366:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:00,366:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,367:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,368:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,368:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this 2024-04-21 18:31:00,368:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( \Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:00,369:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,369:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,379:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,380:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,388:INFO:Calculating mean and std 2024-04-21 18:31:00,388:INFO:Creating metrics dataframe 2024-04-21 18:31:00,392:INFO:Uploading results into container 2024-04-21 18:31:00,392:INFO:Uploading model into container now 2024-04-21 18:31:00,393:INFO:_master_model_container: 3 2024-04-21 18:31:00,393:INFO:_display_container: 2 2024-04-21 18:31:00,393:INFO:GaussianNB(priors=None, var_smoothing=1e-09) 2024-04-21 18:31:00,393:INFO:create_model() successfully completed...................................... 2024-04-21 18:31:00,467:INFO:SubProcess create_model() end ================================== 2024-04-21 18:31:00,468:INFO:Creating metrics dataframe 2024-04-21 18:31:00,472:INFO:Initializing Decision Tree Classifier 2024-04-21 18:31:00,472:INFO:Total runtime is 0.2422153353691101 minutes 2024-04-21 18:31:00,472:INFO:SubProcess create_model() called ================================== 2024-04-21 18:31:00,472:INFO:Initializing create_model() 2024-04-21 18:31:00,472:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:31:00,472:INFO:Checking exceptions 2024-04-21 18:31:00,472:INFO:Importing libraries 2024-04-21 18:31:00,472:INFO:Copying training dataset 2024-04-21 18:31:00,475:INFO:Defining folds 2024-04-21 18:31:00,475:INFO:Declaring metric variables 2024-04-21 18:31:00,476:INFO:Importing untrained model 2024-04-21 18:31:00,476:INFO:Decision Tree Classifier Imported successfully 2024-04-21 18:31:00,476:INFO:Starting cross validation 2024-04-21 18:31:00,477:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:31:00,512:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,513:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,513:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,513:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,513:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,513:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,514:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,514:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:00,515:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:00,515:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,515:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:00,515:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:00,515:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:00,516:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:00,517:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:00,517:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,518:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:00,518:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,518:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,519:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,519:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,519:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,520:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:00,520:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:00,520:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,522:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,522:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,522:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,522:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,523:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,523:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,523:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,523:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,524:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,525:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,526:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,526:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,526:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,526:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,526:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,527:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,528:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,528:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,528:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,530:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,530:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,537:INFO:Calculating mean and std 2024-04-21 18:31:00,537:INFO:Creating metrics dataframe 2024-04-21 18:31:00,541:INFO:Uploading results into container 2024-04-21 18:31:00,542:INFO:Uploading model into container now 2024-04-21 18:31:00,542:INFO:_master_model_container: 4 2024-04-21 18:31:00,542:INFO:_display_container: 2 2024-04-21 18:31:00,543: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=8415, splitter='best') 2024-04-21 18:31:00,543:INFO:create_model() successfully completed...................................... 2024-04-21 18:31:00,620:INFO:SubProcess create_model() end ================================== 2024-04-21 18:31:00,620:INFO:Creating metrics dataframe 2024-04-21 18:31:00,625:INFO:Initializing SVM - Linear Kernel 2024-04-21 18:31:00,625:INFO:Total runtime is 0.24477062622706094 minutes 2024-04-21 18:31:00,625:INFO:SubProcess create_model() called ================================== 2024-04-21 18:31:00,625:INFO:Initializing create_model() 2024-04-21 18:31:00,625:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:31:00,625:INFO:Checking exceptions 2024-04-21 18:31:00,626:INFO:Importing libraries 2024-04-21 18:31:00,626:INFO:Copying training dataset 2024-04-21 18:31:00,628:INFO:Defining folds 2024-04-21 18:31:00,628:INFO:Declaring metric variables 2024-04-21 18:31:00,628:INFO:Importing untrained model 2024-04-21 18:31:00,628:INFO:SVM - Linear Kernel Imported successfully 2024-04-21 18:31:00,629:INFO:Starting cross validation 2024-04-21 18:31:00,630:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:31:00,684:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,687:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,687:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,688:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,688:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,691:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,692:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,692:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,692:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,692:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,692:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,692:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,692:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:31:00,692:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,692:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,692:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,692:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:31:00,697:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,697:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,697:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,698:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,698:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,698:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,699:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,699:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,699:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,699:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:31:00,699:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:31:00,699:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,699:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,702:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,702:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( ric.capitalize()} is", len(result)) 2024-04-21 18:31:00,702:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,702:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,702:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,702:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,702:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:31:00,702:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,705:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,707:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,707:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:31:00,708:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,709:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,709:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,712:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,721:INFO:Calculating mean and std 2024-04-21 18:31:00,722:INFO:Creating metrics dataframe 2024-04-21 18:31:00,724:INFO:Uploading results into container 2024-04-21 18:31:00,724:INFO:Uploading model into container now 2024-04-21 18:31:00,724:INFO:_master_model_container: 5 2024-04-21 18:31:00,724:INFO:_display_container: 2 2024-04-21 18:31:00,724: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=8415, shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0, warm_start=False) 2024-04-21 18:31:00,724:INFO:create_model() successfully completed...................................... 2024-04-21 18:31:00,802:INFO:SubProcess create_model() end ================================== 2024-04-21 18:31:00,802:INFO:Creating metrics dataframe 2024-04-21 18:31:00,805:INFO:Initializing Ridge Classifier 2024-04-21 18:31:00,806:INFO:Total runtime is 0.24777209361394245 minutes 2024-04-21 18:31:00,806:INFO:SubProcess create_model() called ================================== 2024-04-21 18:31:00,806:INFO:Initializing create_model() 2024-04-21 18:31:00,806:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:31:00,806:INFO:Checking exceptions 2024-04-21 18:31:00,806:INFO:Importing libraries 2024-04-21 18:31:00,806:INFO:Copying training dataset 2024-04-21 18:31:00,808:INFO:Defining folds 2024-04-21 18:31:00,808:INFO:Declaring metric variables 2024-04-21 18:31:00,808:INFO:Importing untrained model 2024-04-21 18:31:00,808:INFO:Ridge Classifier Imported successfully 2024-04-21 18:31:00,808:INFO:Starting cross validation 2024-04-21 18:31:00,809:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:31:00,854:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,854:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,854:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,854:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,854:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,854:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,854:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,854:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,859:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,859:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,859:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,861:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,861:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,861:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,864:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,864:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,864:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,864:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,864:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,873:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,877:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,882:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,883:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,883:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,885:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,886:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:31:00,886:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,887:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,888:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,888:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,888:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,890:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,892:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,892:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,895:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,895:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,896:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:00,912:INFO:Calculating mean and std 2024-04-21 18:31:00,912:INFO:Creating metrics dataframe 2024-04-21 18:31:00,918:INFO:Uploading results into container 2024-04-21 18:31:00,919:INFO:Uploading model into container now 2024-04-21 18:31:00,919:INFO:_master_model_container: 6 2024-04-21 18:31:00,919:INFO:_display_container: 2 2024-04-21 18:31:00,920:INFO:RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True, max_iter=None, positive=False, random_state=8415, solver='auto', tol=0.0001) 2024-04-21 18:31:00,920:INFO:create_model() successfully completed...................................... 2024-04-21 18:31:01,006:INFO:SubProcess create_model() end ================================== 2024-04-21 18:31:01,006:INFO:Creating metrics dataframe 2024-04-21 18:31:01,011:INFO:Initializing Random Forest Classifier 2024-04-21 18:31:01,011:INFO:Total runtime is 0.25120869477589924 minutes 2024-04-21 18:31:01,012:INFO:SubProcess create_model() called ================================== 2024-04-21 18:31:01,012:INFO:Initializing create_model() 2024-04-21 18:31:01,012:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:31:01,012:INFO:Checking exceptions 2024-04-21 18:31:01,012:INFO:Importing libraries 2024-04-21 18:31:01,012:INFO:Copying training dataset 2024-04-21 18:31:01,017:INFO:Defining folds 2024-04-21 18:31:01,017:INFO:Declaring metric variables 2024-04-21 18:31:01,018:INFO:Importing untrained model 2024-04-21 18:31:01,018:INFO:Random Forest Classifier Imported successfully 2024-04-21 18:31:01,018:INFO:Starting cross validation 2024-04-21 18:31:01,019:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:31:01,375:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,386:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:01,387:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,388:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,390:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,390:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:01,391:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:01,393:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,393:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,394:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,397:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,397:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,398:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,398:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:01,398:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,400:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,401:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:01,400:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,401:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:01,401:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:01,401:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,403:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,404:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,405:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,405:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,405:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,406:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,409:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,409:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,409:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,409:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:01,410:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,411:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,413:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,415:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,415:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,415:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,418:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,418:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,419:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,422:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:01,422:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,425:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,425:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,428:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:01,428:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,430:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,431:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,433:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,436:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,444:INFO:Calculating mean and std 2024-04-21 18:31:01,446:INFO:Creating metrics dataframe 2024-04-21 18:31:01,449:INFO:Uploading results into container 2024-04-21 18:31:01,450:INFO:Uploading model into container now 2024-04-21 18:31:01,450:INFO:_master_model_container: 7 2024-04-21 18:31:01,450:INFO:_display_container: 2 2024-04-21 18:31:01,451: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=8415, verbose=0, warm_start=False) 2024-04-21 18:31:01,451:INFO:create_model() successfully completed...................................... 2024-04-21 18:31:01,528:INFO:SubProcess create_model() end ================================== 2024-04-21 18:31:01,528:INFO:Creating metrics dataframe 2024-04-21 18:31:01,535:INFO:Initializing Quadratic Discriminant Analysis 2024-04-21 18:31:01,535:INFO:Total runtime is 0.2599389712015788 minutes 2024-04-21 18:31:01,535:INFO:SubProcess create_model() called ================================== 2024-04-21 18:31:01,536:INFO:Initializing create_model() 2024-04-21 18:31:01,536:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:31:01,536:INFO:Checking exceptions 2024-04-21 18:31:01,536:INFO:Importing libraries 2024-04-21 18:31:01,536:INFO:Copying training dataset 2024-04-21 18:31:01,538:INFO:Defining folds 2024-04-21 18:31:01,538:INFO:Declaring metric variables 2024-04-21 18:31:01,538:INFO:Importing untrained model 2024-04-21 18:31:01,538:INFO:Quadratic Discriminant Analysis Imported successfully 2024-04-21 18:31:01,539:INFO:Starting cross validation 2024-04-21 18:31:01,540:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:31:01,574:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:01,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:01,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:01,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:01,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:01,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:01,579:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,579:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:01,579:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,579:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:01,579:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,580:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,581:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,581:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,581:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( o this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,583:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,583:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,583:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:01,583:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,583:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,583:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,583:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,583:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,583:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,583:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:01,587:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,587:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,587:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,587:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,587:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,587:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,587:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,587:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,589:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,589:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,591:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,591:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,597:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,610:INFO:Calculating mean and std 2024-04-21 18:31:01,611:INFO:Creating metrics dataframe 2024-04-21 18:31:01,614:INFO:Uploading results into container 2024-04-21 18:31:01,615:INFO:Uploading model into container now 2024-04-21 18:31:01,615:INFO:_master_model_container: 8 2024-04-21 18:31:01,615:INFO:_display_container: 2 2024-04-21 18:31:01,615:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0, store_covariance=False, tol=0.0001) 2024-04-21 18:31:01,616:INFO:create_model() successfully completed...................................... 2024-04-21 18:31:01,689:INFO:SubProcess create_model() end ================================== 2024-04-21 18:31:01,689:INFO:Creating metrics dataframe 2024-04-21 18:31:01,693:INFO:Initializing Ada Boost Classifier 2024-04-21 18:31:01,693:INFO:Total runtime is 0.2625745415687561 minutes 2024-04-21 18:31:01,693:INFO:SubProcess create_model() called ================================== 2024-04-21 18:31:01,694:INFO:Initializing create_model() 2024-04-21 18:31:01,694:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:31:01,694:INFO:Checking exceptions 2024-04-21 18:31:01,694:INFO:Importing libraries 2024-04-21 18:31:01,694:INFO:Copying training dataset 2024-04-21 18:31:01,696:INFO:Defining folds 2024-04-21 18:31:01,696:INFO:Declaring metric variables 2024-04-21 18:31:01,696:INFO:Importing untrained model 2024-04-21 18:31:01,696:INFO:Ada Boost Classifier Imported successfully 2024-04-21 18:31:01,697:INFO:Starting cross validation 2024-04-21 18:31:01,697:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:31:01,720:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:31:01,720:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:31:01,721:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:31:01,721:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:31:01,724:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:31:01,727:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:31:01,727:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:31:01,728:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:31:01,728:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:31:01,729:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:31:01,866:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,872:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:01,872:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,873:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,874:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,875:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:01,875:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,875:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:01,876:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:01,876:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,877:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,878:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,878:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:01,878:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,879:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,879:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:01,880:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,880:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,882:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,883:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,884:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,884:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,885:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,885:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,887:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,888:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,888:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,889:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,889:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,889:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,891:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:01,892:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,892:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,892:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,892:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:01,894:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,894:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:01,895:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:01,895:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:01,895:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,897:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,897:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,901:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,902:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,902:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,902:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,906:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,906:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,907:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:01,923:INFO:Calculating mean and std 2024-04-21 18:31:01,924:INFO:Creating metrics dataframe 2024-04-21 18:31:01,926:INFO:Uploading results into container 2024-04-21 18:31:01,928:INFO:Uploading model into container now 2024-04-21 18:31:01,928:INFO:_master_model_container: 9 2024-04-21 18:31:01,928:INFO:_display_container: 2 2024-04-21 18:31:01,929:INFO:AdaBoostClassifier(algorithm='SAMME.R', estimator=None, learning_rate=1.0, n_estimators=50, random_state=8415) 2024-04-21 18:31:01,929:INFO:create_model() successfully completed...................................... 2024-04-21 18:31:02,005:INFO:SubProcess create_model() end ================================== 2024-04-21 18:31:02,005:INFO:Creating metrics dataframe 2024-04-21 18:31:02,010:INFO:Initializing Gradient Boosting Classifier 2024-04-21 18:31:02,010:INFO:Total runtime is 0.2678564667701721 minutes 2024-04-21 18:31:02,010:INFO:SubProcess create_model() called ================================== 2024-04-21 18:31:02,010:INFO:Initializing create_model() 2024-04-21 18:31:02,010:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:31:02,010:INFO:Checking exceptions 2024-04-21 18:31:02,010:INFO:Importing libraries 2024-04-21 18:31:02,011:INFO:Copying training dataset 2024-04-21 18:31:02,012:INFO:Defining folds 2024-04-21 18:31:02,013:INFO:Declaring metric variables 2024-04-21 18:31:02,013:INFO:Importing untrained model 2024-04-21 18:31:02,013:INFO:Gradient Boosting Classifier Imported successfully 2024-04-21 18:31:02,014:INFO:Starting cross validation 2024-04-21 18:31:02,015:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:31:02,416:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,417:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,419:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:02,419:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:02,419:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,419:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,421:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,421:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:02,422:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,422:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:02,424:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,424:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,425:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,425:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,426:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:02,426:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,428:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,430:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,430:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,430:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,431:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,435:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( o this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,435:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,435:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,437:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:02,439:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,439:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,440:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,442:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:02,445:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,445:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,446:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,447:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,449:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:02,449:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,450:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,451:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:02,452:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,453:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,455:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,456:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,456:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,458:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,459:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,459:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,460:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:02,463:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,467:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,468:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,479:INFO:Calculating mean and std 2024-04-21 18:31:02,480:INFO:Creating metrics dataframe 2024-04-21 18:31:02,481:INFO:Uploading results into container 2024-04-21 18:31:02,481:INFO:Uploading model into container now 2024-04-21 18:31:02,481:INFO:_master_model_container: 10 2024-04-21 18:31:02,481:INFO:_display_container: 2 2024-04-21 18:31:02,481: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=8415, subsample=1.0, tol=0.0001, validation_fraction=0.1, verbose=0, warm_start=False) 2024-04-21 18:31:02,481:INFO:create_model() successfully completed...................................... 2024-04-21 18:31:02,557:INFO:SubProcess create_model() end ================================== 2024-04-21 18:31:02,557:INFO:Creating metrics dataframe 2024-04-21 18:31:02,560:INFO:Initializing Linear Discriminant Analysis 2024-04-21 18:31:02,560:INFO:Total runtime is 0.277026093006134 minutes 2024-04-21 18:31:02,560:INFO:SubProcess create_model() called ================================== 2024-04-21 18:31:02,560:INFO:Initializing create_model() 2024-04-21 18:31:02,560:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:31:02,560:INFO:Checking exceptions 2024-04-21 18:31:02,560:INFO:Importing libraries 2024-04-21 18:31:02,560:INFO:Copying training dataset 2024-04-21 18:31:02,562:INFO:Defining folds 2024-04-21 18:31:02,563:INFO:Declaring metric variables 2024-04-21 18:31:02,563:INFO:Importing untrained model 2024-04-21 18:31:02,563:INFO:Linear Discriminant Analysis Imported successfully 2024-04-21 18:31:02,563:INFO:Starting cross validation 2024-04-21 18:31:02,564:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:31:02,602:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,603:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,603:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,603:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:02,603:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,603:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) \Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:02,605:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,605:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,605:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,606:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:02,606:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:02,606:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,606:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:02,607:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,607:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:02,606:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:02,607:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:02,608:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:02,608:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,608:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,608:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,608:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,609:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,610:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,610:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,610:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,611:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,611:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,612:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,612:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,613:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,613:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,613:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,614:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,614:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,614:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,614:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,615:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,616:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,616:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,616:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,617:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,618:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,634:INFO:Calculating mean and std 2024-04-21 18:31:02,635:INFO:Creating metrics dataframe 2024-04-21 18:31:02,638:INFO:Uploading results into container 2024-04-21 18:31:02,638:INFO:Uploading model into container now 2024-04-21 18:31:02,639:INFO:_master_model_container: 11 2024-04-21 18:31:02,639:INFO:_display_container: 2 2024-04-21 18:31:02,639:INFO:LinearDiscriminantAnalysis(covariance_estimator=None, n_components=None, priors=None, shrinkage=None, solver='svd', store_covariance=False, tol=0.0001) 2024-04-21 18:31:02,639:INFO:create_model() successfully completed...................................... 2024-04-21 18:31:02,713:INFO:SubProcess create_model() end ================================== 2024-04-21 18:31:02,713:INFO:Creating metrics dataframe 2024-04-21 18:31:02,717:INFO:Initializing Extra Trees Classifier 2024-04-21 18:31:02,717:INFO:Total runtime is 0.2796422282854716 minutes 2024-04-21 18:31:02,719:INFO:SubProcess create_model() called ================================== 2024-04-21 18:31:02,719:INFO:Initializing create_model() 2024-04-21 18:31:02,719:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:31:02,719:INFO:Checking exceptions 2024-04-21 18:31:02,719:INFO:Importing libraries 2024-04-21 18:31:02,719:INFO:Copying training dataset 2024-04-21 18:31:02,721:INFO:Defining folds 2024-04-21 18:31:02,721:INFO:Declaring metric variables 2024-04-21 18:31:02,721:INFO:Importing untrained model 2024-04-21 18:31:02,722:INFO:Extra Trees Classifier Imported successfully 2024-04-21 18:31:02,722:INFO:Starting cross validation 2024-04-21 18:31:02,723:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:31:02,982:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,985:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,985:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:02,987:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,989:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:02,989:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,989:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,989:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:02,993:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,994:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:02,994:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,994:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,997:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:02,997:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( o this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,999:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:02,999:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,001:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,001:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:03,003:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,003:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,003:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,003:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:03,003:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,006:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,008:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,008:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:03,010:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,010:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:03,011:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,011:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,012:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:03,012:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,012:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:03,016:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:03,016:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,016:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,016:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,016:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,021:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:03,021:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,021:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,024:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:03,025:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,026:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,027:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,028:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,030:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,033:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,040:INFO:Calculating mean and std 2024-04-21 18:31:03,041:INFO:Creating metrics dataframe 2024-04-21 18:31:03,041:INFO:Uploading results into container 2024-04-21 18:31:03,041:INFO:Uploading model into container now 2024-04-21 18:31:03,041:INFO:_master_model_container: 12 2024-04-21 18:31:03,041:INFO:_display_container: 2 2024-04-21 18:31:03,046: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=8415, verbose=0, warm_start=False) 2024-04-21 18:31:03,047:INFO:create_model() successfully completed...................................... 2024-04-21 18:31:03,128:INFO:SubProcess create_model() end ================================== 2024-04-21 18:31:03,128:INFO:Creating metrics dataframe 2024-04-21 18:31:03,134:INFO:Initializing Dummy Classifier 2024-04-21 18:31:03,134:INFO:Total runtime is 0.2865895628929138 minutes 2024-04-21 18:31:03,134:INFO:SubProcess create_model() called ================================== 2024-04-21 18:31:03,135:INFO:Initializing create_model() 2024-04-21 18:31:03,135:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:31:03,135:INFO:Checking exceptions 2024-04-21 18:31:03,135:INFO:Importing libraries 2024-04-21 18:31:03,135:INFO:Copying training dataset 2024-04-21 18:31:03,137:INFO:Defining folds 2024-04-21 18:31:03,137:INFO:Declaring metric variables 2024-04-21 18:31:03,137:INFO:Importing untrained model 2024-04-21 18:31:03,138:INFO:Dummy Classifier Imported successfully 2024-04-21 18:31:03,138:INFO:Starting cross validation 2024-04-21 18:31:03,139:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:31:03,164:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:03,166:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:03,167:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:03,167:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:03,168:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:03,168:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:03,169:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,170:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,170:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,170:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:03,172:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:03,173:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:03,174:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,174:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,174:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:03,174:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,175:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,176:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:31:03,176:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:31:03,176:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:31:03,176:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:03,177:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:03,178:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,178:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,179:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,179:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,179:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,180:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,180:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:03,180:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:03,181:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:03,182:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:31:03,182:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:03,183:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,183:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,183:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:03,184:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,184:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:31:03,184:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,185:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,185:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:31:03,185:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:31:03,186:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,188:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,188:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,188:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,188:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,188:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,190:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:31:03,191:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,191:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:31:03,191:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,193:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:31:03,193:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,194:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,194:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,195:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:31:03,197:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:31:03,208:INFO:Calculating mean and std 2024-04-21 18:31:03,209:INFO:Creating metrics dataframe 2024-04-21 18:31:03,214:INFO:Uploading results into container 2024-04-21 18:31:03,214:INFO:Uploading model into container now 2024-04-21 18:31:03,215:INFO:_master_model_container: 13 2024-04-21 18:31:03,215:INFO:_display_container: 2 2024-04-21 18:31:03,215:INFO:DummyClassifier(constant=None, random_state=8415, strategy='prior') 2024-04-21 18:31:03,216:INFO:create_model() successfully completed...................................... 2024-04-21 18:31:03,303:INFO:SubProcess create_model() end ================================== 2024-04-21 18:31:03,303:INFO:Creating metrics dataframe 2024-04-21 18:31:03,308:INFO:Initializing create_model() 2024-04-21 18:31:03,309:INFO:create_model(self=, estimator=GaussianNB(priors=None, var_smoothing=1e-09), 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={}) 2024-04-21 18:31:03,309:INFO:Checking exceptions 2024-04-21 18:31:03,309:INFO:Importing libraries 2024-04-21 18:31:03,309:INFO:Copying training dataset 2024-04-21 18:31:03,311:INFO:Defining folds 2024-04-21 18:31:03,311:INFO:Declaring metric variables 2024-04-21 18:31:03,312:INFO:Importing untrained model 2024-04-21 18:31:03,312:INFO:Declaring custom model 2024-04-21 18:31:03,313:INFO:Naive Bayes Imported successfully 2024-04-21 18:31:03,313:INFO:Cross validation set to False 2024-04-21 18:31:03,313:INFO:Fitting Model 2024-04-21 18:31:03,319:INFO:GaussianNB(priors=None, var_smoothing=1e-09) 2024-04-21 18:31:03,319:INFO:create_model() successfully completed...................................... 2024-04-21 18:31:03,408:INFO:_master_model_container: 13 2024-04-21 18:31:03,408:INFO:_display_container: 2 2024-04-21 18:31:03,408:INFO:GaussianNB(priors=None, var_smoothing=1e-09) 2024-04-21 18:31:03,408:INFO:compare_models() successfully completed...................................... 2024-04-21 18:31:33,058:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:33,058:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:33,058:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:33,058:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:38,489:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:38,489:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:38,489:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:38,489:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:43,735:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:43,735:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:43,735:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:43,735:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:48,791:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:48,791:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:48,791:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:48,791:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:31:56,299:INFO:PyCaret ClassificationExperiment 2024-04-21 18:31:56,299:INFO:Logging name: clf-default-name 2024-04-21 18:31:56,299:INFO:ML Usecase: MLUsecase.CLASSIFICATION 2024-04-21 18:31:56,299:INFO:version 3.3.0 2024-04-21 18:31:56,299:INFO:Initializing setup() 2024-04-21 18:31:56,299:INFO:self.USI: fa39 2024-04-21 18:31:56,300:INFO:self._variable_keys: {'y', 'fold_generator', 'X_test', 'exp_name_log', 'pipeline', 'gpu_param', 'log_plots_param', 'is_multiclass', 'X_train', 'USI', 'y_train', 'fix_imbalance', 'y_test', 'seed', 'html_param', 'n_jobs_param', '_available_plots', 'logging_param', 'gpu_n_jobs_param', 'memory', 'fold_groups_param', '_ml_usecase', 'idx', 'exp_id', 'data', 'target_param', 'X', 'fold_shuffle_param'} 2024-04-21 18:31:56,300:INFO:Checking environment 2024-04-21 18:31:56,300:INFO:python_version: 3.11.5 2024-04-21 18:31:56,300:INFO:python_build: ('main', 'Sep 11 2023 13:26:23') 2024-04-21 18:31:56,300:INFO:machine: AMD64 2024-04-21 18:31:56,320:INFO:platform: Windows-10-10.0.22631-SP0 2024-04-21 18:31:56,327:INFO:Memory: svmem(total=16782184448, available=2597138432, percent=84.5, used=14185046016, free=2597138432) 2024-04-21 18:31:56,327:INFO:Physical Core: 10 2024-04-21 18:31:56,327:INFO:Logical Core: 16 2024-04-21 18:31:56,327:INFO:Checking libraries 2024-04-21 18:31:56,327:INFO:System: 2024-04-21 18:31:56,327:INFO: python: 3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:26:23) [MSC v.1916 64 bit (AMD64)] 2024-04-21 18:31:56,327:INFO:executable: C:\Users\arpit\anaconda3\envs\arpit-test\python.exe 2024-04-21 18:31:56,327:INFO: machine: Windows-10-10.0.22631-SP0 2024-04-21 18:31:56,328:INFO:PyCaret required dependencies: 2024-04-21 18:31:56,395:INFO: pip: 23.3 2024-04-21 18:31:56,395:INFO: setuptools: 68.0.0 2024-04-21 18:31:56,395:INFO: pycaret: 3.3.0 2024-04-21 18:31:56,396:INFO: IPython: 8.16.1 2024-04-21 18:31:56,396:INFO: ipywidgets: 8.1.2 2024-04-21 18:31:56,396:INFO: tqdm: 4.66.1 2024-04-21 18:31:56,396:INFO: numpy: 1.23.5 2024-04-21 18:31:56,396:INFO: pandas: 1.5.3 2024-04-21 18:31:56,396:INFO: jinja2: 3.1.2 2024-04-21 18:31:56,396:INFO: scipy: 1.11.4 2024-04-21 18:31:56,396:INFO: joblib: 1.3.2 2024-04-21 18:31:56,396:INFO: sklearn: 1.4.1.post1 2024-04-21 18:31:56,396:INFO: pyod: 1.1.3 2024-04-21 18:31:56,396:INFO: imblearn: 0.12.0 2024-04-21 18:31:56,396:INFO: category_encoders: 2.6.3 2024-04-21 18:31:56,396:INFO: lightgbm: 4.3.0 2024-04-21 18:31:56,396:INFO: numba: 0.58.1 2024-04-21 18:31:56,396:INFO: requests: 2.31.0 2024-04-21 18:31:56,396:INFO: matplotlib: 3.7.5 2024-04-21 18:31:56,396:INFO: scikitplot: 0.3.7 2024-04-21 18:31:56,396:INFO: yellowbrick: 1.5 2024-04-21 18:31:56,396:INFO: plotly: 5.18.0 2024-04-21 18:31:56,396:INFO: plotly-resampler: Not installed 2024-04-21 18:31:56,396:INFO: kaleido: 0.2.1 2024-04-21 18:31:56,396:INFO: schemdraw: 0.15 2024-04-21 18:31:56,396:INFO: statsmodels: 0.14.1 2024-04-21 18:31:56,396:INFO: sktime: 0.26.0 2024-04-21 18:31:56,396:INFO: tbats: 1.1.3 2024-04-21 18:31:56,396:INFO: pmdarima: 2.0.4 2024-04-21 18:31:56,396:INFO: psutil: 5.9.6 2024-04-21 18:31:56,396:INFO: markupsafe: 2.1.3 2024-04-21 18:31:56,396:INFO: pickle5: Not installed 2024-04-21 18:31:56,396:INFO: cloudpickle: 3.0.0 2024-04-21 18:31:56,396:INFO: deprecation: 2.1.0 2024-04-21 18:31:56,396:INFO: xxhash: 3.4.1 2024-04-21 18:31:56,396:INFO: wurlitzer: Not installed 2024-04-21 18:31:56,396:INFO:PyCaret optional dependencies: 2024-04-21 18:31:56,405:INFO: shap: Not installed 2024-04-21 18:31:56,405:INFO: interpret: Not installed 2024-04-21 18:31:56,405:INFO: umap: 0.5.5 2024-04-21 18:31:56,405:INFO: ydata_profiling: 4.6.5 2024-04-21 18:31:56,405:INFO: explainerdashboard: Not installed 2024-04-21 18:31:56,405:INFO: autoviz: Not installed 2024-04-21 18:31:56,405:INFO: fairlearn: Not installed 2024-04-21 18:31:56,405:INFO: deepchecks: Not installed 2024-04-21 18:31:56,405:INFO: xgboost: Not installed 2024-04-21 18:31:56,405:INFO: catboost: Not installed 2024-04-21 18:31:56,405:INFO: kmodes: Not installed 2024-04-21 18:31:56,405:INFO: mlxtend: Not installed 2024-04-21 18:31:56,405:INFO: statsforecast: Not installed 2024-04-21 18:31:56,405:INFO: tune_sklearn: Not installed 2024-04-21 18:31:56,405:INFO: ray: Not installed 2024-04-21 18:31:56,405:INFO: hyperopt: Not installed 2024-04-21 18:31:56,405:INFO: optuna: Not installed 2024-04-21 18:31:56,405:INFO: skopt: Not installed 2024-04-21 18:31:56,406:INFO: mlflow: Not installed 2024-04-21 18:31:56,406:INFO: gradio: Not installed 2024-04-21 18:31:56,406:INFO: fastapi: 0.110.1 2024-04-21 18:31:56,406:INFO: uvicorn: 0.29.0 2024-04-21 18:31:56,406:INFO: m2cgen: Not installed 2024-04-21 18:31:56,406:INFO: evidently: Not installed 2024-04-21 18:31:56,406:INFO: fugue: Not installed 2024-04-21 18:31:56,406:INFO: streamlit: 1.29.0 2024-04-21 18:31:56,406:INFO: prophet: Not installed 2024-04-21 18:31:56,406:INFO:None 2024-04-21 18:31:56,406:INFO:Set up data. 2024-04-21 18:31:56,412:INFO:Set up folding strategy. 2024-04-21 18:31:56,412:INFO:Set up train/test split. 2024-04-21 18:31:56,416:INFO:Set up index. 2024-04-21 18:31:56,417:INFO:Assigning column types. 2024-04-21 18:31:56,420:INFO:Engine successfully changes for model 'lr' to 'sklearn'. 2024-04-21 18:31:56,458:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. 2024-04-21 18:31:56,462:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 18:31:56,499:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:56,499:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:56,552:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. 2024-04-21 18:31:56,553:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 18:31:56,586:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:56,587:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:56,587:INFO:Engine successfully changes for model 'knn' to 'sklearn'. 2024-04-21 18:31:56,624:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 18:31:56,655:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:56,656:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:56,694:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 18:31:56,717:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:56,718:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:56,718:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'. 2024-04-21 18:31:56,771:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:56,771:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:56,837:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:56,837:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:56,841:INFO:Preparing preprocessing pipeline... 2024-04-21 18:31:56,841:INFO:Set up label encoding. 2024-04-21 18:31:56,841:INFO:Set up simple imputation. 2024-04-21 18:31:56,870:INFO:Finished creating preprocessing pipeline. 2024-04-21 18:31:56,877:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\arpit\AppData\Local\Temp\joblib), steps=[('label_encoding', TransformerWrapperWithInverse(exclude=None, include=None, transformer=LabelEncoder())), ('numerical_imputer', TransformerWrapper(exclude=None, include=['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'], transformer=SimpleImputer(add_indicator=False, copy=True, fill_value=None, keep_empty_features=False, missing_values=nan, strategy='mean'))), ('categorical_imputer', TransformerWrapper(exclude=None, include=[], transformer=SimpleImputer(add_indicator=False, copy=True, fill_value=None, keep_empty_features=False, missing_values=nan, strategy='most_frequent')))], verbose=False) 2024-04-21 18:31:56,877:INFO:Creating final display dataframe. 2024-04-21 18:31:56,962:INFO:Setup _display_container: Description Value 0 Session id 6763 1 Target Species 2 Target type Multiclass 3 Target mapping Iris-setosa: 0, Iris-versicolor: 1, Iris-virgi... 4 Original data shape (150, 6) 5 Transformed data shape (150, 6) 6 Transformed train set shape (105, 6) 7 Transformed test set shape (45, 6) 8 Numeric features 5 9 Preprocess True 10 Imputation type simple 11 Numeric imputation mean 12 Categorical imputation mode 13 Fold Generator StratifiedKFold 14 Fold Number 10 15 CPU Jobs -1 16 Use GPU False 17 Log Experiment False 18 Experiment Name clf-default-name 19 USI fa39 2024-04-21 18:31:57,043:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:57,043:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:57,121:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:57,121:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:31:57,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\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 18:31:57,130:INFO:setup() successfully completed in 0.84s............... 2024-04-21 18:31:57,130:INFO:Initializing get_config() 2024-04-21 18:31:57,130:INFO:get_config(self=, variable=X_train) 2024-04-21 18:31:57,131:INFO:Variable: 'X_train' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_train_transformed' instead. 2024-04-21 18:31:57,133:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pycaret_experiment\pycaret_experiment.py:321: UserWarning: Variable: 'X_train' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_train_transformed' instead. warnings.warn(msg) # print on screen 2024-04-21 18:31:57,146:INFO:Variable: returned as Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm 121 122 5.6 2.8 4.9 2.0 23 24 5.1 3.3 1.7 0.5 24 25 4.8 3.4 1.9 0.2 5 6 5.4 3.9 1.7 0.4 73 74 6.1 2.8 4.7 1.2 .. ... ... ... ... ... 135 136 7.7 3.0 6.1 2.3 125 126 7.2 3.2 6.0 1.8 71 72 6.1 2.8 4.0 1.3 108 109 6.7 2.5 5.8 1.8 111 112 6.4 2.7 5.3 1.9 [105 rows x 5 columns] 2024-04-21 18:31:57,146:INFO:get_config() successfully completed...................................... 2024-04-21 18:31:57,146:INFO:Initializing get_config() 2024-04-21 18:31:57,146:INFO:get_config(self=, variable=X_test) 2024-04-21 18:31:57,146:INFO:Variable: 'X_test' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_test_transformed' instead. 2024-04-21 18:31:57,146:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pycaret_experiment\pycaret_experiment.py:321: UserWarning: Variable: 'X_test' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_test_transformed' instead. warnings.warn(msg) # print on screen 2024-04-21 18:31:57,173:INFO:Variable: returned as Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm 29 30 4.7 3.2 1.6 0.2 129 130 7.2 3.0 5.8 1.6 22 23 4.6 3.6 1.0 0.2 46 47 5.1 3.8 1.6 0.2 127 128 6.1 3.0 4.9 1.8 12 13 4.8 3.0 1.4 0.1 143 144 6.8 3.2 5.9 2.3 8 9 4.4 2.9 1.4 0.2 60 61 5.0 2.0 3.5 1.0 2 3 4.7 3.2 1.3 0.2 116 117 6.5 3.0 5.5 1.8 21 22 5.1 3.7 1.5 0.4 11 12 4.8 3.4 1.6 0.2 1 2 4.9 3.0 1.4 0.2 134 135 6.1 2.6 5.6 1.4 137 138 6.4 3.1 5.5 1.8 139 140 6.9 3.1 5.4 2.1 141 142 6.9 3.1 5.1 2.3 93 94 5.0 2.3 3.3 1.0 113 114 5.7 2.5 5.0 2.0 20 21 5.4 3.4 1.7 0.2 40 41 5.0 3.5 1.3 0.3 78 79 6.0 2.9 4.5 1.5 87 88 6.3 2.3 4.4 1.3 147 148 6.5 3.0 5.2 2.0 95 96 5.7 3.0 4.2 1.2 90 91 5.5 2.6 4.4 1.2 107 108 7.3 2.9 6.3 1.8 117 118 7.7 3.8 6.7 2.2 33 34 5.5 4.2 1.4 0.2 84 85 5.4 3.0 4.5 1.5 79 80 5.7 2.6 3.5 1.0 126 127 6.2 2.8 4.8 1.8 92 93 5.8 2.6 4.0 1.2 86 87 6.7 3.1 4.7 1.5 61 62 5.9 3.0 4.2 1.5 115 116 6.4 3.2 5.3 2.3 51 52 6.4 3.2 4.5 1.5 70 71 5.9 3.2 4.8 1.8 59 60 5.2 2.7 3.9 1.4 63 64 6.1 2.9 4.7 1.4 36 37 5.5 3.5 1.3 0.2 131 132 7.9 3.8 6.4 2.0 4 5 5.0 3.6 1.4 0.2 28 29 5.2 3.4 1.4 0.2 2024-04-21 18:31:57,173:INFO:get_config() successfully completed...................................... 2024-04-21 18:31:57,173:INFO:Initializing compare_models() 2024-04-21 18:31:57,174:INFO:compare_models(self=, include=None, exclude=['lightgbm', 'catboost', 'xgboost'], 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': , 'include': None, 'exclude': ['lightgbm', 'catboost', 'xgboost'], '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__': }) 2024-04-21 18:31:57,174:INFO:Checking exceptions 2024-04-21 18:31:57,177:INFO:Preparing display monitor 2024-04-21 18:31:57,181:INFO:Initializing Logistic Regression 2024-04-21 18:31:57,181:INFO:Total runtime is 0.0 minutes 2024-04-21 18:31:57,182:INFO:SubProcess create_model() called ================================== 2024-04-21 18:31:57,182:INFO:Initializing create_model() 2024-04-21 18:31:57,182:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:31:57,182:INFO:Checking exceptions 2024-04-21 18:31:57,182:INFO:Importing libraries 2024-04-21 18:31:57,182:INFO:Copying training dataset 2024-04-21 18:31:57,184:INFO:Defining folds 2024-04-21 18:31:57,184:INFO:Declaring metric variables 2024-04-21 18:31:57,184:INFO:Importing untrained model 2024-04-21 18:31:57,184:INFO:Logistic Regression Imported successfully 2024-04-21 18:31:57,184:INFO:Starting cross validation 2024-04-21 18:31:57,184:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:03,943:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:03,964:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:03,964:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:03,971:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:03,977:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:03,981:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:03,981:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:03,981:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:03,986:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:03,986:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:03,991:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:03,995:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:03,995:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:04,001:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,002:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:04,002:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,002:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:04,005:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) tion.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:04,011:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( o this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:04,102:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,102:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,102:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,106:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,109:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,109:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:04,109:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:04,111:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,111:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:04,111:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:04,111:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,111:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,117:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,117:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,117:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,119:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,119:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,126:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,126:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,126:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,140:INFO:Calculating mean and std 2024-04-21 18:32:04,141:INFO:Creating metrics dataframe 2024-04-21 18:32:04,141:INFO:Uploading results into container 2024-04-21 18:32:04,149:INFO:Uploading model into container now 2024-04-21 18:32:04,149:INFO:_master_model_container: 1 2024-04-21 18:32:04,149:INFO:_display_container: 2 2024-04-21 18:32:04,149: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=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 18:32:04,149:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:04,251:INFO:SubProcess create_model() end ================================== 2024-04-21 18:32:04,251:INFO:Creating metrics dataframe 2024-04-21 18:32:04,251:INFO:Initializing K Neighbors Classifier 2024-04-21 18:32:04,251:INFO:Total runtime is 0.11783594290415446 minutes 2024-04-21 18:32:04,251:INFO:SubProcess create_model() called ================================== 2024-04-21 18:32:04,251:INFO:Initializing create_model() 2024-04-21 18:32:04,251:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:32:04,251:INFO:Checking exceptions 2024-04-21 18:32:04,251:INFO:Importing libraries 2024-04-21 18:32:04,251:INFO:Copying training dataset 2024-04-21 18:32:04,256:INFO:Defining folds 2024-04-21 18:32:04,256:INFO:Declaring metric variables 2024-04-21 18:32:04,256:INFO:Importing untrained model 2024-04-21 18:32:04,256:INFO:K Neighbors Classifier Imported successfully 2024-04-21 18:32:04,256:INFO:Starting cross validation 2024-04-21 18:32:04,256:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:04,323:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:04,351:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:04,351:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:04,351:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:04,351:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:04,351:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,351:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,351:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,351:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,360:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,360:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,360:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,361:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,361:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,361:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,361:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:04,361:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:09,724:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:09,724:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:09,741:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:09,746:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:09,746:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:09,751:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:09,752:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:09,755:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:09,756:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:09,803:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:09,812:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:09,821:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:09,826:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:09,830:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:09,974:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:09,988:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:09,994:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:09,997:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:09,998:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,001:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,014:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:10,019:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,019:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,025:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,029:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,034:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:10,039:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,044:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,051:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,068:INFO:Calculating mean and std 2024-04-21 18:32:10,069:INFO:Creating metrics dataframe 2024-04-21 18:32:10,078:INFO:Uploading results into container 2024-04-21 18:32:10,079:INFO:Uploading model into container now 2024-04-21 18:32:10,080:INFO:_master_model_container: 2 2024-04-21 18:32:10,080:INFO:_display_container: 2 2024-04-21 18:32:10,081: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 18:32:10,081:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:10,204:INFO:SubProcess create_model() end ================================== 2024-04-21 18:32:10,204:INFO:Creating metrics dataframe 2024-04-21 18:32:10,211:INFO:Initializing Naive Bayes 2024-04-21 18:32:10,211:INFO:Total runtime is 0.21716034412384033 minutes 2024-04-21 18:32:10,211:INFO:SubProcess create_model() called ================================== 2024-04-21 18:32:10,211:INFO:Initializing create_model() 2024-04-21 18:32:10,211:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:32:10,211:INFO:Checking exceptions 2024-04-21 18:32:10,211:INFO:Importing libraries 2024-04-21 18:32:10,212:INFO:Copying training dataset 2024-04-21 18:32:10,215:INFO:Defining folds 2024-04-21 18:32:10,215:INFO:Declaring metric variables 2024-04-21 18:32:10,215:INFO:Importing untrained model 2024-04-21 18:32:10,216:INFO:Naive Bayes Imported successfully 2024-04-21 18:32:10,216:INFO:Starting cross validation 2024-04-21 18:32:10,217:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:10,261:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,265:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,265:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:10,265:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,268:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:10,268:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:10,271:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,271:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,271:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,271:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,271:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:10,271:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,276:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,276:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,276:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,276:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:10,278:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,278:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,281:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,278:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,281:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:10,281:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( o this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,281:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) \Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:10,281:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,281:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:10,281:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,286:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,286:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,286:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:10,286:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,286:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,286:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:10,286:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,289:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,291:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,291:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,291:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,291:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,291:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,291:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,299:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,299:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,299:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,301:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,302:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,308:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,309:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,320:INFO:Calculating mean and std 2024-04-21 18:32:10,321:INFO:Creating metrics dataframe 2024-04-21 18:32:10,325:INFO:Uploading results into container 2024-04-21 18:32:10,326:INFO:Uploading model into container now 2024-04-21 18:32:10,326:INFO:_master_model_container: 3 2024-04-21 18:32:10,326:INFO:_display_container: 2 2024-04-21 18:32:10,326:INFO:GaussianNB(priors=None, var_smoothing=1e-09) 2024-04-21 18:32:10,326:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:10,413:INFO:SubProcess create_model() end ================================== 2024-04-21 18:32:10,413:INFO:Creating metrics dataframe 2024-04-21 18:32:10,419:INFO:Initializing Decision Tree Classifier 2024-04-21 18:32:10,420:INFO:Total runtime is 0.22064371506373087 minutes 2024-04-21 18:32:10,420:INFO:SubProcess create_model() called ================================== 2024-04-21 18:32:10,421:INFO:Initializing create_model() 2024-04-21 18:32:10,421:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:32:10,421:INFO:Checking exceptions 2024-04-21 18:32:10,421:INFO:Importing libraries 2024-04-21 18:32:10,421:INFO:Copying training dataset 2024-04-21 18:32:10,422:INFO:Defining folds 2024-04-21 18:32:10,422:INFO:Declaring metric variables 2024-04-21 18:32:10,426:INFO:Importing untrained model 2024-04-21 18:32:10,426:INFO:Decision Tree Classifier Imported successfully 2024-04-21 18:32:10,426:INFO:Starting cross validation 2024-04-21 18:32:10,426:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:10,464:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,466:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:10,468:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,468:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,471:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,471:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:10,472:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:10,472:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:10,473:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,473:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,474:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,474:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,475:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,475:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,476:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,476:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:10,477:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:10,477:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:10,478:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,478:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,478:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:10,480:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,480:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,480:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,480:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,480:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,480:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,480:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,480:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,481:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:10,482:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:10,482:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:10,482:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,482:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,482:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,482:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,482:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,482:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,486:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,487:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,490:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,491:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,491:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,492:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,494:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,494:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,500:INFO:Calculating mean and std 2024-04-21 18:32:10,501:INFO:Creating metrics dataframe 2024-04-21 18:32:10,505:INFO:Uploading results into container 2024-04-21 18:32:10,505:INFO:Uploading model into container now 2024-04-21 18:32:10,506:INFO:_master_model_container: 4 2024-04-21 18:32:10,506:INFO:_display_container: 2 2024-04-21 18:32:10,506: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=6763, splitter='best') 2024-04-21 18:32:10,506:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:10,579:INFO:SubProcess create_model() end ================================== 2024-04-21 18:32:10,580:INFO:Creating metrics dataframe 2024-04-21 18:32:10,584:INFO:Initializing SVM - Linear Kernel 2024-04-21 18:32:10,584:INFO:Total runtime is 0.22337988217671711 minutes 2024-04-21 18:32:10,584:INFO:SubProcess create_model() called ================================== 2024-04-21 18:32:10,584:INFO:Initializing create_model() 2024-04-21 18:32:10,584:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:32:10,584:INFO:Checking exceptions 2024-04-21 18:32:10,584:INFO:Importing libraries 2024-04-21 18:32:10,584:INFO:Copying training dataset 2024-04-21 18:32:10,587:INFO:Defining folds 2024-04-21 18:32:10,587:INFO:Declaring metric variables 2024-04-21 18:32:10,587:INFO:Importing untrained model 2024-04-21 18:32:10,588:INFO:SVM - Linear Kernel Imported successfully 2024-04-21 18:32:10,588:INFO:Starting cross validation 2024-04-21 18:32:10,588:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:10,644:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,646:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,647:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,648:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,648:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,648:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,648:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,652:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,652:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,652:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,652:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,652:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,652:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,652:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,652:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,652:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,652:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,652:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,652:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,652:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:10,652:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,657:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,657:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,657:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,657:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,657:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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"{met2024-04-21 18:32:10,657:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,657:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,657:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,661:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:10,661:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:10,661:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:10,661:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,661:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,661:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,661:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,661:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:10,661:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,661:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,667:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,668:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:10,668:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:10,672:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,673:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,689:INFO:Calculating mean and std 2024-04-21 18:32:10,690:INFO:Creating metrics dataframe 2024-04-21 18:32:10,694:INFO:Uploading results into container 2024-04-21 18:32:10,694:INFO:Uploading model into container now 2024-04-21 18:32:10,695:INFO:_master_model_container: 5 2024-04-21 18:32:10,695:INFO:_display_container: 2 2024-04-21 18:32:10,695: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=6763, shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0, warm_start=False) 2024-04-21 18:32:10,695:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:10,766:INFO:SubProcess create_model() end ================================== 2024-04-21 18:32:10,766:INFO:Creating metrics dataframe 2024-04-21 18:32:10,771:INFO:Initializing Ridge Classifier 2024-04-21 18:32:10,771:INFO:Total runtime is 0.2265034000078837 minutes 2024-04-21 18:32:10,771:INFO:SubProcess create_model() called ================================== 2024-04-21 18:32:10,771:INFO:Initializing create_model() 2024-04-21 18:32:10,771:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:32:10,771:INFO:Checking exceptions 2024-04-21 18:32:10,771:INFO:Importing libraries 2024-04-21 18:32:10,771:INFO:Copying training dataset 2024-04-21 18:32:10,777:INFO:Defining folds 2024-04-21 18:32:10,777:INFO:Declaring metric variables 2024-04-21 18:32:10,777:INFO:Importing untrained model 2024-04-21 18:32:10,777:INFO:Ridge Classifier Imported successfully 2024-04-21 18:32:10,778:INFO:Starting cross validation 2024-04-21 18:32:10,779:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:10,833:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,841:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,841:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,841:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,846:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,851:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,851:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,856:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,856:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,856:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,866:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,871:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,881:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,881:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,891:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,901:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,915:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,915:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,915:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,921:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,921:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,931:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,931:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,941:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,951:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,964:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,977:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,977:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:10,981:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,988:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,992:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:10,993:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:11,012:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:11,031:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:11,042:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:11,051:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:11,051:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:32:11,057:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:11,086:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:11,094:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:11,108:INFO:Calculating mean and std 2024-04-21 18:32:11,121:INFO:Creating metrics dataframe 2024-04-21 18:32:11,137:INFO:Uploading results into container 2024-04-21 18:32:11,139:INFO:Uploading model into container now 2024-04-21 18:32:11,141:INFO:_master_model_container: 6 2024-04-21 18:32:11,141:INFO:_display_container: 2 2024-04-21 18:32:11,141:INFO:RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True, max_iter=None, positive=False, random_state=6763, solver='auto', tol=0.0001) 2024-04-21 18:32:11,141:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:11,288:INFO:SubProcess create_model() end ================================== 2024-04-21 18:32:11,288:INFO:Creating metrics dataframe 2024-04-21 18:32:11,303:INFO:Initializing Random Forest Classifier 2024-04-21 18:32:11,304:INFO:Total runtime is 0.23536841869354247 minutes 2024-04-21 18:32:11,304:INFO:SubProcess create_model() called ================================== 2024-04-21 18:32:11,305:INFO:Initializing create_model() 2024-04-21 18:32:11,305:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:32:11,306:INFO:Checking exceptions 2024-04-21 18:32:11,306:INFO:Importing libraries 2024-04-21 18:32:11,306:INFO:Copying training dataset 2024-04-21 18:32:11,314:INFO:Defining folds 2024-04-21 18:32:11,314:INFO:Declaring metric variables 2024-04-21 18:32:11,315:INFO:Importing untrained model 2024-04-21 18:32:11,315:INFO:Random Forest Classifier Imported successfully 2024-04-21 18:32:11,316:INFO:Starting cross validation 2024-04-21 18:32:11,317:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:12,591:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:12,591:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:12,596:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:12,596:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:12,596:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,596:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,601:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,601:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,606:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,606:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,643:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:12,646:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:12,648:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:12,648:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:12,648:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,651:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,651:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,651:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,651:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,658:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,693:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:12,693:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:12,696:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,700:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:12,701:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:12,705:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,705:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,705:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:12,705:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,705:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,705:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:12,711:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,711:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,711:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,711:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,746:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:12,746:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:12,746:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,753:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,753:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,793:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:12,793:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:12,793:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,803:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,803:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,866:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:12,871:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:12,871:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,876:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,880:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:12,886:INFO:Calculating mean and std 2024-04-21 18:32:12,886:INFO:Creating metrics dataframe 2024-04-21 18:32:12,891:INFO:Uploading results into container 2024-04-21 18:32:12,891:INFO:Uploading model into container now 2024-04-21 18:32:12,891:INFO:_master_model_container: 7 2024-04-21 18:32:12,891:INFO:_display_container: 2 2024-04-21 18:32:12,891: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=6763, verbose=0, warm_start=False) 2024-04-21 18:32:12,891:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:13,011:INFO:SubProcess create_model() end ================================== 2024-04-21 18:32:13,011:INFO:Creating metrics dataframe 2024-04-21 18:32:13,019:INFO:Initializing Quadratic Discriminant Analysis 2024-04-21 18:32:13,019:INFO:Total runtime is 0.2639717857042948 minutes 2024-04-21 18:32:13,019:INFO:SubProcess create_model() called ================================== 2024-04-21 18:32:13,019:INFO:Initializing create_model() 2024-04-21 18:32:13,019:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:32:13,019:INFO:Checking exceptions 2024-04-21 18:32:13,019:INFO:Importing libraries 2024-04-21 18:32:13,019:INFO:Copying training dataset 2024-04-21 18:32:13,026:INFO:Defining folds 2024-04-21 18:32:13,027:INFO:Declaring metric variables 2024-04-21 18:32:13,027:INFO:Importing untrained model 2024-04-21 18:32:13,028:INFO:Quadratic Discriminant Analysis Imported successfully 2024-04-21 18:32:13,028:INFO:Starting cross validation 2024-04-21 18:32:13,028:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:13,071:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,075:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:13,075:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,075:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,084:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,084:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,084:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:13,084:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,084:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,091:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:13,091:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,091:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,091:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,091:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,091:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,091:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:13,091:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,091:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,101:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,101:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,115:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,115:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:13,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:13,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:13,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,131:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,131:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,151:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,151:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:13,151:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,151:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,151:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,161:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:13,167:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,167:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:13,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,181:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,191:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,221:INFO:Calculating mean and std 2024-04-21 18:32:13,227:INFO:Creating metrics dataframe 2024-04-21 18:32:13,232:INFO:Uploading results into container 2024-04-21 18:32:13,233:INFO:Uploading model into container now 2024-04-21 18:32:13,235:INFO:_master_model_container: 8 2024-04-21 18:32:13,235:INFO:_display_container: 2 2024-04-21 18:32:13,236:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0, store_covariance=False, tol=0.0001) 2024-04-21 18:32:13,236:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:13,323:INFO:SubProcess create_model() end ================================== 2024-04-21 18:32:13,323:INFO:Creating metrics dataframe 2024-04-21 18:32:13,336:INFO:Initializing Ada Boost Classifier 2024-04-21 18:32:13,336:INFO:Total runtime is 0.26925267775853473 minutes 2024-04-21 18:32:13,336:INFO:SubProcess create_model() called ================================== 2024-04-21 18:32:13,336:INFO:Initializing create_model() 2024-04-21 18:32:13,336:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:32:13,336:INFO:Checking exceptions 2024-04-21 18:32:13,336:INFO:Importing libraries 2024-04-21 18:32:13,336:INFO:Copying training dataset 2024-04-21 18:32:13,344:INFO:Defining folds 2024-04-21 18:32:13,344:INFO:Declaring metric variables 2024-04-21 18:32:13,344:INFO:Importing untrained model 2024-04-21 18:32:13,344:INFO:Ada Boost Classifier Imported successfully 2024-04-21 18:32:13,345:INFO:Starting cross validation 2024-04-21 18:32:13,345:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:13,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:32:13,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:32:13,401:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:32:13,422:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:32:13,422:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:32:13,441:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:32:13,451:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:32:13,451:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:32:13,451:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:32:13,625:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:32:13,701:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,721:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:13,721:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,725:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,725:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:13,725:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,731:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,736:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,736:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,741:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,847:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,851:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:13,851:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,851:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,851:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,857:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,859:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:13,861:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,861:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,873:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:13,873:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,873:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,873:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,881:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,885:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,952:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:13,952:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:13,952:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,961:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:13,963:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:14,021:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:14,021:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:14,021:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:14,026:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:14,031:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:14,036:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:14,041:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:14,041:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:14,040:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:14,057:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:14,060:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:14,063:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:14,063:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:14,068:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:14,068:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:14,072:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:14,081:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:14,087:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:14,092:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:14,098:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:14,120:INFO:Calculating mean and std 2024-04-21 18:32:14,122:INFO:Creating metrics dataframe 2024-04-21 18:32:14,126:INFO:Uploading results into container 2024-04-21 18:32:14,128:INFO:Uploading model into container now 2024-04-21 18:32:14,128:INFO:_master_model_container: 9 2024-04-21 18:32:14,128:INFO:_display_container: 2 2024-04-21 18:32:14,129:INFO:AdaBoostClassifier(algorithm='SAMME.R', estimator=None, learning_rate=1.0, n_estimators=50, random_state=6763) 2024-04-21 18:32:14,129:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:14,221:INFO:SubProcess create_model() end ================================== 2024-04-21 18:32:14,221:INFO:Creating metrics dataframe 2024-04-21 18:32:14,237:INFO:Initializing Gradient Boosting Classifier 2024-04-21 18:32:14,237:INFO:Total runtime is 0.28426775137583415 minutes 2024-04-21 18:32:14,237:INFO:SubProcess create_model() called ================================== 2024-04-21 18:32:14,237:INFO:Initializing create_model() 2024-04-21 18:32:14,237:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:32:14,237:INFO:Checking exceptions 2024-04-21 18:32:14,237:INFO:Importing libraries 2024-04-21 18:32:14,237:INFO:Copying training dataset 2024-04-21 18:32:14,243:INFO:Defining folds 2024-04-21 18:32:14,243:INFO:Declaring metric variables 2024-04-21 18:32:14,244:INFO:Importing untrained model 2024-04-21 18:32:14,244:INFO:Gradient Boosting Classifier Imported successfully 2024-04-21 18:32:14,244:INFO:Starting cross validation 2024-04-21 18:32:14,244:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:15,447:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:15,449:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:15,451:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,451:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,457:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:15,457:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,461:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:15,461:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,538:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,541:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,595:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:15,595:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:15,601:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,601:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,601:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,709:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:15,712:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:15,712:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,722:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,757:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,811:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:15,811:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:15,821:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,821:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,832:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,871:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:15,871:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:15,910:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,914:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,914:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,931:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:15,931:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:15,931:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,941:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:15,961:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,001:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:16,001:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:16,001:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,011:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,021:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,026:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:16,041:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:16,041:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,041:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,051:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,123:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:16,126:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:16,129:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,135:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,142:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,153:INFO:Calculating mean and std 2024-04-21 18:32:16,154:INFO:Creating metrics dataframe 2024-04-21 18:32:16,160:INFO:Uploading results into container 2024-04-21 18:32:16,161:INFO:Uploading model into container now 2024-04-21 18:32:16,162:INFO:_master_model_container: 10 2024-04-21 18:32:16,162:INFO:_display_container: 2 2024-04-21 18:32:16,163: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=6763, subsample=1.0, tol=0.0001, validation_fraction=0.1, verbose=0, warm_start=False) 2024-04-21 18:32:16,163:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:16,281:INFO:SubProcess create_model() end ================================== 2024-04-21 18:32:16,281:INFO:Creating metrics dataframe 2024-04-21 18:32:16,291:INFO:Initializing Linear Discriminant Analysis 2024-04-21 18:32:16,291:INFO:Total runtime is 0.31849975188573204 minutes 2024-04-21 18:32:16,291:INFO:SubProcess create_model() called ================================== 2024-04-21 18:32:16,294:INFO:Initializing create_model() 2024-04-21 18:32:16,294:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:32:16,294:INFO:Checking exceptions 2024-04-21 18:32:16,294:INFO:Importing libraries 2024-04-21 18:32:16,294:INFO:Copying training dataset 2024-04-21 18:32:16,306:INFO:Defining folds 2024-04-21 18:32:16,306:INFO:Declaring metric variables 2024-04-21 18:32:16,306:INFO:Importing untrained model 2024-04-21 18:32:16,306:INFO:Linear Discriminant Analysis Imported successfully 2024-04-21 18:32:16,306:INFO:Starting cross validation 2024-04-21 18:32:16,318:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:16,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:16,381:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:16,381:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:16,381:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:16,381:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:16,383:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,383:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:16,383:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,383:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,383:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,383:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,383:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,383:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,383:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:16,391:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,391:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:16,391:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,391:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,391:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,401:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,415:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:16,415:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:16,415:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:16,415:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,415:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:16,415:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:16,421:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:16,421:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,421:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:16,421:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,421:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,421:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:16,421:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,441:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:16,447:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:16,447:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,449:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,451:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:16,451:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,456:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,456:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,461:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,462:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,473:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,476:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:16,501:INFO:Calculating mean and std 2024-04-21 18:32:16,504:INFO:Creating metrics dataframe 2024-04-21 18:32:16,504:INFO:Uploading results into container 2024-04-21 18:32:16,510:INFO:Uploading model into container now 2024-04-21 18:32:16,511:INFO:_master_model_container: 11 2024-04-21 18:32:16,511:INFO:_display_container: 2 2024-04-21 18:32:16,511:INFO:LinearDiscriminantAnalysis(covariance_estimator=None, n_components=None, priors=None, shrinkage=None, solver='svd', store_covariance=False, tol=0.0001) 2024-04-21 18:32:16,511:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:16,590:INFO:SubProcess create_model() end ================================== 2024-04-21 18:32:16,590:INFO:Creating metrics dataframe 2024-04-21 18:32:16,591:INFO:Initializing Extra Trees Classifier 2024-04-21 18:32:16,591:INFO:Total runtime is 0.32349845965703333 minutes 2024-04-21 18:32:16,591:INFO:SubProcess create_model() called ================================== 2024-04-21 18:32:16,591:INFO:Initializing create_model() 2024-04-21 18:32:16,591:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:32:16,591:INFO:Checking exceptions 2024-04-21 18:32:16,591:INFO:Importing libraries 2024-04-21 18:32:16,591:INFO:Copying training dataset 2024-04-21 18:32:16,591:INFO:Defining folds 2024-04-21 18:32:16,591:INFO:Declaring metric variables 2024-04-21 18:32:16,591:INFO:Importing untrained model 2024-04-21 18:32:16,591:INFO:Extra Trees Classifier Imported successfully 2024-04-21 18:32:16,591:INFO:Starting cross validation 2024-04-21 18:32:16,591:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:17,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:17,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:17,371:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:17,379:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,379:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,379:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,379:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,381:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:17,382:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,382:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,382:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,382:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,382:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,382:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,382:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,382:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,427:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,427:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:17,431:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,436:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,439:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,443:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,443:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,445:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:17,447:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:17,447:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,447:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,451:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,451:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,451:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,451:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,461:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,463:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:17,463:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,463:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,471:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,507:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,507:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:17,511:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,513:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,521:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,527:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,531:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:17,532:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,538:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,545:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,555:INFO:Calculating mean and std 2024-04-21 18:32:17,557:INFO:Creating metrics dataframe 2024-04-21 18:32:17,563:INFO:Uploading results into container 2024-04-21 18:32:17,564:INFO:Uploading model into container now 2024-04-21 18:32:17,565:INFO:_master_model_container: 12 2024-04-21 18:32:17,565:INFO:_display_container: 2 2024-04-21 18:32:17,566: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=6763, verbose=0, warm_start=False) 2024-04-21 18:32:17,566:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:17,659:INFO:SubProcess create_model() end ================================== 2024-04-21 18:32:17,659:INFO:Creating metrics dataframe 2024-04-21 18:32:17,663:INFO:Initializing Dummy Classifier 2024-04-21 18:32:17,663:INFO:Total runtime is 0.34136580626169843 minutes 2024-04-21 18:32:17,663:INFO:SubProcess create_model() called ================================== 2024-04-21 18:32:17,663:INFO:Initializing create_model() 2024-04-21 18:32:17,663:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:32:17,663:INFO:Checking exceptions 2024-04-21 18:32:17,663:INFO:Importing libraries 2024-04-21 18:32:17,663:INFO:Copying training dataset 2024-04-21 18:32:17,668:INFO:Defining folds 2024-04-21 18:32:17,668:INFO:Declaring metric variables 2024-04-21 18:32:17,668:INFO:Importing untrained model 2024-04-21 18:32:17,668:INFO:Dummy Classifier Imported successfully 2024-04-21 18:32:17,668:INFO:Starting cross validation 2024-04-21 18:32:17,668:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:17,691:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,691:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:17,691:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,691:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,691:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,691:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:17,691:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:17,701:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,701:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,701:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,701:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:17,701:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,701:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,701:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( ric.capitalize()} is", len(result)) 2024-04-21 18:32:17,701:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:17,701:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,701:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,711:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,711:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:17,711:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,716:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,716:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:17,721:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,726:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,726:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,726:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:17,726:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:17,732:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,732:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,732:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,732:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:17,732:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,732:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,732:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,732:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:17,732:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:17,732:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,732:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,732:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,741:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:17,741:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,741:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,748:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:17,751:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,754:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,754:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:17,754:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,754:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,761:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:17,761:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:17,761:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:17,761:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,765:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,771:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,771:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,771:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:17,771:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:32:17,771:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,771:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:17,786:INFO:Calculating mean and std 2024-04-21 18:32:17,786:INFO:Creating metrics dataframe 2024-04-21 18:32:17,791:INFO:Uploading results into container 2024-04-21 18:32:17,791:INFO:Uploading model into container now 2024-04-21 18:32:17,791:INFO:_master_model_container: 13 2024-04-21 18:32:17,791:INFO:_display_container: 2 2024-04-21 18:32:17,791:INFO:DummyClassifier(constant=None, random_state=6763, strategy='prior') 2024-04-21 18:32:17,791:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:17,888:INFO:SubProcess create_model() end ================================== 2024-04-21 18:32:17,888:INFO:Creating metrics dataframe 2024-04-21 18:32:17,899:INFO:Initializing create_model() 2024-04-21 18:32:17,899:INFO:create_model(self=, estimator=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=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), 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={}) 2024-04-21 18:32:17,899:INFO:Checking exceptions 2024-04-21 18:32:17,900:INFO:Importing libraries 2024-04-21 18:32:17,900:INFO:Copying training dataset 2024-04-21 18:32:17,901:INFO:Defining folds 2024-04-21 18:32:17,901:INFO:Declaring metric variables 2024-04-21 18:32:17,904:INFO:Importing untrained model 2024-04-21 18:32:17,904:INFO:Declaring custom model 2024-04-21 18:32:17,904:INFO:Logistic Regression Imported successfully 2024-04-21 18:32:17,905:INFO:Cross validation set to False 2024-04-21 18:32:17,905:INFO:Fitting Model 2024-04-21 18:32:18,046: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=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 18:32:18,047:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:18,186:INFO:_master_model_container: 13 2024-04-21 18:32:18,186:INFO:_display_container: 2 2024-04-21 18:32:18,186: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=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 18:32:18,186:INFO:compare_models() successfully completed...................................... 2024-04-21 18:32:25,167:INFO:Initializing create_model() 2024-04-21 18:32:25,167:INFO:create_model(self=, estimator=lr, 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={}) 2024-04-21 18:32:25,167:INFO:Checking exceptions 2024-04-21 18:32:25,168:INFO:Importing libraries 2024-04-21 18:32:25,168:INFO:Copying training dataset 2024-04-21 18:32:25,170:INFO:Defining folds 2024-04-21 18:32:25,170:INFO:Declaring metric variables 2024-04-21 18:32:25,171:INFO:Importing untrained model 2024-04-21 18:32:25,171:INFO:Logistic Regression Imported successfully 2024-04-21 18:32:25,171:INFO:Starting cross validation 2024-04-21 18:32:25,171:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:25,248:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:25,250:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:25,252:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:25,253:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,254:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:25,257:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,257:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,259:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,261:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:25,262:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:25,262:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,262:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:25,262:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:25,262:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:25,262:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,262:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,262:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:25,262:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,262:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,262:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,272:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,272:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,272:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:25,273:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:25,274:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,274:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:25,275:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:25,275:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,275:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:25,276:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:25,277:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,278:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,278:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:25,279:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,279:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:25,280:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,280:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:25,280:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,280:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:25,282:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,282:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,282:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,283:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,283:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,283:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,283:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,283:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,283:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,283:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:25,303:INFO:Calculating mean and std 2024-04-21 18:32:25,303:INFO:Creating metrics dataframe 2024-04-21 18:32:25,305:INFO:Finalizing model 2024-04-21 18:32:25,353:INFO:Uploading results into container 2024-04-21 18:32:25,353:INFO:Uploading model into container now 2024-04-21 18:32:25,363:INFO:_master_model_container: 14 2024-04-21 18:32:25,363:INFO:_display_container: 3 2024-04-21 18:32:25,363: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=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 18:32:25,363:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:25,447:INFO:Initializing tune_model() 2024-04-21 18:32:25,447:INFO:tune_model(self=, estimator=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=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), 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={}) 2024-04-21 18:32:25,447:INFO:Checking exceptions 2024-04-21 18:32:25,450:INFO:Copying training dataset 2024-04-21 18:32:25,451:INFO:Checking base model 2024-04-21 18:32:25,451:INFO:Base model : Logistic Regression 2024-04-21 18:32:25,451:INFO:Declaring metric variables 2024-04-21 18:32:25,452:INFO:Defining Hyperparameters 2024-04-21 18:32:25,542:INFO:Tuning with n_jobs=-1 2024-04-21 18:32:25,543:INFO:Initializing RandomizedSearchCV 2024-04-21 18:32:27,034:INFO:best_params: {'actual_estimator__class_weight': {}, 'actual_estimator__C': 4.977} 2024-04-21 18:32:27,035:INFO:Hyperparameter search completed 2024-04-21 18:32:27,035:INFO:SubProcess create_model() called ================================== 2024-04-21 18:32:27,036:INFO:Initializing create_model() 2024-04-21 18:32:27,036:INFO:create_model(self=, estimator=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=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'class_weight': {}, 'C': 4.977}) 2024-04-21 18:32:27,036:INFO:Checking exceptions 2024-04-21 18:32:27,036:INFO:Importing libraries 2024-04-21 18:32:27,036:INFO:Copying training dataset 2024-04-21 18:32:27,041:INFO:Defining folds 2024-04-21 18:32:27,042:INFO:Declaring metric variables 2024-04-21 18:32:27,042:INFO:Importing untrained model 2024-04-21 18:32:27,042:INFO:Declaring custom model 2024-04-21 18:32:27,043:INFO:Logistic Regression Imported successfully 2024-04-21 18:32:27,043:INFO:Starting cross validation 2024-04-21 18:32:27,044:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:27,160:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,162:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,163:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,163:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:27,163:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:27,165:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,165:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:27,165:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,168:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,168:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,173:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,173:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,173:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,175:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:27,175:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,175:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,178:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,178:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,180:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:27,181:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,182:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,183:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:27,186:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,186:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,187:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,188:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,188:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,189:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:27,190:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:27,191:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,191:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,193:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,195:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,195:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,196:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,196:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,198:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,198:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:27,199:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,200:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,204:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,205:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,206:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:27,207:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,207:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,210:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,214:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,220:INFO:Calculating mean and std 2024-04-21 18:32:27,221:INFO:Creating metrics dataframe 2024-04-21 18:32:27,223:INFO:Finalizing model 2024-04-21 18:32:27,282:INFO:Uploading results into container 2024-04-21 18:32:27,283:INFO:Uploading model into container now 2024-04-21 18:32:27,283:INFO:_master_model_container: 15 2024-04-21 18:32:27,283:INFO:_display_container: 4 2024-04-21 18:32:27,284:INFO:LogisticRegression(C=4.977, class_weight={}, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=None, max_iter=1000, multi_class='auto', n_jobs=None, penalty='l2', random_state=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 18:32:27,284:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:27,367:INFO:SubProcess create_model() end ================================== 2024-04-21 18:32:27,367:INFO:choose_better activated 2024-04-21 18:32:27,367:INFO:SubProcess create_model() called ================================== 2024-04-21 18:32:27,367:INFO:Initializing create_model() 2024-04-21 18:32:27,367:INFO:create_model(self=, estimator=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=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), 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={}) 2024-04-21 18:32:27,367:INFO:Checking exceptions 2024-04-21 18:32:27,369:INFO:Importing libraries 2024-04-21 18:32:27,369:INFO:Copying training dataset 2024-04-21 18:32:27,371:INFO:Defining folds 2024-04-21 18:32:27,371:INFO:Declaring metric variables 2024-04-21 18:32:27,371:INFO:Importing untrained model 2024-04-21 18:32:27,371:INFO:Declaring custom model 2024-04-21 18:32:27,372:INFO:Logistic Regression Imported successfully 2024-04-21 18:32:27,372:INFO:Starting cross validation 2024-04-21 18:32:27,372:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:32:27,468:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,468:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,468:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:27,471:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,471:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:27,473:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,473:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:27,474:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,474:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,474:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,474:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,478:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:27,478:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,481:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,481:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,481:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,481:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,484:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,485:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,485:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:27,485:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:27,485:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,485:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,488:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,488:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,488:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,490:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:27,491:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,493:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,493:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,494:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,494:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,495:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:27,495:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,495:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,495:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,501:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,501:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,505:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,508:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,508:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:32:27,510:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:32:27,510:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:32:27,511:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,512:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,515:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,517:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,519:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,520:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:32:27,526:INFO:Calculating mean and std 2024-04-21 18:32:27,526:INFO:Creating metrics dataframe 2024-04-21 18:32:27,528:INFO:Finalizing model 2024-04-21 18:32:27,580:INFO:Uploading results into container 2024-04-21 18:32:27,581:INFO:Uploading model into container now 2024-04-21 18:32:27,581:INFO:_master_model_container: 16 2024-04-21 18:32:27,581:INFO:_display_container: 5 2024-04-21 18:32:27,581: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=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 18:32:27,581:INFO:create_model() successfully completed...................................... 2024-04-21 18:32:27,651:INFO:SubProcess create_model() end ================================== 2024-04-21 18:32:27,651: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=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) result for Accuracy is 1.0 2024-04-21 18:32:27,651:INFO:LogisticRegression(C=4.977, class_weight={}, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=None, max_iter=1000, multi_class='auto', n_jobs=None, penalty='l2', random_state=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) result for Accuracy is 1.0 2024-04-21 18:32:27,651: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=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) is best model 2024-04-21 18:32:27,651:INFO:choose_better completed 2024-04-21 18:32:27,651: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 18:32:27,665:INFO:_master_model_container: 16 2024-04-21 18:32:27,665:INFO:_display_container: 4 2024-04-21 18:32:27,666: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=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 18:32:27,666:INFO:tune_model() successfully completed...................................... 2024-04-21 18:32:27,751:INFO:Initializing evaluate_model() 2024-04-21 18:32:27,751:INFO:evaluate_model(self=, estimator=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=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) 2024-04-21 18:32:28,202:INFO:Initializing plot_model() 2024-04-21 18:32:28,202:INFO:plot_model(self=, estimator=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=6763, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), plot=pipeline, scale=1, save=False, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) 2024-04-21 18:32:28,202:INFO:Checking exceptions 2024-04-21 18:32:28,204:INFO:Preloading libraries 2024-04-21 18:32:28,204:INFO:Copying training dataset 2024-04-21 18:32:28,204:INFO:Plot type: pipeline 2024-04-21 18:36:22,184:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:36:22,184:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:36:22,184:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:36:22,184:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 18:36:57,843:INFO:PyCaret ClassificationExperiment 2024-04-21 18:36:57,843:INFO:Logging name: clf-default-name 2024-04-21 18:36:57,843:INFO:ML Usecase: MLUsecase.CLASSIFICATION 2024-04-21 18:36:57,843:INFO:version 3.3.0 2024-04-21 18:36:57,843:INFO:Initializing setup() 2024-04-21 18:36:57,843:INFO:self.USI: 2abe 2024-04-21 18:36:57,843:INFO:self._variable_keys: {'fold_groups_param', 'fold_shuffle_param', 'fold_generator', 'y', 'target_param', 'is_multiclass', 'data', 'n_jobs_param', 'y_train', 'html_param', '_ml_usecase', 'idx', 'logging_param', 'pipeline', 'memory', 'exp_name_log', 'exp_id', 'y_test', 'USI', 'X_train', 'X_test', 'gpu_n_jobs_param', 'seed', 'gpu_param', '_available_plots', 'fix_imbalance', 'X', 'log_plots_param'} 2024-04-21 18:36:57,844:INFO:Checking environment 2024-04-21 18:36:57,844:INFO:python_version: 3.11.5 2024-04-21 18:36:57,844:INFO:python_build: ('main', 'Sep 11 2023 13:26:23') 2024-04-21 18:36:57,844:INFO:machine: AMD64 2024-04-21 18:36:57,870:INFO:platform: Windows-10-10.0.22631-SP0 2024-04-21 18:36:57,876:INFO:Memory: svmem(total=16782184448, available=878698496, percent=94.8, used=15903485952, free=878698496) 2024-04-21 18:36:57,876:INFO:Physical Core: 10 2024-04-21 18:36:57,876:INFO:Logical Core: 16 2024-04-21 18:36:57,876:INFO:Checking libraries 2024-04-21 18:36:57,876:INFO:System: 2024-04-21 18:36:57,876:INFO: python: 3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:26:23) [MSC v.1916 64 bit (AMD64)] 2024-04-21 18:36:57,876:INFO:executable: C:\Users\arpit\anaconda3\envs\arpit-test\python.exe 2024-04-21 18:36:57,876:INFO: machine: Windows-10-10.0.22631-SP0 2024-04-21 18:36:57,876:INFO:PyCaret required dependencies: 2024-04-21 18:36:58,581:INFO: pip: 23.3 2024-04-21 18:36:58,582:INFO: setuptools: 68.0.0 2024-04-21 18:36:58,582:INFO: pycaret: 3.3.0 2024-04-21 18:36:58,582:INFO: IPython: 8.16.1 2024-04-21 18:36:58,582:INFO: ipywidgets: 8.1.2 2024-04-21 18:36:58,582:INFO: tqdm: 4.66.1 2024-04-21 18:36:58,582:INFO: numpy: 1.23.5 2024-04-21 18:36:58,582:INFO: pandas: 1.5.3 2024-04-21 18:36:58,582:INFO: jinja2: 3.1.2 2024-04-21 18:36:58,582:INFO: scipy: 1.11.4 2024-04-21 18:36:58,582:INFO: joblib: 1.3.2 2024-04-21 18:36:58,582:INFO: sklearn: 1.4.1.post1 2024-04-21 18:36:58,582:INFO: pyod: 1.1.3 2024-04-21 18:36:58,582:INFO: imblearn: 0.12.0 2024-04-21 18:36:58,582:INFO: category_encoders: 2.6.3 2024-04-21 18:36:58,582:INFO: lightgbm: 4.3.0 2024-04-21 18:36:58,582:INFO: numba: 0.58.1 2024-04-21 18:36:58,582:INFO: requests: 2.31.0 2024-04-21 18:36:58,582:INFO: matplotlib: 3.7.5 2024-04-21 18:36:58,582:INFO: scikitplot: 0.3.7 2024-04-21 18:36:58,582:INFO: yellowbrick: 1.5 2024-04-21 18:36:58,582:INFO: plotly: 5.18.0 2024-04-21 18:36:58,582:INFO: plotly-resampler: Not installed 2024-04-21 18:36:58,582:INFO: kaleido: 0.2.1 2024-04-21 18:36:58,582:INFO: schemdraw: 0.15 2024-04-21 18:36:58,582:INFO: statsmodels: 0.14.1 2024-04-21 18:36:58,582:INFO: sktime: 0.26.0 2024-04-21 18:36:58,582:INFO: tbats: 1.1.3 2024-04-21 18:36:58,582:INFO: pmdarima: 2.0.4 2024-04-21 18:36:58,582:INFO: psutil: 5.9.6 2024-04-21 18:36:58,582:INFO: markupsafe: 2.1.3 2024-04-21 18:36:58,582:INFO: pickle5: Not installed 2024-04-21 18:36:58,582:INFO: cloudpickle: 3.0.0 2024-04-21 18:36:58,582:INFO: deprecation: 2.1.0 2024-04-21 18:36:58,582:INFO: xxhash: 3.4.1 2024-04-21 18:36:58,582:INFO: wurlitzer: Not installed 2024-04-21 18:36:58,582:INFO:PyCaret optional dependencies: 2024-04-21 18:36:58,590:INFO: shap: Not installed 2024-04-21 18:36:58,590:INFO: interpret: Not installed 2024-04-21 18:36:58,590:INFO: umap: 0.5.5 2024-04-21 18:36:58,590:INFO: ydata_profiling: 4.6.5 2024-04-21 18:36:58,590:INFO: explainerdashboard: Not installed 2024-04-21 18:36:58,590:INFO: autoviz: Not installed 2024-04-21 18:36:58,590:INFO: fairlearn: Not installed 2024-04-21 18:36:58,591:INFO: deepchecks: Not installed 2024-04-21 18:36:58,591:INFO: xgboost: Not installed 2024-04-21 18:36:58,591:INFO: catboost: Not installed 2024-04-21 18:36:58,591:INFO: kmodes: Not installed 2024-04-21 18:36:58,591:INFO: mlxtend: Not installed 2024-04-21 18:36:58,591:INFO: statsforecast: Not installed 2024-04-21 18:36:58,591:INFO: tune_sklearn: Not installed 2024-04-21 18:36:58,591:INFO: ray: Not installed 2024-04-21 18:36:58,591:INFO: hyperopt: Not installed 2024-04-21 18:36:58,591:INFO: optuna: Not installed 2024-04-21 18:36:58,591:INFO: skopt: Not installed 2024-04-21 18:36:58,591:INFO: mlflow: Not installed 2024-04-21 18:36:58,591:INFO: gradio: Not installed 2024-04-21 18:36:58,591:INFO: fastapi: 0.110.1 2024-04-21 18:36:58,591:INFO: uvicorn: 0.29.0 2024-04-21 18:36:58,591:INFO: m2cgen: Not installed 2024-04-21 18:36:58,591:INFO: evidently: Not installed 2024-04-21 18:36:58,591:INFO: fugue: Not installed 2024-04-21 18:36:58,591:INFO: streamlit: 1.29.0 2024-04-21 18:36:58,591:INFO: prophet: Not installed 2024-04-21 18:36:58,591:INFO:None 2024-04-21 18:36:58,591:INFO:Set up data. 2024-04-21 18:36:59,021:INFO:Set up folding strategy. 2024-04-21 18:36:59,021:INFO:Set up train/test split. 2024-04-21 18:36:59,597:INFO:Set up index. 2024-04-21 18:36:59,606:INFO:Assigning column types. 2024-04-21 18:36:59,609:INFO:Engine successfully changes for model 'lr' to 'sklearn'. 2024-04-21 18:36:59,650:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. 2024-04-21 18:36:59,717:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 18:37:00,665:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:00,666:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:00,698:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. 2024-04-21 18:37:00,698:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 18:37:00,718:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:00,718:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:00,718:INFO:Engine successfully changes for model 'knn' to 'sklearn'. 2024-04-21 18:37:00,753:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 18:37:00,774:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:00,775:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:00,812:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 18:37:00,830:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:00,830:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:00,831:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'. 2024-04-21 18:37:00,880:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:00,880:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:00,933:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:00,934:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:01,122:INFO:Preparing preprocessing pipeline... 2024-04-21 18:37:01,226:INFO:Set up label encoding. 2024-04-21 18:37:01,226:INFO:Set up simple imputation. 2024-04-21 18:37:01,305:INFO:Finished creating preprocessing pipeline. 2024-04-21 18:37:01,395:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\arpit\AppData\Local\Temp\joblib), steps=[('label_encoding', TransformerWrapperWithInverse(exclude=None, include=None, transformer=LabelEncoder())), ('numerical_imputer', TransformerWrapper(exclude=None, include=['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'], transformer=SimpleImputer(add_indicator=False, copy=True, fill_value=None, keep_empty_features=False, missing_values=nan, strategy='mean'))), ('categorical_imputer', TransformerWrapper(exclude=None, include=[], transformer=SimpleImputer(add_indicator=False, copy=True, fill_value=None, keep_empty_features=False, missing_values=nan, strategy='most_frequent')))], verbose=False) 2024-04-21 18:37:01,395:INFO:Creating final display dataframe. 2024-04-21 18:37:01,605:INFO:Setup _display_container: Description Value 0 Session id 1482 1 Target Species 2 Target type Multiclass 3 Target mapping Iris-setosa: 0, Iris-versicolor: 1, Iris-virgi... 4 Original data shape (150, 6) 5 Transformed data shape (150, 6) 6 Transformed train set shape (105, 6) 7 Transformed test set shape (45, 6) 8 Numeric features 5 9 Preprocess True 10 Imputation type simple 11 Numeric imputation mean 12 Categorical imputation mode 13 Fold Generator StratifiedKFold 14 Fold Number 10 15 CPU Jobs -1 16 Use GPU False 17 Log Experiment False 18 Experiment Name clf-default-name 19 USI 2abe 2024-04-21 18:37:01,678:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:01,678:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:01,727:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:01,728:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 18:37:02,020:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\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 18:37:02,020:INFO:setup() successfully completed in 4.59s............... 2024-04-21 18:37:02,020:INFO:Initializing get_config() 2024-04-21 18:37:02,020:INFO:get_config(self=, variable=X_train) 2024-04-21 18:37:02,021:INFO:Variable: 'X_train' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_train_transformed' instead. 2024-04-21 18:37:02,289:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pycaret_experiment\pycaret_experiment.py:321: UserWarning: Variable: 'X_train' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_train_transformed' instead. warnings.warn(msg) # print on screen 2024-04-21 18:37:02,423:INFO:Variable: returned as Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm 147 148 6.5 3.0 5.2 2.0 121 122 5.6 2.8 4.9 2.0 111 112 6.4 2.7 5.3 1.9 70 71 5.9 3.2 4.8 1.8 114 115 5.8 2.8 5.1 2.4 .. ... ... ... ... ... 1 2 4.9 3.0 1.4 0.2 18 19 5.7 3.8 1.7 0.3 58 59 6.6 2.9 4.6 1.3 15 16 5.7 4.4 1.5 0.4 113 114 5.7 2.5 5.0 2.0 [105 rows x 5 columns] 2024-04-21 18:37:02,423:INFO:get_config() successfully completed...................................... 2024-04-21 18:37:02,423:INFO:Initializing get_config() 2024-04-21 18:37:02,423:INFO:get_config(self=, variable=X_test) 2024-04-21 18:37:02,424:INFO:Variable: 'X_test' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_test_transformed' instead. 2024-04-21 18:37:02,424:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pycaret_experiment\pycaret_experiment.py:321: UserWarning: Variable: 'X_test' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_test_transformed' instead. warnings.warn(msg) # print on screen 2024-04-21 18:37:02,441:INFO:Variable: returned as Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm 81 82 5.5 2.4 3.7 1.0 56 57 6.3 3.3 4.7 1.6 41 42 4.5 2.3 1.3 0.3 50 51 7.0 3.2 4.7 1.4 31 32 5.4 3.4 1.5 0.4 88 89 5.6 3.0 4.1 1.3 140 141 6.7 3.1 5.6 2.4 143 144 6.8 3.2 5.9 2.3 66 67 5.6 3.0 4.5 1.5 13 14 4.3 3.0 1.1 0.1 100 101 6.3 3.3 6.0 2.5 109 110 7.2 3.6 6.1 2.5 54 55 6.5 2.8 4.6 1.5 25 26 5.0 3.0 1.6 0.2 124 125 6.7 3.3 5.7 2.1 106 107 4.9 2.5 4.5 1.7 29 30 4.7 3.2 1.6 0.2 72 73 6.3 2.5 4.9 1.5 32 33 5.2 4.1 1.5 0.1 130 131 7.4 2.8 6.1 1.9 5 6 5.4 3.9 1.7 0.4 91 92 6.1 3.0 4.6 1.4 69 70 5.6 2.5 3.9 1.1 0 1 5.1 3.5 1.4 0.2 99 100 5.7 2.8 4.1 1.3 37 38 4.9 3.1 1.5 0.1 103 104 6.3 2.9 5.6 1.8 7 8 5.0 3.4 1.5 0.2 118 119 7.7 2.6 6.9 2.3 138 139 6.0 3.0 4.8 1.8 59 60 5.2 2.7 3.9 1.4 97 98 6.2 2.9 4.3 1.3 145 146 6.7 3.0 5.2 2.3 71 72 6.1 2.8 4.0 1.3 92 93 5.8 2.6 4.0 1.2 12 13 4.8 3.0 1.4 0.1 137 138 6.4 3.1 5.5 1.8 2 3 4.7 3.2 1.3 0.2 16 17 5.4 3.9 1.3 0.4 131 132 7.9 3.8 6.4 2.0 142 143 5.8 2.7 5.1 1.9 134 135 6.1 2.6 5.6 1.4 44 45 5.1 3.8 1.9 0.4 57 58 4.9 2.4 3.3 1.0 17 18 5.1 3.5 1.4 0.3 2024-04-21 18:37:02,442:INFO:get_config() successfully completed...................................... 2024-04-21 18:37:02,442:INFO:Initializing compare_models() 2024-04-21 18:37:02,442:INFO:compare_models(self=, include=None, exclude=['lightgbm', 'catboost', 'xgboost'], 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': , 'include': None, 'exclude': ['lightgbm', 'catboost', 'xgboost'], '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__': }) 2024-04-21 18:37:02,442:INFO:Checking exceptions 2024-04-21 18:37:02,445:INFO:Preparing display monitor 2024-04-21 18:37:02,453:INFO:Initializing Logistic Regression 2024-04-21 18:37:02,453:INFO:Total runtime is 1.6677379608154298e-05 minutes 2024-04-21 18:37:02,453:INFO:SubProcess create_model() called ================================== 2024-04-21 18:37:02,453:INFO:Initializing create_model() 2024-04-21 18:37:02,453:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:37:02,453:INFO:Checking exceptions 2024-04-21 18:37:02,453:INFO:Importing libraries 2024-04-21 18:37:02,453:INFO:Copying training dataset 2024-04-21 18:37:02,458:INFO:Defining folds 2024-04-21 18:37:02,458:INFO:Declaring metric variables 2024-04-21 18:37:02,458:INFO:Importing untrained model 2024-04-21 18:37:02,458:INFO:Logistic Regression Imported successfully 2024-04-21 18:37:02,458:INFO:Starting cross validation 2024-04-21 18:37:02,459:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:37:52,027:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:37:52,040:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:37:52,043:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:37:52,048:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:37:52,053:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:37:52,054:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,058:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:37:52,065:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,066:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:37:52,066:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:37:52,067:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:37:52,070:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,071:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,071:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,074:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:37:52,075:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:37:52,075:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:37:52,076:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:37:52,076:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,076:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,079:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,082:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:37:52,082:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:37:52,082:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:37:52,083:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,084:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,088:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:37:52,088:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:37:52,090:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,091:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,092:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,094:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:37:52,094:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,094:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,095:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,096:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,099:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,099:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,100:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,100:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,102:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,104:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,105:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,106:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:37:52,108:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,113:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,114:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,119:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,125:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,143:INFO:Calculating mean and std 2024-04-21 18:37:52,145:INFO:Creating metrics dataframe 2024-04-21 18:37:52,153:INFO:Uploading results into container 2024-04-21 18:37:52,154:INFO:Uploading model into container now 2024-04-21 18:37:52,155:INFO:_master_model_container: 1 2024-04-21 18:37:52,156:INFO:_display_container: 2 2024-04-21 18:37:52,156: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=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 18:37:52,156:INFO:create_model() successfully completed...................................... 2024-04-21 18:37:52,273:INFO:SubProcess create_model() end ================================== 2024-04-21 18:37:52,274:INFO:Creating metrics dataframe 2024-04-21 18:37:52,420:INFO:Initializing K Neighbors Classifier 2024-04-21 18:37:52,420:INFO:Total runtime is 0.8328000624974569 minutes 2024-04-21 18:37:52,420:INFO:SubProcess create_model() called ================================== 2024-04-21 18:37:52,420:INFO:Initializing create_model() 2024-04-21 18:37:52,420:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:37:52,420:INFO:Checking exceptions 2024-04-21 18:37:52,420:INFO:Importing libraries 2024-04-21 18:37:52,421:INFO:Copying training dataset 2024-04-21 18:37:52,424:INFO:Defining folds 2024-04-21 18:37:52,424:INFO:Declaring metric variables 2024-04-21 18:37:52,424:INFO:Importing untrained model 2024-04-21 18:37:52,424:INFO:K Neighbors Classifier Imported successfully 2024-04-21 18:37:52,424:INFO:Starting cross validation 2024-04-21 18:37:52,425:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:37:52,500:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:37:52,500:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:37:52,502:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:37:52,502:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:37:52,503:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:37:52,503:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:37:52,504:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,504:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:37:52,505:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:37:52,506:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,508:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,508:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,508:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,509:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,512:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,512:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,513:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,514:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,518:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:37:52,518:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:33,531:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:33,531:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:33,532:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:33,533:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:33,533:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:33,534:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:35,708:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:35,708:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:35,708:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:35,708:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:35,708:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:35,708:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:36,036:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,036:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,037:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,037:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,037:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,039:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,040:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,040:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,040:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,040:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,041:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,042:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,042:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,051:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,053:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,037:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,064:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,069:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,078:INFO:Calculating mean and std 2024-04-21 18:38:36,080:INFO:Creating metrics dataframe 2024-04-21 18:38:36,083:INFO:Uploading results into container 2024-04-21 18:38:36,084:INFO:Uploading model into container now 2024-04-21 18:38:36,084:INFO:_master_model_container: 2 2024-04-21 18:38:36,084:INFO:_display_container: 2 2024-04-21 18:38:36,084: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 18:38:36,084:INFO:create_model() successfully completed...................................... 2024-04-21 18:38:36,164:INFO:SubProcess create_model() end ================================== 2024-04-21 18:38:36,164:INFO:Creating metrics dataframe 2024-04-21 18:38:36,287:INFO:Initializing Naive Bayes 2024-04-21 18:38:36,287:INFO:Total runtime is 1.5639256119728089 minutes 2024-04-21 18:38:36,287:INFO:SubProcess create_model() called ================================== 2024-04-21 18:38:36,288:INFO:Initializing create_model() 2024-04-21 18:38:36,288:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:38:36,288:INFO:Checking exceptions 2024-04-21 18:38:36,288:INFO:Importing libraries 2024-04-21 18:38:36,288:INFO:Copying training dataset 2024-04-21 18:38:36,290:INFO:Defining folds 2024-04-21 18:38:36,290:INFO:Declaring metric variables 2024-04-21 18:38:36,290:INFO:Importing untrained model 2024-04-21 18:38:36,290:INFO:Naive Bayes Imported successfully 2024-04-21 18:38:36,290:INFO:Starting cross validation 2024-04-21 18:38:36,291:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:38:36,594:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:36,594:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:36,594:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:36,595:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:36,596:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:36,596:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:36,596:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:36,596:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:36,596:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:36,597:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:36,597:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:36,597:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:36,597:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,598:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,598:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:36,598:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:36,598:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,598:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:36,598:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:36,598:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:36,599:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,599:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:36,600:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,600:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,601:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,601:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,601:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,601:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,601:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,602:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,602:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,602:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,602:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,602:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,603:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,603:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,603:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,604:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,604:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,605:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,605:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,605:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,605:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,606:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,607:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,608:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:36,622:INFO:Calculating mean and std 2024-04-21 18:38:36,623:INFO:Creating metrics dataframe 2024-04-21 18:38:36,625:INFO:Uploading results into container 2024-04-21 18:38:36,625:INFO:Uploading model into container now 2024-04-21 18:38:36,625:INFO:_master_model_container: 3 2024-04-21 18:38:36,625:INFO:_display_container: 2 2024-04-21 18:38:36,625:INFO:GaussianNB(priors=None, var_smoothing=1e-09) 2024-04-21 18:38:36,625:INFO:create_model() successfully completed...................................... 2024-04-21 18:38:36,683:INFO:SubProcess create_model() end ================================== 2024-04-21 18:38:36,684:INFO:Creating metrics dataframe 2024-04-21 18:38:36,686:INFO:Initializing Decision Tree Classifier 2024-04-21 18:38:36,686:INFO:Total runtime is 1.5705694874127707 minutes 2024-04-21 18:38:36,686:INFO:SubProcess create_model() called ================================== 2024-04-21 18:38:36,687:INFO:Initializing create_model() 2024-04-21 18:38:36,687:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:38:36,687:INFO:Checking exceptions 2024-04-21 18:38:36,687:INFO:Importing libraries 2024-04-21 18:38:36,687:INFO:Copying training dataset 2024-04-21 18:38:36,688:INFO:Defining folds 2024-04-21 18:38:36,688:INFO:Declaring metric variables 2024-04-21 18:38:36,688:INFO:Importing untrained model 2024-04-21 18:38:36,688:INFO:Decision Tree Classifier Imported successfully 2024-04-21 18:38:36,688:INFO:Starting cross validation 2024-04-21 18:38:36,689:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:38:38,987:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:38,988:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:38,988:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:38,988:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:38,988:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:38,988:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:38,988:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:38,988:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:38,988:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:38,988:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:38,989:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:38,989:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:38,989:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:38,989:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:38,989:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:38,989:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:38,989:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,991:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,991:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,991:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,991:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:38,991:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,991:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,991:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,992:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,992:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,993:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,993:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,993:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,994:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,994:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,994:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,994:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,994:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,994:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,994:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,995:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,995:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,996:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,996:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,996:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,996:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,996:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:38,996:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:39,008:INFO:Calculating mean and std 2024-04-21 18:38:39,008:INFO:Creating metrics dataframe 2024-04-21 18:38:39,010:INFO:Uploading results into container 2024-04-21 18:38:39,010:INFO:Uploading model into container now 2024-04-21 18:38:39,010:INFO:_master_model_container: 4 2024-04-21 18:38:39,010:INFO:_display_container: 2 2024-04-21 18:38:39,011: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=1482, splitter='best') 2024-04-21 18:38:39,011:INFO:create_model() successfully completed...................................... 2024-04-21 18:38:39,066:INFO:SubProcess create_model() end ================================== 2024-04-21 18:38:39,066:INFO:Creating metrics dataframe 2024-04-21 18:38:39,069:INFO:Initializing SVM - Linear Kernel 2024-04-21 18:38:39,069:INFO:Total runtime is 1.6102850357691447 minutes 2024-04-21 18:38:39,069:INFO:SubProcess create_model() called ================================== 2024-04-21 18:38:39,069:INFO:Initializing create_model() 2024-04-21 18:38:39,069:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:38:39,069:INFO:Checking exceptions 2024-04-21 18:38:39,069:INFO:Importing libraries 2024-04-21 18:38:39,069:INFO:Copying training dataset 2024-04-21 18:38:39,070:INFO:Defining folds 2024-04-21 18:38:39,070:INFO:Declaring metric variables 2024-04-21 18:38:39,071:INFO:Importing untrained model 2024-04-21 18:38:39,071:INFO:SVM - Linear Kernel Imported successfully 2024-04-21 18:38:39,071:INFO:Starting cross validation 2024-04-21 18:38:39,071:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:38:41,322:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:41,322:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:41,322:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:41,322:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:41,322:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:41,322:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:41,322:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:41,322:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:41,322:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:41,323:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,323:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,323:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,323:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,323:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,323:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,323:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,323:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,323:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,323:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,324:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,324:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,324:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,324:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,324:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:41,326:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:41,326:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,326:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:41,326:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,326:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,326:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,326:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,326:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,327:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,327:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,327:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,327:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,327:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:41,327:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:41,328:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:41,328:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:41,328:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,329:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,329:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,329:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,329:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:41,343:INFO:Calculating mean and std 2024-04-21 18:38:41,344:INFO:Creating metrics dataframe 2024-04-21 18:38:41,347:INFO:Uploading results into container 2024-04-21 18:38:41,348:INFO:Uploading model into container now 2024-04-21 18:38:41,348:INFO:_master_model_container: 5 2024-04-21 18:38:41,348:INFO:_display_container: 2 2024-04-21 18:38:41,348: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=1482, shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0, warm_start=False) 2024-04-21 18:38:41,348:INFO:create_model() successfully completed...................................... 2024-04-21 18:38:41,423:INFO:SubProcess create_model() end ================================== 2024-04-21 18:38:41,424:INFO:Creating metrics dataframe 2024-04-21 18:38:41,428:INFO:Initializing Ridge Classifier 2024-04-21 18:38:41,428:INFO:Total runtime is 1.6496010621388753 minutes 2024-04-21 18:38:41,428:INFO:SubProcess create_model() called ================================== 2024-04-21 18:38:41,428:INFO:Initializing create_model() 2024-04-21 18:38:41,428:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:38:41,428:INFO:Checking exceptions 2024-04-21 18:38:41,428:INFO:Importing libraries 2024-04-21 18:38:41,428:INFO:Copying training dataset 2024-04-21 18:38:41,429:INFO:Defining folds 2024-04-21 18:38:41,429:INFO:Declaring metric variables 2024-04-21 18:38:41,429:INFO:Importing untrained model 2024-04-21 18:38:41,429:INFO:Ridge Classifier Imported successfully 2024-04-21 18:38:41,429:INFO:Starting cross validation 2024-04-21 18:38:41,430:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:38:46,124:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:46,125:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,126:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:46,127:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,128:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,129:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:46,130:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,131:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,131:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:46,133:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:46,133:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:46,133:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:46,134:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,134:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:46,134:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,134:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:46,135:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,135:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 18:38:46,135:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,136:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,136:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,136:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,137:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,138:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,138:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,140:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,141:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,141:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,141:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,141:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,142:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,143:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,143:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,143:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,143:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,144:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,145:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,145:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,147:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,161:INFO:Calculating mean and std 2024-04-21 18:38:46,162:INFO:Creating metrics dataframe 2024-04-21 18:38:46,165:INFO:Uploading results into container 2024-04-21 18:38:46,165:INFO:Uploading model into container now 2024-04-21 18:38:46,166:INFO:_master_model_container: 6 2024-04-21 18:38:46,166:INFO:_display_container: 2 2024-04-21 18:38:46,166:INFO:RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True, max_iter=None, positive=False, random_state=1482, solver='auto', tol=0.0001) 2024-04-21 18:38:46,166:INFO:create_model() successfully completed...................................... 2024-04-21 18:38:46,232:INFO:SubProcess create_model() end ================================== 2024-04-21 18:38:46,232:INFO:Creating metrics dataframe 2024-04-21 18:38:46,235:INFO:Initializing Random Forest Classifier 2024-04-21 18:38:46,236:INFO:Total runtime is 1.7297475973765055 minutes 2024-04-21 18:38:46,236:INFO:SubProcess create_model() called ================================== 2024-04-21 18:38:46,236:INFO:Initializing create_model() 2024-04-21 18:38:46,236:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:38:46,236:INFO:Checking exceptions 2024-04-21 18:38:46,236:INFO:Importing libraries 2024-04-21 18:38:46,236:INFO:Copying training dataset 2024-04-21 18:38:46,239:INFO:Defining folds 2024-04-21 18:38:46,239:INFO:Declaring metric variables 2024-04-21 18:38:46,241:INFO:Importing untrained model 2024-04-21 18:38:46,241:INFO:Random Forest Classifier Imported successfully 2024-04-21 18:38:46,241:INFO:Starting cross validation 2024-04-21 18:38:46,242:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:38:46,627:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:46,632:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:46,632:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:46,632:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:46,632:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:46,632:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:46,632:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:46,633:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,634:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,635:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,635:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,635:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,636:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,636:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,636:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,638:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,639:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,639:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,639:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,640:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,640:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,641:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:46,642:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:46,642:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,643:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,643:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,644:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:46,645:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:46,646:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,646:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,647:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:46,650:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,650:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:46,650:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:46,650:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,653:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,653:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:46,654:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,655:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,655:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,656:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,658:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,658:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,662:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:46,672:INFO:Calculating mean and std 2024-04-21 18:38:46,673:INFO:Creating metrics dataframe 2024-04-21 18:38:46,675:INFO:Uploading results into container 2024-04-21 18:38:46,676:INFO:Uploading model into container now 2024-04-21 18:38:46,676:INFO:_master_model_container: 7 2024-04-21 18:38:46,676:INFO:_display_container: 2 2024-04-21 18:38:46,676: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=1482, verbose=0, warm_start=False) 2024-04-21 18:38:46,676:INFO:create_model() successfully completed...................................... 2024-04-21 18:38:46,746:INFO:SubProcess create_model() end ================================== 2024-04-21 18:38:46,746:INFO:Creating metrics dataframe 2024-04-21 18:38:46,868:INFO:Initializing Quadratic Discriminant Analysis 2024-04-21 18:38:46,869:INFO:Total runtime is 1.740283445517222 minutes 2024-04-21 18:38:46,869:INFO:SubProcess create_model() called ================================== 2024-04-21 18:38:46,869:INFO:Initializing create_model() 2024-04-21 18:38:46,869:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:38:46,869:INFO:Checking exceptions 2024-04-21 18:38:46,869:INFO:Importing libraries 2024-04-21 18:38:46,869:INFO:Copying training dataset 2024-04-21 18:38:46,871:INFO:Defining folds 2024-04-21 18:38:46,871:INFO:Declaring metric variables 2024-04-21 18:38:46,871:INFO:Importing untrained model 2024-04-21 18:38:46,872:INFO:Quadratic Discriminant Analysis Imported successfully 2024-04-21 18:38:46,872:INFO:Starting cross validation 2024-04-21 18:38:46,872:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:38:52,122:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:52,122:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:52,123:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:52,124:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:52,124:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:52,124:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,125:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,126:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,127:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,127:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,128:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,129:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:52,130:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,131:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:52,131:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:52,132:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:52,132:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,133:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:52,133:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,133:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:52,134:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,134:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:52,134:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,134:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:52,135:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:52,135:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:52,135:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:52,136:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:52,136:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,137:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,138:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,138:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,138:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,140:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,140:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,141:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,141:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,142:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:52,144:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,144:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,144:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:52,146:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,149:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,150:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,151:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,152:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:52,153:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,157:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,159:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:52,166:INFO:Calculating mean and std 2024-04-21 18:38:52,167:INFO:Creating metrics dataframe 2024-04-21 18:38:52,169:INFO:Uploading results into container 2024-04-21 18:38:52,170:INFO:Uploading model into container now 2024-04-21 18:38:52,170:INFO:_master_model_container: 8 2024-04-21 18:38:52,170:INFO:_display_container: 2 2024-04-21 18:38:52,170:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0, store_covariance=False, tol=0.0001) 2024-04-21 18:38:52,170:INFO:create_model() successfully completed...................................... 2024-04-21 18:38:52,235:INFO:SubProcess create_model() end ================================== 2024-04-21 18:38:52,235:INFO:Creating metrics dataframe 2024-04-21 18:38:52,239:INFO:Initializing Ada Boost Classifier 2024-04-21 18:38:52,239:INFO:Total runtime is 1.8297976493835448 minutes 2024-04-21 18:38:52,240:INFO:SubProcess create_model() called ================================== 2024-04-21 18:38:52,240:INFO:Initializing create_model() 2024-04-21 18:38:52,240:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:38:52,240:INFO:Checking exceptions 2024-04-21 18:38:52,240:INFO:Importing libraries 2024-04-21 18:38:52,240:INFO:Copying training dataset 2024-04-21 18:38:52,245:INFO:Defining folds 2024-04-21 18:38:52,245:INFO:Declaring metric variables 2024-04-21 18:38:52,245:INFO:Importing untrained model 2024-04-21 18:38:52,245:INFO:Ada Boost Classifier Imported successfully 2024-04-21 18:38:52,245:INFO:Starting cross validation 2024-04-21 18:38:52,246:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:38:53,180:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:38:53,180:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:38:53,181:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:38:53,182:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:38:53,183:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:38:53,209:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:38:53,210:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:38:53,210:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:38:53,212:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 18:38:53,771:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:53,774:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:53,777:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:53,839:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:53,841:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:53,843:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:53,846:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:53,846:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:53,846:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:53,847:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:53,848:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:53,849:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:53,850:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:53,850:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:53,852:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:53,853:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:53,855:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:53,856:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:53,866:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:53,871:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:53,880:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:53,999:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:53,913:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) \Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:54,000:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:54,001:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:54,001:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:54,002:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:54,147:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:54,147:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:54,147:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:54,149:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:54,151:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:54,152:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:54,003:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = tra2024-04-21 18:38:54,158:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( kages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:54,161:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:54,164:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:54,167:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:54,330:INFO:Calculating mean and std 2024-04-21 18:38:54,333:INFO:Creating metrics dataframe 2024-04-21 18:38:54,338:INFO:Uploading results into container 2024-04-21 18:38:54,341:INFO:Uploading model into container now 2024-04-21 18:38:54,341:INFO:_master_model_container: 9 2024-04-21 18:38:54,341:INFO:_display_container: 2 2024-04-21 18:38:54,342:INFO:AdaBoostClassifier(algorithm='SAMME.R', estimator=None, learning_rate=1.0, n_estimators=50, random_state=1482) 2024-04-21 18:38:54,342:INFO:create_model() successfully completed...................................... 2024-04-21 18:38:54,415:INFO:SubProcess create_model() end ================================== 2024-04-21 18:38:54,415:INFO:Creating metrics dataframe 2024-04-21 18:38:54,420:INFO:Initializing Gradient Boosting Classifier 2024-04-21 18:38:54,420:INFO:Total runtime is 1.8661396423975625 minutes 2024-04-21 18:38:54,421:INFO:SubProcess create_model() called ================================== 2024-04-21 18:38:54,421:INFO:Initializing create_model() 2024-04-21 18:38:54,421:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:38:54,421:INFO:Checking exceptions 2024-04-21 18:38:54,421:INFO:Importing libraries 2024-04-21 18:38:54,421:INFO:Copying training dataset 2024-04-21 18:38:54,424:INFO:Defining folds 2024-04-21 18:38:54,424:INFO:Declaring metric variables 2024-04-21 18:38:54,424:INFO:Importing untrained model 2024-04-21 18:38:54,425:INFO:Gradient Boosting Classifier Imported successfully 2024-04-21 18:38:54,425:INFO:Starting cross validation 2024-04-21 18:38:54,425:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:38:55,349:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:55,351:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:55,354:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,359:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,363:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,390:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:55,390:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:55,392:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:55,394:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,396:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:55,397:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:55,398:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,403:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,429:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:55,431:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:55,433:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,438:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,443:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,449:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:55,452:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,453:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,457:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,458:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,460:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:55,462:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,463:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,464:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:55,468:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,475:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,479:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,584:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:55,588:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:55,591:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,596:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,601:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:55,601:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,605:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:55,608:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,614:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,619:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,653:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:55,655:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:55,660:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,664:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,671:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,679:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:55,681:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:55,685:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,692:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,698:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:55,715:INFO:Calculating mean and std 2024-04-21 18:38:55,718:INFO:Creating metrics dataframe 2024-04-21 18:38:55,724:INFO:Uploading results into container 2024-04-21 18:38:55,726:INFO:Uploading model into container now 2024-04-21 18:38:55,726:INFO:_master_model_container: 10 2024-04-21 18:38:55,726:INFO:_display_container: 2 2024-04-21 18:38:55,727: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=1482, subsample=1.0, tol=0.0001, validation_fraction=0.1, verbose=0, warm_start=False) 2024-04-21 18:38:55,727:INFO:create_model() successfully completed...................................... 2024-04-21 18:38:55,848:INFO:SubProcess create_model() end ================================== 2024-04-21 18:38:55,848:INFO:Creating metrics dataframe 2024-04-21 18:38:55,985:INFO:Initializing Linear Discriminant Analysis 2024-04-21 18:38:55,985:INFO:Total runtime is 1.8922187368075052 minutes 2024-04-21 18:38:55,985:INFO:SubProcess create_model() called ================================== 2024-04-21 18:38:55,985:INFO:Initializing create_model() 2024-04-21 18:38:55,985:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:38:55,985:INFO:Checking exceptions 2024-04-21 18:38:55,985:INFO:Importing libraries 2024-04-21 18:38:55,985:INFO:Copying training dataset 2024-04-21 18:38:55,989:INFO:Defining folds 2024-04-21 18:38:55,990:INFO:Declaring metric variables 2024-04-21 18:38:55,990:INFO:Importing untrained model 2024-04-21 18:38:55,990:INFO:Linear Discriminant Analysis Imported successfully 2024-04-21 18:38:55,990:INFO:Starting cross validation 2024-04-21 18:38:55,991:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:38:56,056:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,058:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:56,060:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,060:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,062:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:56,064:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,065:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,068:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,069:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,070:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,070:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:56,072:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:56,073:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,076:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,077:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,079:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,081:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,082:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:56,084:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,088:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,089:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,091:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:56,093:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,094:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,095:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:56,098:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,098:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,109:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,110:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,112:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,112:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:56,116:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,119:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,122:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,126:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,129:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,133:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,133:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,137:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:56,139:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,139:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,143:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,144:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,144:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,148:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:56,152:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,152:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,159:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,159:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,170:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,193:INFO:Calculating mean and std 2024-04-21 18:38:56,194:INFO:Creating metrics dataframe 2024-04-21 18:38:56,200:INFO:Uploading results into container 2024-04-21 18:38:56,201:INFO:Uploading model into container now 2024-04-21 18:38:56,201:INFO:_master_model_container: 11 2024-04-21 18:38:56,201:INFO:_display_container: 2 2024-04-21 18:38:56,202:INFO:LinearDiscriminantAnalysis(covariance_estimator=None, n_components=None, priors=None, shrinkage=None, solver='svd', store_covariance=False, tol=0.0001) 2024-04-21 18:38:56,202:INFO:create_model() successfully completed...................................... 2024-04-21 18:38:56,300:INFO:SubProcess create_model() end ================================== 2024-04-21 18:38:56,300:INFO:Creating metrics dataframe 2024-04-21 18:38:56,307:INFO:Initializing Extra Trees Classifier 2024-04-21 18:38:56,307:INFO:Total runtime is 1.8975993514060974 minutes 2024-04-21 18:38:56,307:INFO:SubProcess create_model() called ================================== 2024-04-21 18:38:56,307:INFO:Initializing create_model() 2024-04-21 18:38:56,307:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:38:56,307:INFO:Checking exceptions 2024-04-21 18:38:56,307:INFO:Importing libraries 2024-04-21 18:38:56,307:INFO:Copying training dataset 2024-04-21 18:38:56,312:INFO:Defining folds 2024-04-21 18:38:56,313:INFO:Declaring metric variables 2024-04-21 18:38:56,313:INFO:Importing untrained model 2024-04-21 18:38:56,314:INFO:Extra Trees Classifier Imported successfully 2024-04-21 18:38:56,314:INFO:Starting cross validation 2024-04-21 18:38:56,316:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:38:56,804:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,806:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:56,808:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,811:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,813:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,814:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:56,814:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,815:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,816:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,817:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:56,818:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,819:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,820:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:56,822:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,823:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,824:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,826:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,826:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,827:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,831:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,853:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,854:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,855:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:56,856:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:56,858:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,858:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,862:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,863:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,864:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,864:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:56,865:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,867:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,868:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,870:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,875:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,881:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,883:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,883:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:56,885:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,886:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:56,888:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,888:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,891:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,892:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,896:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,902:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:56,905:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:56,908:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,917:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,921:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:56,939:INFO:Calculating mean and std 2024-04-21 18:38:56,941:INFO:Creating metrics dataframe 2024-04-21 18:38:56,945:INFO:Uploading results into container 2024-04-21 18:38:56,946:INFO:Uploading model into container now 2024-04-21 18:38:56,946:INFO:_master_model_container: 12 2024-04-21 18:38:56,947:INFO:_display_container: 2 2024-04-21 18:38:56,948: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=1482, verbose=0, warm_start=False) 2024-04-21 18:38:56,948:INFO:create_model() successfully completed...................................... 2024-04-21 18:38:57,048:INFO:SubProcess create_model() end ================================== 2024-04-21 18:38:57,048:INFO:Creating metrics dataframe 2024-04-21 18:38:57,055:INFO:Initializing Dummy Classifier 2024-04-21 18:38:57,056:INFO:Total runtime is 1.9100671092669168 minutes 2024-04-21 18:38:57,056:INFO:SubProcess create_model() called ================================== 2024-04-21 18:38:57,056:INFO:Initializing create_model() 2024-04-21 18:38:57,056:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 18:38:57,056:INFO:Checking exceptions 2024-04-21 18:38:57,056:INFO:Importing libraries 2024-04-21 18:38:57,056:INFO:Copying training dataset 2024-04-21 18:38:57,060:INFO:Defining folds 2024-04-21 18:38:57,061:INFO:Declaring metric variables 2024-04-21 18:38:57,061:INFO:Importing untrained model 2024-04-21 18:38:57,061:INFO:Dummy Classifier Imported successfully 2024-04-21 18:38:57,061:INFO:Starting cross validation 2024-04-21 18:38:57,062:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:38:57,093:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:57,094:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:57,095:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:57,096:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:57,097:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,097:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:57,098:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:57,099:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,100:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,100:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,102:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:57,103:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,104:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,104:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,105:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:57,105:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:57,107:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,108:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,112:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:57,115:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:57,117:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,121:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,123:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:57,125:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,128:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:57,130:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:38:57,133:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,137:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,139:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:57,139:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:57,141:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:57,147:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,149:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:57,152:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:57,152:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this 2024-04-21 18:38:57,142:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( o this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) \Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:57,168:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,168:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,172:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:57,173:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,173:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,175:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:57,175:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,176:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:57,177:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:38:57,179:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:38:57,179:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,181:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:57,181:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,185:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,186:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:57,186:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,188:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 18:38:57,188:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,191:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,192:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,196:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:38:57,219:INFO:Calculating mean and std 2024-04-21 18:38:57,220:INFO:Creating metrics dataframe 2024-04-21 18:38:57,225:INFO:Uploading results into container 2024-04-21 18:38:57,226:INFO:Uploading model into container now 2024-04-21 18:38:57,226:INFO:_master_model_container: 13 2024-04-21 18:38:57,226:INFO:_display_container: 2 2024-04-21 18:38:57,227:INFO:DummyClassifier(constant=None, random_state=1482, strategy='prior') 2024-04-21 18:38:57,227:INFO:create_model() successfully completed...................................... 2024-04-21 18:38:57,320:INFO:SubProcess create_model() end ================================== 2024-04-21 18:38:57,321:INFO:Creating metrics dataframe 2024-04-21 18:38:57,331:INFO:Initializing create_model() 2024-04-21 18:38:57,331:INFO:create_model(self=, estimator=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=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), 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={}) 2024-04-21 18:38:57,332:INFO:Checking exceptions 2024-04-21 18:38:57,333:INFO:Importing libraries 2024-04-21 18:38:57,333:INFO:Copying training dataset 2024-04-21 18:38:57,338:INFO:Defining folds 2024-04-21 18:38:57,338:INFO:Declaring metric variables 2024-04-21 18:38:57,339:INFO:Importing untrained model 2024-04-21 18:38:57,339:INFO:Declaring custom model 2024-04-21 18:38:57,339:INFO:Logistic Regression Imported successfully 2024-04-21 18:38:57,341:INFO:Cross validation set to False 2024-04-21 18:38:57,341:INFO:Fitting Model 2024-04-21 18:38:57,478: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=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 18:38:57,478:INFO:create_model() successfully completed...................................... 2024-04-21 18:38:57,618:INFO:_master_model_container: 13 2024-04-21 18:38:57,618:INFO:_display_container: 2 2024-04-21 18:38:57,619: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=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 18:38:57,619:INFO:compare_models() successfully completed...................................... 2024-04-21 18:39:02,080:INFO:Initializing create_model() 2024-04-21 18:39:02,080:INFO:create_model(self=, estimator=lr, 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={}) 2024-04-21 18:39:02,080:INFO:Checking exceptions 2024-04-21 18:39:02,082:INFO:Importing libraries 2024-04-21 18:39:02,082:INFO:Copying training dataset 2024-04-21 18:39:02,085:INFO:Defining folds 2024-04-21 18:39:02,085:INFO:Declaring metric variables 2024-04-21 18:39:02,085:INFO:Importing untrained model 2024-04-21 18:39:02,086:INFO:Logistic Regression Imported successfully 2024-04-21 18:39:02,086:INFO:Starting cross validation 2024-04-21 18:39:02,086:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:39:02,167:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:02,171:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:39:02,174:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,178:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,182:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,183:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:02,185:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:39:02,185:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:02,186:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:39:02,187:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:02,187:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,190:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:39:02,190:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,191:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,192:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,192:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:02,193:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,194:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:39:02,195:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,196:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,196:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,197:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,199:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:02,201:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,201:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,202:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:39:02,204:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,205:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,208:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,211:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,213:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:02,214:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:39:02,216:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,216:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:02,218:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,219:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:02,219:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:39:02,221:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:39:02,221:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,221:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,222:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,224:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,226:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,227:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,228:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,229:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:02,230:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:39:02,232:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,235:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,238:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:02,242:INFO:Calculating mean and std 2024-04-21 18:39:02,243:INFO:Creating metrics dataframe 2024-04-21 18:39:02,246:INFO:Finalizing model 2024-04-21 18:39:02,292:INFO:Uploading results into container 2024-04-21 18:39:02,293:INFO:Uploading model into container now 2024-04-21 18:39:02,307:INFO:_master_model_container: 14 2024-04-21 18:39:02,307:INFO:_display_container: 3 2024-04-21 18:39:02,308: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=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 18:39:02,308:INFO:create_model() successfully completed...................................... 2024-04-21 18:39:02,387:INFO:Initializing tune_model() 2024-04-21 18:39:02,388:INFO:tune_model(self=, estimator=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=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), 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={}) 2024-04-21 18:39:02,388:INFO:Checking exceptions 2024-04-21 18:39:02,390:INFO:Copying training dataset 2024-04-21 18:39:02,392:INFO:Checking base model 2024-04-21 18:39:02,392:INFO:Base model : Logistic Regression 2024-04-21 18:39:02,392:INFO:Declaring metric variables 2024-04-21 18:39:02,392:INFO:Defining Hyperparameters 2024-04-21 18:39:02,467:INFO:Tuning with n_jobs=-1 2024-04-21 18:39:02,468:INFO:Initializing RandomizedSearchCV 2024-04-21 18:39:03,468:INFO:best_params: {'actual_estimator__class_weight': 'balanced', 'actual_estimator__C': 4.337} 2024-04-21 18:39:03,468:INFO:Hyperparameter search completed 2024-04-21 18:39:03,469:INFO:SubProcess create_model() called ================================== 2024-04-21 18:39:03,469:INFO:Initializing create_model() 2024-04-21 18:39:03,469:INFO:create_model(self=, estimator=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=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'class_weight': 'balanced', 'C': 4.337}) 2024-04-21 18:39:03,469:INFO:Checking exceptions 2024-04-21 18:39:03,469:INFO:Importing libraries 2024-04-21 18:39:03,469:INFO:Copying training dataset 2024-04-21 18:39:03,473:INFO:Defining folds 2024-04-21 18:39:03,473:INFO:Declaring metric variables 2024-04-21 18:39:03,474:INFO:Importing untrained model 2024-04-21 18:39:03,474:INFO:Declaring custom model 2024-04-21 18:39:03,475:INFO:Logistic Regression Imported successfully 2024-04-21 18:39:03,475:INFO:Starting cross validation 2024-04-21 18:39:03,476:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:39:03,570:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,573:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:39:03,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,575:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,577:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:39:03,580:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,580:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,581:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,583:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:39:03,584:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,585:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,586:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,586:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,588:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:39:03,589:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,590:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,591:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,593:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,595:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,599:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,605:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,607:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,607:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:39:03,610:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:39:03,610:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,612:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,614:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,615:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,617:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,619:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,619:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,621:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,621:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:39:03,622:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:39:03,623:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,624:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,625:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,626:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,627:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,627:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,629:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,629:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:39:03,630:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,630:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:39:03,631:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,632:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,633:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,634:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,636:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,637:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,650:INFO:Calculating mean and std 2024-04-21 18:39:03,651:INFO:Creating metrics dataframe 2024-04-21 18:39:03,653:INFO:Finalizing model 2024-04-21 18:39:03,698:INFO:Uploading results into container 2024-04-21 18:39:03,699:INFO:Uploading model into container now 2024-04-21 18:39:03,699:INFO:_master_model_container: 15 2024-04-21 18:39:03,699:INFO:_display_container: 4 2024-04-21 18:39:03,700:INFO:LogisticRegression(C=4.337, class_weight='balanced', dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=None, max_iter=1000, multi_class='auto', n_jobs=None, penalty='l2', random_state=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 18:39:03,700:INFO:create_model() successfully completed...................................... 2024-04-21 18:39:03,774:INFO:SubProcess create_model() end ================================== 2024-04-21 18:39:03,775:INFO:choose_better activated 2024-04-21 18:39:03,775:INFO:SubProcess create_model() called ================================== 2024-04-21 18:39:03,775:INFO:Initializing create_model() 2024-04-21 18:39:03,775:INFO:create_model(self=, estimator=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=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), 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={}) 2024-04-21 18:39:03,775:INFO:Checking exceptions 2024-04-21 18:39:03,776:INFO:Importing libraries 2024-04-21 18:39:03,776:INFO:Copying training dataset 2024-04-21 18:39:03,779:INFO:Defining folds 2024-04-21 18:39:03,779:INFO:Declaring metric variables 2024-04-21 18:39:03,779:INFO:Importing untrained model 2024-04-21 18:39:03,779:INFO:Declaring custom model 2024-04-21 18:39:03,779:INFO:Logistic Regression Imported successfully 2024-04-21 18:39:03,780:INFO:Starting cross validation 2024-04-21 18:39:03,780:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 18:39:03,863:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,865:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:39:03,868:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,872:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,873:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,875:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:39:03,877:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,879:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,880:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,882:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,883:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:39:03,883:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,883:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,885:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:39:03,885:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:39:03,886:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,888:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,888:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,888:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,889:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,889:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,891:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,892:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:39:03,893:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,894:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,895:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,896:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,898:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,901:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,903:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,905:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,905:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:39:03,905:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,907:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,907:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:39:03,907:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 18:39:03,907:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,908:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,908:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,910:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,912:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,912:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,915:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,916:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 18:39:03,916:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,916:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,917:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 18:39:03,919:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,922:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,924:WARNING:C:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 18:39:03,934:INFO:Calculating mean and std 2024-04-21 18:39:03,934:INFO:Creating metrics dataframe 2024-04-21 18:39:03,936:INFO:Finalizing model 2024-04-21 18:39:03,978:INFO:Uploading results into container 2024-04-21 18:39:03,978:INFO:Uploading model into container now 2024-04-21 18:39:03,979:INFO:_master_model_container: 16 2024-04-21 18:39:03,979:INFO:_display_container: 5 2024-04-21 18:39:03,979: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=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 18:39:03,979:INFO:create_model() successfully completed...................................... 2024-04-21 18:39:04,055:INFO:SubProcess create_model() end ================================== 2024-04-21 18:39:04,056: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=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) result for Accuracy is 1.0 2024-04-21 18:39:04,057:INFO:LogisticRegression(C=4.337, class_weight='balanced', dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=None, max_iter=1000, multi_class='auto', n_jobs=None, penalty='l2', random_state=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) result for Accuracy is 1.0 2024-04-21 18:39:04,057: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=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) is best model 2024-04-21 18:39:04,057:INFO:choose_better completed 2024-04-21 18:39:04,057: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 18:39:04,071:INFO:_master_model_container: 16 2024-04-21 18:39:04,071:INFO:_display_container: 4 2024-04-21 18:39:04,071: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=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 18:39:04,071:INFO:tune_model() successfully completed...................................... 2024-04-21 18:39:04,153:INFO:Initializing evaluate_model() 2024-04-21 18:39:04,153:INFO:evaluate_model(self=, estimator=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=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) 2024-04-21 18:39:04,444:INFO:Initializing plot_model() 2024-04-21 18:39:04,444:INFO:plot_model(self=, estimator=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=1482, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False), plot=pipeline, scale=1, save=False, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) 2024-04-21 18:39:04,444:INFO:Checking exceptions 2024-04-21 18:39:04,445:INFO:Preloading libraries 2024-04-21 18:39:04,446:INFO:Copying training dataset 2024-04-21 18:39:04,446:INFO:Plot type: pipeline 2024-04-21 18:39:08,667:INFO:Visual Rendered Successfully 2024-04-21 18:39:08,762:INFO:plot_model() successfully completed...................................... 2024-04-21 19:32:57,824:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 19:32:57,824:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 19:32:57,824:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 19:32:57,824:WARNING: 'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. 2024-04-21 19:33:04,193:INFO:PyCaret ClassificationExperiment 2024-04-21 19:33:04,193:INFO:Logging name: clf-default-name 2024-04-21 19:33:04,193:INFO:ML Usecase: MLUsecase.CLASSIFICATION 2024-04-21 19:33:04,193:INFO:version 3.3.0 2024-04-21 19:33:04,193:INFO:Initializing setup() 2024-04-21 19:33:04,193:INFO:self.USI: cef9 2024-04-21 19:33:04,193:INFO:self._variable_keys: {'y_test', 'X', 'is_multiclass', '_ml_usecase', 'target_param', 'fold_groups_param', 'gpu_param', 'logging_param', 'fix_imbalance', 'n_jobs_param', 'USI', 'y', 'fold_generator', 'exp_name_log', 'idx', 'gpu_n_jobs_param', 'y_train', 'log_plots_param', 'X_test', 'X_train', 'html_param', 'pipeline', 'memory', 'exp_id', 'data', 'seed', '_available_plots', 'fold_shuffle_param'} 2024-04-21 19:33:04,193:INFO:Checking environment 2024-04-21 19:33:04,194:INFO:python_version: 3.11.5 2024-04-21 19:33:04,194:INFO:python_build: ('main', 'Sep 11 2023 13:26:23') 2024-04-21 19:33:04,194:INFO:machine: AMD64 2024-04-21 19:33:04,207:INFO:platform: Windows-10-10.0.22631-SP0 2024-04-21 19:33:04,217:INFO:Memory: svmem(total=16782184448, available=6591143936, percent=60.7, used=10191040512, free=6591143936) 2024-04-21 19:33:04,217:INFO:Physical Core: 10 2024-04-21 19:33:04,217:INFO:Logical Core: 16 2024-04-21 19:33:04,217:INFO:Checking libraries 2024-04-21 19:33:04,217:INFO:System: 2024-04-21 19:33:04,217:INFO: python: 3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:26:23) [MSC v.1916 64 bit (AMD64)] 2024-04-21 19:33:04,217:INFO:executable: c:\Users\arpit\anaconda3\envs\arpit-test\python.exe 2024-04-21 19:33:04,217:INFO: machine: Windows-10-10.0.22631-SP0 2024-04-21 19:33:04,217:INFO:PyCaret required dependencies: 2024-04-21 19:33:04,276:INFO: pip: 23.3 2024-04-21 19:33:04,276:INFO: setuptools: 68.0.0 2024-04-21 19:33:04,276:INFO: pycaret: 3.3.0 2024-04-21 19:33:04,276:INFO: IPython: 8.16.1 2024-04-21 19:33:04,276:INFO: ipywidgets: 8.1.2 2024-04-21 19:33:04,276:INFO: tqdm: 4.66.1 2024-04-21 19:33:04,276:INFO: numpy: 1.23.5 2024-04-21 19:33:04,276:INFO: pandas: 1.5.3 2024-04-21 19:33:04,276:INFO: jinja2: 3.1.2 2024-04-21 19:33:04,276:INFO: scipy: 1.11.4 2024-04-21 19:33:04,276:INFO: joblib: 1.3.2 2024-04-21 19:33:04,276:INFO: sklearn: 1.4.1.post1 2024-04-21 19:33:04,276:INFO: pyod: 1.1.3 2024-04-21 19:33:04,276:INFO: imblearn: 0.12.0 2024-04-21 19:33:04,276:INFO: category_encoders: 2.6.3 2024-04-21 19:33:04,276:INFO: lightgbm: 4.3.0 2024-04-21 19:33:04,276:INFO: numba: 0.58.1 2024-04-21 19:33:04,276:INFO: requests: 2.31.0 2024-04-21 19:33:04,276:INFO: matplotlib: 3.7.5 2024-04-21 19:33:04,276:INFO: scikitplot: 0.3.7 2024-04-21 19:33:04,276:INFO: yellowbrick: 1.5 2024-04-21 19:33:04,276:INFO: plotly: 5.18.0 2024-04-21 19:33:04,276:INFO: plotly-resampler: Not installed 2024-04-21 19:33:04,276:INFO: kaleido: 0.2.1 2024-04-21 19:33:04,276:INFO: schemdraw: 0.15 2024-04-21 19:33:04,276:INFO: statsmodels: 0.14.1 2024-04-21 19:33:04,276:INFO: sktime: 0.26.0 2024-04-21 19:33:04,276:INFO: tbats: 1.1.3 2024-04-21 19:33:04,276:INFO: pmdarima: 2.0.4 2024-04-21 19:33:04,276:INFO: psutil: 5.9.6 2024-04-21 19:33:04,276:INFO: markupsafe: 2.1.3 2024-04-21 19:33:04,276:INFO: pickle5: Not installed 2024-04-21 19:33:04,276:INFO: cloudpickle: 3.0.0 2024-04-21 19:33:04,276:INFO: deprecation: 2.1.0 2024-04-21 19:33:04,276:INFO: xxhash: 3.4.1 2024-04-21 19:33:04,276:INFO: wurlitzer: Not installed 2024-04-21 19:33:04,276:INFO:PyCaret optional dependencies: 2024-04-21 19:33:04,285:INFO: shap: Not installed 2024-04-21 19:33:04,285:INFO: interpret: Not installed 2024-04-21 19:33:04,285:INFO: umap: 0.5.5 2024-04-21 19:33:04,285:INFO: ydata_profiling: 4.6.5 2024-04-21 19:33:04,285:INFO: explainerdashboard: Not installed 2024-04-21 19:33:04,285:INFO: autoviz: Not installed 2024-04-21 19:33:04,285:INFO: fairlearn: Not installed 2024-04-21 19:33:04,285:INFO: deepchecks: Not installed 2024-04-21 19:33:04,285:INFO: xgboost: Not installed 2024-04-21 19:33:04,285:INFO: catboost: Not installed 2024-04-21 19:33:04,285:INFO: kmodes: Not installed 2024-04-21 19:33:04,285:INFO: mlxtend: Not installed 2024-04-21 19:33:04,285:INFO: statsforecast: Not installed 2024-04-21 19:33:04,285:INFO: tune_sklearn: Not installed 2024-04-21 19:33:04,285:INFO: ray: Not installed 2024-04-21 19:33:04,285:INFO: hyperopt: Not installed 2024-04-21 19:33:04,285:INFO: optuna: Not installed 2024-04-21 19:33:04,285:INFO: skopt: Not installed 2024-04-21 19:33:04,285:INFO: mlflow: Not installed 2024-04-21 19:33:04,285:INFO: gradio: Not installed 2024-04-21 19:33:04,285:INFO: fastapi: 0.110.1 2024-04-21 19:33:04,285:INFO: uvicorn: 0.29.0 2024-04-21 19:33:04,285:INFO: m2cgen: Not installed 2024-04-21 19:33:04,285:INFO: evidently: Not installed 2024-04-21 19:33:04,285:INFO: fugue: Not installed 2024-04-21 19:33:04,285:INFO: streamlit: 1.29.0 2024-04-21 19:33:04,285:INFO: prophet: Not installed 2024-04-21 19:33:04,285:INFO:None 2024-04-21 19:33:04,285:INFO:Set up data. 2024-04-21 19:33:04,395:INFO:Set up folding strategy. 2024-04-21 19:33:04,395:INFO:Set up train/test split. 2024-04-21 19:33:04,395:INFO:Set up index. 2024-04-21 19:33:04,395:INFO:Assigning column types. 2024-04-21 19:33:04,401:INFO:Engine successfully changes for model 'lr' to 'sklearn'. 2024-04-21 19:33:04,429:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. 2024-04-21 19:33:04,429:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 19:33:04,457:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,457:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,484:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. 2024-04-21 19:33:04,484:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 19:33:04,505:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,505:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,505:INFO:Engine successfully changes for model 'knn' to 'sklearn'. 2024-04-21 19:33:04,533:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 19:33:04,549:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,554:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,581:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None. 2024-04-21 19:33:04,603:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,604:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,604:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'. 2024-04-21 19:33:04,651:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,651:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,700:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,700:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,707:INFO:Preparing preprocessing pipeline... 2024-04-21 19:33:04,708:INFO:Set up label encoding. 2024-04-21 19:33:04,708:INFO:Set up simple imputation. 2024-04-21 19:33:04,728:INFO:Finished creating preprocessing pipeline. 2024-04-21 19:33:04,730:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\arpit\AppData\Local\Temp\joblib), steps=[('label_encoding', TransformerWrapperWithInverse(exclude=None, include=None, transformer=LabelEncoder())), ('numerical_imputer', TransformerWrapper(exclude=None, include=['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'], transformer=SimpleImputer(add_indicator=False, copy=True, fill_value=None, keep_empty_features=False, missing_values=nan, strategy='mean'))), ('categorical_imputer', TransformerWrapper(exclude=None, include=[], transformer=SimpleImputer(add_indicator=False, copy=True, fill_value=None, keep_empty_features=False, missing_values=nan, strategy='most_frequent')))], verbose=False) 2024-04-21 19:33:04,730:INFO:Creating final display dataframe. 2024-04-21 19:33:04,778:INFO:Setup _display_container: Description Value 0 Session id 7944 1 Target Species 2 Target type Multiclass 3 Target mapping Iris-setosa: 0, Iris-versicolor: 1, Iris-virgi... 4 Original data shape (150, 6) 5 Transformed data shape (150, 6) 6 Transformed train set shape (105, 6) 7 Transformed test set shape (45, 6) 8 Numeric features 5 9 Preprocess True 10 Imputation type simple 11 Numeric imputation mean 12 Categorical imputation mode 13 Fold Generator StratifiedKFold 14 Fold Number 10 15 CPU Jobs -1 16 Use GPU False 17 Log Experiment False 18 Experiment Name clf-default-name 19 USI cef9 2024-04-21 19:33:04,853:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,859:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,908:WARNING: 'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,908:WARNING: 'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. Alternately, you can install this by running `pip install pycaret[models]` 2024-04-21 19:33:04,908:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\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 19:33:04,908:INFO:setup() successfully completed in 0.73s............... 2024-04-21 19:33:04,908:INFO:Initializing get_config() 2024-04-21 19:33:04,908:INFO:get_config(self=, variable=X_train) 2024-04-21 19:33:04,908:INFO:Variable: 'X_train' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_train_transformed' instead. 2024-04-21 19:33:04,915:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pycaret_experiment\pycaret_experiment.py:321: UserWarning: Variable: 'X_train' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_train_transformed' instead. warnings.warn(msg) # print on screen 2024-04-21 19:33:04,917:INFO:Variable: returned as Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm 44 45 5.1 3.8 1.9 0.4 30 31 4.8 3.1 1.6 0.2 23 24 5.1 3.3 1.7 0.5 72 73 6.3 2.5 4.9 1.5 106 107 4.9 2.5 4.5 1.7 .. ... ... ... ... ... 101 102 5.8 2.7 5.1 1.9 16 17 5.4 3.9 1.3 0.4 134 135 6.1 2.6 5.6 1.4 125 126 7.2 3.2 6.0 1.8 84 85 5.4 3.0 4.5 1.5 [105 rows x 5 columns] 2024-04-21 19:33:04,917:INFO:get_config() successfully completed...................................... 2024-04-21 19:33:04,917:INFO:Initializing get_config() 2024-04-21 19:33:04,917:INFO:get_config(self=, variable=X_test) 2024-04-21 19:33:04,917:INFO:Variable: 'X_test' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_test_transformed' instead. 2024-04-21 19:33:04,922:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pycaret_experiment\pycaret_experiment.py:321: UserWarning: Variable: 'X_test' used to return the transformed values in PyCaret 2.x. From PyCaret 3.x, this will return the raw values. If you need the transformed values, call get_config with 'X_test_transformed' instead. warnings.warn(msg) # print on screen 2024-04-21 19:33:04,929:INFO:Variable: returned as Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm 104 105 6.5 3.0 5.8 2.2 148 149 6.2 3.4 5.4 2.3 98 99 5.1 2.5 3.0 1.1 12 13 4.8 3.0 1.4 0.1 135 136 7.7 3.0 6.1 2.3 63 64 6.1 2.9 4.7 1.4 87 88 6.3 2.3 4.4 1.3 78 79 6.0 2.9 4.5 1.5 13 14 4.3 3.0 1.1 0.1 33 34 5.5 4.2 1.4 0.2 55 56 5.7 2.8 4.5 1.3 10 11 5.4 3.7 1.5 0.2 26 27 5.0 3.4 1.6 0.4 32 33 5.2 4.1 1.5 0.1 132 133 6.4 2.8 5.6 2.2 62 63 6.0 2.2 4.0 1.0 145 146 6.7 3.0 5.2 2.3 68 69 6.2 2.2 4.5 1.5 143 144 6.8 3.2 5.9 2.3 105 106 7.6 3.0 6.6 2.1 139 140 6.9 3.1 5.4 2.1 142 143 5.8 2.7 5.1 1.9 15 16 5.7 4.4 1.5 0.4 76 77 6.8 2.8 4.8 1.4 24 25 4.8 3.4 1.9 0.2 39 40 5.1 3.4 1.5 0.2 111 112 6.4 2.7 5.3 1.9 25 26 5.0 3.0 1.6 0.2 7 8 5.0 3.4 1.5 0.2 40 41 5.0 3.5 1.3 0.3 146 147 6.3 2.5 5.0 1.9 124 125 6.7 3.3 5.7 2.1 138 139 6.0 3.0 4.8 1.8 5 6 5.4 3.9 1.7 0.4 66 67 5.6 3.0 4.5 1.5 90 91 5.5 2.6 4.4 1.2 35 36 5.0 3.2 1.2 0.2 97 98 6.2 2.9 4.3 1.3 64 65 5.6 2.9 3.6 1.3 17 18 5.1 3.5 1.4 0.3 53 54 5.5 2.3 4.0 1.3 137 138 6.4 3.1 5.5 1.8 54 55 6.5 2.8 4.6 1.5 102 103 7.1 3.0 5.9 2.1 89 90 5.5 2.5 4.0 1.3 2024-04-21 19:33:04,929:INFO:get_config() successfully completed...................................... 2024-04-21 19:33:04,929:INFO:Initializing compare_models() 2024-04-21 19:33:04,929:INFO:compare_models(self=, include=None, exclude=['lightgbm', 'catboost', 'xgboost'], 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': , 'include': None, 'exclude': ['lightgbm', 'catboost', 'xgboost'], '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__': }) 2024-04-21 19:33:04,929:INFO:Checking exceptions 2024-04-21 19:33:04,929:INFO:Preparing display monitor 2024-04-21 19:33:04,936:INFO:Initializing Logistic Regression 2024-04-21 19:33:04,936:INFO:Total runtime is 0.0 minutes 2024-04-21 19:33:04,937:INFO:SubProcess create_model() called ================================== 2024-04-21 19:33:04,937:INFO:Initializing create_model() 2024-04-21 19:33:04,937:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 19:33:04,937:INFO:Checking exceptions 2024-04-21 19:33:04,937:INFO:Importing libraries 2024-04-21 19:33:04,937:INFO:Copying training dataset 2024-04-21 19:33:04,939:INFO:Defining folds 2024-04-21 19:33:04,939:INFO:Declaring metric variables 2024-04-21 19:33:04,939:INFO:Importing untrained model 2024-04-21 19:33:04,939:INFO:Logistic Regression Imported successfully 2024-04-21 19:33:04,939:INFO:Starting cross validation 2024-04-21 19:33:04,939:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:09,505:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:09,505:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:09,505:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:09,505:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:09,505:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:09,505:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:09,505:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:09,505:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:09,505:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:09,505:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:09,512:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:09,512:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:09,512:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:09,512:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:09,512:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:09,512:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:09,512:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:09,520:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:09,520:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,523:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,533:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,533:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,533:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,543:INFO:Calculating mean and std 2024-04-21 19:33:09,557:INFO:Creating metrics dataframe 2024-04-21 19:33:09,558:INFO:Uploading results into container 2024-04-21 19:33:09,558:INFO:Uploading model into container now 2024-04-21 19:33:09,558:INFO:_master_model_container: 1 2024-04-21 19:33:09,558:INFO:_display_container: 2 2024-04-21 19:33:09,558: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=7944, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False) 2024-04-21 19:33:09,558:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:09,643:INFO:SubProcess create_model() end ================================== 2024-04-21 19:33:09,643:INFO:Creating metrics dataframe 2024-04-21 19:33:09,643:INFO:Initializing K Neighbors Classifier 2024-04-21 19:33:09,643:INFO:Total runtime is 0.07844887574513754 minutes 2024-04-21 19:33:09,643:INFO:SubProcess create_model() called ================================== 2024-04-21 19:33:09,643:INFO:Initializing create_model() 2024-04-21 19:33:09,643:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 19:33:09,643:INFO:Checking exceptions 2024-04-21 19:33:09,643:INFO:Importing libraries 2024-04-21 19:33:09,643:INFO:Copying training dataset 2024-04-21 19:33:09,643:INFO:Defining folds 2024-04-21 19:33:09,643:INFO:Declaring metric variables 2024-04-21 19:33:09,643:INFO:Importing untrained model 2024-04-21 19:33:09,643:INFO:K Neighbors Classifier Imported successfully 2024-04-21 19:33:09,643:INFO:Starting cross validation 2024-04-21 19:33:09,643:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:09,709:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:09,709:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:09,709:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:09,709:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:09,709:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:09,709:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:09,709:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,709:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:09,709:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,709:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,719:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,719:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,719:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,721:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,721:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,721:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,721:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:09,721:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,508:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,508:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,511:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,513:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,518:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:12,518:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,518:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:12,518:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,518:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,518:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:12,525:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:12,532:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,539:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,539:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,539:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,539:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,539:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,539:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,539:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,539:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:12,539:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,539:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,546:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,546:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,548:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,553:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,558:INFO:Calculating mean and std 2024-04-21 19:33:12,559:INFO:Creating metrics dataframe 2024-04-21 19:33:12,561:INFO:Uploading results into container 2024-04-21 19:33:12,562:INFO:Uploading model into container now 2024-04-21 19:33:12,562:INFO:_master_model_container: 2 2024-04-21 19:33:12,562:INFO:_display_container: 2 2024-04-21 19:33:12,563: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 19:33:12,563:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:12,629:INFO:SubProcess create_model() end ================================== 2024-04-21 19:33:12,629:INFO:Creating metrics dataframe 2024-04-21 19:33:12,636:INFO:Initializing Naive Bayes 2024-04-21 19:33:12,636:INFO:Total runtime is 0.1283296267191569 minutes 2024-04-21 19:33:12,636:INFO:SubProcess create_model() called ================================== 2024-04-21 19:33:12,636:INFO:Initializing create_model() 2024-04-21 19:33:12,636:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 19:33:12,636:INFO:Checking exceptions 2024-04-21 19:33:12,636:INFO:Importing libraries 2024-04-21 19:33:12,636:INFO:Copying training dataset 2024-04-21 19:33:12,636:INFO:Defining folds 2024-04-21 19:33:12,636:INFO:Declaring metric variables 2024-04-21 19:33:12,636:INFO:Importing untrained model 2024-04-21 19:33:12,636:INFO:Naive Bayes Imported successfully 2024-04-21 19:33:12,636:INFO:Starting cross validation 2024-04-21 19:33:12,636:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:12,664:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,666:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:12,666:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,670:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,670:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,670:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,671:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,671:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,672:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:12,673:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:12,673:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:12,673:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:12,676:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) \Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:12,674:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,674:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,678:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:12,678:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:12,678:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:12,678:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:12,678:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,678:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,678:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,678:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,678:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,678:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,685:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,685:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,685:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,685:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,686:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,687:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,687:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,687:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,688:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,689:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,689:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,690:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,690:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,692:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,692:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,707:INFO:Calculating mean and std 2024-04-21 19:33:12,708:INFO:Creating metrics dataframe 2024-04-21 19:33:12,711:INFO:Uploading results into container 2024-04-21 19:33:12,711:INFO:Uploading model into container now 2024-04-21 19:33:12,711:INFO:_master_model_container: 3 2024-04-21 19:33:12,711:INFO:_display_container: 2 2024-04-21 19:33:12,712:INFO:GaussianNB(priors=None, var_smoothing=1e-09) 2024-04-21 19:33:12,712:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:12,775:INFO:SubProcess create_model() end ================================== 2024-04-21 19:33:12,775:INFO:Creating metrics dataframe 2024-04-21 19:33:12,777:INFO:Initializing Decision Tree Classifier 2024-04-21 19:33:12,779:INFO:Total runtime is 0.13068462212880452 minutes 2024-04-21 19:33:12,779:INFO:SubProcess create_model() called ================================== 2024-04-21 19:33:12,779:INFO:Initializing create_model() 2024-04-21 19:33:12,779:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 19:33:12,779:INFO:Checking exceptions 2024-04-21 19:33:12,779:INFO:Importing libraries 2024-04-21 19:33:12,779:INFO:Copying training dataset 2024-04-21 19:33:12,779:INFO:Defining folds 2024-04-21 19:33:12,779:INFO:Declaring metric variables 2024-04-21 19:33:12,779:INFO:Importing untrained model 2024-04-21 19:33:12,779:INFO:Decision Tree Classifier Imported successfully 2024-04-21 19:33:12,781:INFO:Starting cross validation 2024-04-21 19:33:12,782:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:12,829:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,829:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,829:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,830:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,830:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,830:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,830:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,830:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,831:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,831:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,831:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,832:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,833:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,833:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,833:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,834:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,834:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,836:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,850:INFO:Calculating mean and std 2024-04-21 19:33:12,850:INFO:Creating metrics dataframe 2024-04-21 19:33:12,852:INFO:Uploading results into container 2024-04-21 19:33:12,852:INFO:Uploading model into container now 2024-04-21 19:33:12,852:INFO:_master_model_container: 4 2024-04-21 19:33:12,852:INFO:_display_container: 2 2024-04-21 19:33:12,852: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=7944, splitter='best') 2024-04-21 19:33:12,852:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:12,914:INFO:SubProcess create_model() end ================================== 2024-04-21 19:33:12,914:INFO:Creating metrics dataframe 2024-04-21 19:33:12,914:INFO:Initializing SVM - Linear Kernel 2024-04-21 19:33:12,914:INFO:Total runtime is 0.1329575220743815 minutes 2024-04-21 19:33:12,914:INFO:SubProcess create_model() called ================================== 2024-04-21 19:33:12,914:INFO:Initializing create_model() 2024-04-21 19:33:12,914:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 19:33:12,914:INFO:Checking exceptions 2024-04-21 19:33:12,914:INFO:Importing libraries 2024-04-21 19:33:12,914:INFO:Copying training dataset 2024-04-21 19:33:12,914:INFO:Defining folds 2024-04-21 19:33:12,921:INFO:Declaring metric variables 2024-04-21 19:33:12,921:INFO:Importing untrained model 2024-04-21 19:33:12,921:INFO:SVM - Linear Kernel Imported successfully 2024-04-21 19:33:12,921:INFO:Starting cross validation 2024-04-21 19:33:12,921:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:12,977:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:12,981:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:12,982:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:12,983:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:12,983:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,983:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,984:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,984:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,987:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,987:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,989:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:12,989:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:12,989:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,989:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:12,989:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,990:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:12,990:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:12,990:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:12,990:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:12,991:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,991:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:12,991:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,991:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:12,991:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,991:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,992:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,992:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,993:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,993:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,993:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,993:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,994:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,995:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:12,995:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,996:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,996:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,996:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,996:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,997:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:12,997:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:12,997:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,997:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:12,997:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,997:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,997:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:12,997:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,997:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:12,997:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,013:INFO:Calculating mean and std 2024-04-21 19:33:13,014:INFO:Creating metrics dataframe 2024-04-21 19:33:13,015:INFO:Uploading results into container 2024-04-21 19:33:13,015:INFO:Uploading model into container now 2024-04-21 19:33:13,018:INFO:_master_model_container: 5 2024-04-21 19:33:13,018:INFO:_display_container: 2 2024-04-21 19:33:13,018: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=7944, shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0, warm_start=False) 2024-04-21 19:33:13,018:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:13,073:INFO:SubProcess create_model() end ================================== 2024-04-21 19:33:13,073:INFO:Creating metrics dataframe 2024-04-21 19:33:13,081:INFO:Initializing Ridge Classifier 2024-04-21 19:33:13,081:INFO:Total runtime is 0.13575294812520344 minutes 2024-04-21 19:33:13,081:INFO:SubProcess create_model() called ================================== 2024-04-21 19:33:13,081:INFO:Initializing create_model() 2024-04-21 19:33:13,081:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 19:33:13,081:INFO:Checking exceptions 2024-04-21 19:33:13,081:INFO:Importing libraries 2024-04-21 19:33:13,081:INFO:Copying training dataset 2024-04-21 19:33:13,082:INFO:Defining folds 2024-04-21 19:33:13,083:INFO:Declaring metric variables 2024-04-21 19:33:13,083:INFO:Importing untrained model 2024-04-21 19:33:13,083:INFO:Ridge Classifier Imported successfully 2024-04-21 19:33:13,083:INFO:Starting cross validation 2024-04-21 19:33:13,083:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:13,110:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:13,110:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:13,115:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:13,115:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:13,116:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,116:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,117:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:13,117:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:13,117:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,118:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,118:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,118:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,118:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,119:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:13,119:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:13,119:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:13,119:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,121:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 344, in _score response_method = _check_response_method(estimator, self._response_method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\validation.py", line 2106, in _check_response_method raise AttributeError( AttributeError: Pipeline has none of the following attributes: predict_proba. warnings.warn( 2024-04-21 19:33:13,121:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,121:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,121:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,121:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,121:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,121:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,122:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,122:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,123:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,123:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,123:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,125:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,125:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,125:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,125:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,125:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,125:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,125:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,125:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,130:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,131:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,141:INFO:Calculating mean and std 2024-04-21 19:33:13,141:INFO:Creating metrics dataframe 2024-04-21 19:33:13,144:INFO:Uploading results into container 2024-04-21 19:33:13,145:INFO:Uploading model into container now 2024-04-21 19:33:13,145:INFO:_master_model_container: 6 2024-04-21 19:33:13,145:INFO:_display_container: 2 2024-04-21 19:33:13,145:INFO:RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True, max_iter=None, positive=False, random_state=7944, solver='auto', tol=0.0001) 2024-04-21 19:33:13,145:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:13,203:INFO:SubProcess create_model() end ================================== 2024-04-21 19:33:13,203:INFO:Creating metrics dataframe 2024-04-21 19:33:13,205:INFO:Initializing Random Forest Classifier 2024-04-21 19:33:13,205:INFO:Total runtime is 0.13781691789627074 minutes 2024-04-21 19:33:13,205:INFO:SubProcess create_model() called ================================== 2024-04-21 19:33:13,206:INFO:Initializing create_model() 2024-04-21 19:33:13,206:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 19:33:13,206:INFO:Checking exceptions 2024-04-21 19:33:13,206:INFO:Importing libraries 2024-04-21 19:33:13,206:INFO:Copying training dataset 2024-04-21 19:33:13,207:INFO:Defining folds 2024-04-21 19:33:13,207:INFO:Declaring metric variables 2024-04-21 19:33:13,207:INFO:Importing untrained model 2024-04-21 19:33:13,207:INFO:Random Forest Classifier Imported successfully 2024-04-21 19:33:13,208:INFO:Starting cross validation 2024-04-21 19:33:13,208:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:13,448:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,450:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:13,451:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,451:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,453:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:13,455:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,455:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,455:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,455:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,458:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,458:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:13,459:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:13,459:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,459:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:13,461:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,461:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,462:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,463:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,463:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,463:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,463:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,463:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,463:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:13,468:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,468:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,468:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,469:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,471:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,471:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,473:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,473:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:13,474:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,474:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,474:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:13,476:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,476:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:13,477:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,477:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,477:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,477:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,477:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,477:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,477:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,482:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,482:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:13,482:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,484:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,486:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,489:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,503:INFO:Calculating mean and std 2024-04-21 19:33:13,504:INFO:Creating metrics dataframe 2024-04-21 19:33:13,506:INFO:Uploading results into container 2024-04-21 19:33:13,506:INFO:Uploading model into container now 2024-04-21 19:33:13,507:INFO:_master_model_container: 7 2024-04-21 19:33:13,507:INFO:_display_container: 2 2024-04-21 19:33:13,507: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=7944, verbose=0, warm_start=False) 2024-04-21 19:33:13,507:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:13,567:INFO:SubProcess create_model() end ================================== 2024-04-21 19:33:13,567:INFO:Creating metrics dataframe 2024-04-21 19:33:13,570:INFO:Initializing Quadratic Discriminant Analysis 2024-04-21 19:33:13,570:INFO:Total runtime is 0.14390093088150022 minutes 2024-04-21 19:33:13,570:INFO:SubProcess create_model() called ================================== 2024-04-21 19:33:13,570:INFO:Initializing create_model() 2024-04-21 19:33:13,570:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 19:33:13,570:INFO:Checking exceptions 2024-04-21 19:33:13,570:INFO:Importing libraries 2024-04-21 19:33:13,570:INFO:Copying training dataset 2024-04-21 19:33:13,572:INFO:Defining folds 2024-04-21 19:33:13,572:INFO:Declaring metric variables 2024-04-21 19:33:13,572:INFO:Importing untrained model 2024-04-21 19:33:13,572:INFO:Quadratic Discriminant Analysis Imported successfully 2024-04-21 19:33:13,572:INFO:Starting cross validation 2024-04-21 19:33:13,573:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:13,599:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,601:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,602:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:13,602:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,602:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:13,604:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,604:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,604:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:13,605:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( o this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,606:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:13,607:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,607:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,608:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,608:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:13,609:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,609:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,610:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:13,610:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) \Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:13,611:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,612:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,613:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:13,613:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,613:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,613:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,615:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,615:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,615:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:13,615:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,615:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,615:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,615:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,615:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,610:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,615:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,615:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,623:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,624:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,624:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,625:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,626:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,627:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,628:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,628:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:13,630:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,632:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,634:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,645:INFO:Calculating mean and std 2024-04-21 19:33:13,646:INFO:Creating metrics dataframe 2024-04-21 19:33:13,646:INFO:Uploading results into container 2024-04-21 19:33:13,646:INFO:Uploading model into container now 2024-04-21 19:33:13,649:INFO:_master_model_container: 8 2024-04-21 19:33:13,649:INFO:_display_container: 2 2024-04-21 19:33:13,650:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0, store_covariance=False, tol=0.0001) 2024-04-21 19:33:13,650:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:13,706:INFO:SubProcess create_model() end ================================== 2024-04-21 19:33:13,706:INFO:Creating metrics dataframe 2024-04-21 19:33:13,708:INFO:Initializing Ada Boost Classifier 2024-04-21 19:33:13,708:INFO:Total runtime is 0.14620018800099688 minutes 2024-04-21 19:33:13,708:INFO:SubProcess create_model() called ================================== 2024-04-21 19:33:13,710:INFO:Initializing create_model() 2024-04-21 19:33:13,710:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 19:33:13,710:INFO:Checking exceptions 2024-04-21 19:33:13,710:INFO:Importing libraries 2024-04-21 19:33:13,710:INFO:Copying training dataset 2024-04-21 19:33:13,711:INFO:Defining folds 2024-04-21 19:33:13,711:INFO:Declaring metric variables 2024-04-21 19:33:13,711:INFO:Importing untrained model 2024-04-21 19:33:13,711:INFO:Ada Boost Classifier Imported successfully 2024-04-21 19:33:13,712:INFO:Starting cross validation 2024-04-21 19:33:13,712:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:13,728:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 19:33:13,733:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 19:33:13,733:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 19:33:13,733:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 19:33:13,734:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 19:33:13,734:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 19:33:13,734:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 19:33:13,735:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 19:33:13,736:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 19:33:13,736:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning. warnings.warn( 2024-04-21 19:33:13,836:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,838:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,838:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,838:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:13,839:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,840:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:13,840:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:13,840:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,840:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,840:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:13,842:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,842:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:13,842:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,842:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,844:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,844:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,846:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,846:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,846:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,847:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,848:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,848:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,849:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,850:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,850:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:13,852:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,852:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,852:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,852:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,852:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,852:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:13,852:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:13,852:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,852:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,852:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,858:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,858:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,859:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:13,859:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:13,860:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,860:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,860:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,860:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:13,862:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,862:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,863:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,864:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,865:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,865:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:13,872:INFO:Calculating mean and std 2024-04-21 19:33:13,872:INFO:Creating metrics dataframe 2024-04-21 19:33:13,872:INFO:Uploading results into container 2024-04-21 19:33:13,872:INFO:Uploading model into container now 2024-04-21 19:33:13,872:INFO:_master_model_container: 9 2024-04-21 19:33:13,872:INFO:_display_container: 2 2024-04-21 19:33:13,872:INFO:AdaBoostClassifier(algorithm='SAMME.R', estimator=None, learning_rate=1.0, n_estimators=50, random_state=7944) 2024-04-21 19:33:13,872:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:13,934:INFO:SubProcess create_model() end ================================== 2024-04-21 19:33:13,934:INFO:Creating metrics dataframe 2024-04-21 19:33:13,934:INFO:Initializing Gradient Boosting Classifier 2024-04-21 19:33:13,934:INFO:Total runtime is 0.1499683062235514 minutes 2024-04-21 19:33:13,934:INFO:SubProcess create_model() called ================================== 2024-04-21 19:33:13,934:INFO:Initializing create_model() 2024-04-21 19:33:13,934:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 19:33:13,934:INFO:Checking exceptions 2024-04-21 19:33:13,934:INFO:Importing libraries 2024-04-21 19:33:13,934:INFO:Copying training dataset 2024-04-21 19:33:13,934:INFO:Defining folds 2024-04-21 19:33:13,934:INFO:Declaring metric variables 2024-04-21 19:33:13,934:INFO:Importing untrained model 2024-04-21 19:33:13,934:INFO:Gradient Boosting Classifier Imported successfully 2024-04-21 19:33:13,934:INFO:Starting cross validation 2024-04-21 19:33:13,934:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:14,201:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,203:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,204:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,207:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,209:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,211:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,212:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,212:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,212:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,215:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,215:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,215:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,215:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,215:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,219:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,220:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,222:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,224:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,226:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,226:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,227:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,227:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,227:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,227:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,227:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,227:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,227:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,227:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,227:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,233:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,233:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,233:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,233:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,236:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,242:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,243:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,243:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,245:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,245:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,247:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,247:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,249:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,251:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,253:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,253:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,253:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,257:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,261:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,261:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,278:INFO:Calculating mean and std 2024-04-21 19:33:14,278:INFO:Creating metrics dataframe 2024-04-21 19:33:14,281:INFO:Uploading results into container 2024-04-21 19:33:14,281:INFO:Uploading model into container now 2024-04-21 19:33:14,281:INFO:_master_model_container: 10 2024-04-21 19:33:14,281:INFO:_display_container: 2 2024-04-21 19:33:14,282: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=7944, subsample=1.0, tol=0.0001, validation_fraction=0.1, verbose=0, warm_start=False) 2024-04-21 19:33:14,282:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:14,337:INFO:SubProcess create_model() end ================================== 2024-04-21 19:33:14,337:INFO:Creating metrics dataframe 2024-04-21 19:33:14,344:INFO:Initializing Linear Discriminant Analysis 2024-04-21 19:33:14,344:INFO:Total runtime is 0.15679139296213782 minutes 2024-04-21 19:33:14,345:INFO:SubProcess create_model() called ================================== 2024-04-21 19:33:14,345:INFO:Initializing create_model() 2024-04-21 19:33:14,345:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 19:33:14,345:INFO:Checking exceptions 2024-04-21 19:33:14,345:INFO:Importing libraries 2024-04-21 19:33:14,345:INFO:Copying training dataset 2024-04-21 19:33:14,346:INFO:Defining folds 2024-04-21 19:33:14,346:INFO:Declaring metric variables 2024-04-21 19:33:14,346:INFO:Importing untrained model 2024-04-21 19:33:14,347:INFO:Linear Discriminant Analysis Imported successfully 2024-04-21 19:33:14,347:INFO:Starting cross validation 2024-04-21 19:33:14,347:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:14,379:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,379:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,379:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,379:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,379:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,379:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,379:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,379:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,379:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,379:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,379:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,379:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,384:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,384:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,384:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,385:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,385:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,385:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,386:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,386:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,386:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,386:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,387:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,387:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,387:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,387:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,388:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,388:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,388:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,388:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,389:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,389:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,389:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,390:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,390:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,390:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,390:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,391:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,391:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,391:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,392:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,393:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,393:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,393:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,394:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,395:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,395:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,405:INFO:Calculating mean and std 2024-04-21 19:33:14,406:INFO:Creating metrics dataframe 2024-04-21 19:33:14,408:INFO:Uploading results into container 2024-04-21 19:33:14,409:INFO:Uploading model into container now 2024-04-21 19:33:14,409:INFO:_master_model_container: 11 2024-04-21 19:33:14,409:INFO:_display_container: 2 2024-04-21 19:33:14,409:INFO:LinearDiscriminantAnalysis(covariance_estimator=None, n_components=None, priors=None, shrinkage=None, solver='svd', store_covariance=False, tol=0.0001) 2024-04-21 19:33:14,409:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:14,469:INFO:SubProcess create_model() end ================================== 2024-04-21 19:33:14,469:INFO:Creating metrics dataframe 2024-04-21 19:33:14,472:INFO:Initializing Extra Trees Classifier 2024-04-21 19:33:14,472:INFO:Total runtime is 0.15892998377482093 minutes 2024-04-21 19:33:14,472:INFO:SubProcess create_model() called ================================== 2024-04-21 19:33:14,472:INFO:Initializing create_model() 2024-04-21 19:33:14,472:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 19:33:14,472:INFO:Checking exceptions 2024-04-21 19:33:14,472:INFO:Importing libraries 2024-04-21 19:33:14,472:INFO:Copying training dataset 2024-04-21 19:33:14,474:INFO:Defining folds 2024-04-21 19:33:14,474:INFO:Declaring metric variables 2024-04-21 19:33:14,474:INFO:Importing untrained model 2024-04-21 19:33:14,474:INFO:Extra Trees Classifier Imported successfully 2024-04-21 19:33:14,474:INFO:Starting cross validation 2024-04-21 19:33:14,475:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:14,661:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,664:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,666:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,669:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,672:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,674:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,674:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,674:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,676:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,676:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,677:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,677:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,677:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,677:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,679:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,679:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,679:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,679:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,679:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,680:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,680:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,680:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,680:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,680:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,680:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,684:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,684:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,684:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,684:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,684:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,685:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,686:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,686:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,686:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,688:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,688:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,688:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,690:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,692:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,692:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,698:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,699:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,700:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,701:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,701:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,702:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,704:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,704:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,706:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,706:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,717:INFO:Calculating mean and std 2024-04-21 19:33:14,717:INFO:Creating metrics dataframe 2024-04-21 19:33:14,720:INFO:Uploading results into container 2024-04-21 19:33:14,720:INFO:Uploading model into container now 2024-04-21 19:33:14,721:INFO:_master_model_container: 12 2024-04-21 19:33:14,721:INFO:_display_container: 2 2024-04-21 19:33:14,721: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=7944, verbose=0, warm_start=False) 2024-04-21 19:33:14,721:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:14,777:INFO:SubProcess create_model() end ================================== 2024-04-21 19:33:14,777:INFO:Creating metrics dataframe 2024-04-21 19:33:14,777:INFO:Initializing Dummy Classifier 2024-04-21 19:33:14,777:INFO:Total runtime is 0.1640164494514465 minutes 2024-04-21 19:33:14,777:INFO:SubProcess create_model() called ================================== 2024-04-21 19:33:14,781:INFO:Initializing create_model() 2024-04-21 19:33:14,781:INFO:create_model(self=, 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) 2024-04-21 19:33:14,781:INFO:Checking exceptions 2024-04-21 19:33:14,781:INFO:Importing libraries 2024-04-21 19:33:14,781:INFO:Copying training dataset 2024-04-21 19:33:14,783:INFO:Defining folds 2024-04-21 19:33:14,783:INFO:Declaring metric variables 2024-04-21 19:33:14,783:INFO:Importing untrained model 2024-04-21 19:33:14,783:INFO:Dummy Classifier Imported successfully 2024-04-21 19:33:14,783:INFO:Starting cross validation 2024-04-21 19:33:14,784:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:14,797:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,799:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,799:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,800:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,801:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,801:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,802:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,802:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,803:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,803:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,804:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:14,805:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,806:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,806:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,807:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,807:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,807:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,807:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,807:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,807:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:14,809:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,809:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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)) metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,810:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,810:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,810:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,810:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,810:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:14,810:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,810:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,810:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,810:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,810:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:14,816:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,816:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,817:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,817:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:14,817:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,817:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:14,817:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,817:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:14,817:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,817:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,817:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,819:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,819:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,819:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:14,819:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,819:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:14,819:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,819:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,823:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,823:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,823:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:14,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:14,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1509: 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 19:33:14,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,824:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:14,840:INFO:Calculating mean and std 2024-04-21 19:33:14,840:INFO:Creating metrics dataframe 2024-04-21 19:33:14,840:INFO:Uploading results into container 2024-04-21 19:33:14,844:INFO:Uploading model into container now 2024-04-21 19:33:14,844:INFO:_master_model_container: 13 2024-04-21 19:33:14,844:INFO:_display_container: 2 2024-04-21 19:33:14,844:INFO:DummyClassifier(constant=None, random_state=7944, strategy='prior') 2024-04-21 19:33:14,844:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:14,905:INFO:SubProcess create_model() end ================================== 2024-04-21 19:33:14,905:INFO:Creating metrics dataframe 2024-04-21 19:33:14,910:INFO:Initializing create_model() 2024-04-21 19:33:14,924:INFO:create_model(self=, estimator=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=7944, verbose=0, warm_start=False), 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={}) 2024-04-21 19:33:14,924:INFO:Checking exceptions 2024-04-21 19:33:14,925:INFO:Importing libraries 2024-04-21 19:33:14,925:INFO:Copying training dataset 2024-04-21 19:33:14,926:INFO:Defining folds 2024-04-21 19:33:14,926:INFO:Declaring metric variables 2024-04-21 19:33:14,926:INFO:Importing untrained model 2024-04-21 19:33:14,926:INFO:Declaring custom model 2024-04-21 19:33:14,927:INFO:Random Forest Classifier Imported successfully 2024-04-21 19:33:14,927:INFO:Cross validation set to False 2024-04-21 19:33:14,927:INFO:Fitting Model 2024-04-21 19:33:15,032: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=7944, verbose=0, warm_start=False) 2024-04-21 19:33:15,032:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:15,104:INFO:_master_model_container: 13 2024-04-21 19:33:15,105:INFO:_display_container: 2 2024-04-21 19:33:15,105: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=7944, verbose=0, warm_start=False) 2024-04-21 19:33:15,105:INFO:compare_models() successfully completed...................................... 2024-04-21 19:33:15,105:INFO:Initializing create_model() 2024-04-21 19:33:15,105:INFO:create_model(self=, estimator=rf, 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={}) 2024-04-21 19:33:15,105:INFO:Checking exceptions 2024-04-21 19:33:15,106:INFO:Importing libraries 2024-04-21 19:33:15,106:INFO:Copying training dataset 2024-04-21 19:33:15,107:INFO:Defining folds 2024-04-21 19:33:15,109:INFO:Declaring metric variables 2024-04-21 19:33:15,109:INFO:Importing untrained model 2024-04-21 19:33:15,109:INFO:Random Forest Classifier Imported successfully 2024-04-21 19:33:15,109:INFO:Starting cross validation 2024-04-21 19:33:15,109:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:15,367:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:15,369:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:15,369:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:15,372:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,372:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:15,372:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:15,372:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,372:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:15,375:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,375:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:15,375:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,376:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,376:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,376:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:15,376:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,378:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,378:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,379:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:15,379:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,381:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,381:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:15,381:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:15,383:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,383:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:15,383:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,383:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:15,385:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:15,385:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:15,385:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:15,386:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,387:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:15,387:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,387:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,387:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:15,388:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,389:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,389:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,389:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,390:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,390:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,392:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,393:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,393:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,393:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,393:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:15,395:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:15,395:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,395:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,399:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,401:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:15,406:INFO:Calculating mean and std 2024-04-21 19:33:15,406:INFO:Creating metrics dataframe 2024-04-21 19:33:15,408:INFO:Finalizing model 2024-04-21 19:33:15,511:INFO:Uploading results into container 2024-04-21 19:33:15,511:INFO:Uploading model into container now 2024-04-21 19:33:15,517:INFO:_master_model_container: 14 2024-04-21 19:33:15,517:INFO:_display_container: 3 2024-04-21 19:33:15,517: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=7944, verbose=0, warm_start=False) 2024-04-21 19:33:15,517:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:15,585:INFO:Initializing tune_model() 2024-04-21 19:33:15,585:INFO:tune_model(self=, estimator=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=7944, verbose=0, warm_start=False), 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={}) 2024-04-21 19:33:15,585:INFO:Checking exceptions 2024-04-21 19:33:15,587:INFO:Copying training dataset 2024-04-21 19:33:15,588:INFO:Checking base model 2024-04-21 19:33:15,588:INFO:Base model : Random Forest Classifier 2024-04-21 19:33:15,589:INFO:Declaring metric variables 2024-04-21 19:33:15,589:INFO:Defining Hyperparameters 2024-04-21 19:33:15,649:INFO:Tuning with n_jobs=-1 2024-04-21 19:33:15,649:INFO:Initializing RandomizedSearchCV 2024-04-21 19:33:18,216:INFO:best_params: {'actual_estimator__n_estimators': 30, 'actual_estimator__min_samples_split': 9, 'actual_estimator__min_samples_leaf': 2, 'actual_estimator__min_impurity_decrease': 0.0001, 'actual_estimator__max_features': 'log2', 'actual_estimator__max_depth': 9, 'actual_estimator__criterion': 'entropy', 'actual_estimator__class_weight': 'balanced_subsample', 'actual_estimator__bootstrap': False} 2024-04-21 19:33:18,216:INFO:Hyperparameter search completed 2024-04-21 19:33:18,216:INFO:SubProcess create_model() called ================================== 2024-04-21 19:33:18,216:INFO:Initializing create_model() 2024-04-21 19:33:18,216:INFO:create_model(self=, estimator=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=7944, verbose=0, warm_start=False), 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=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'n_estimators': 30, 'min_samples_split': 9, 'min_samples_leaf': 2, 'min_impurity_decrease': 0.0001, 'max_features': 'log2', 'max_depth': 9, 'criterion': 'entropy', 'class_weight': 'balanced_subsample', 'bootstrap': False}) 2024-04-21 19:33:18,218:INFO:Checking exceptions 2024-04-21 19:33:18,218:INFO:Importing libraries 2024-04-21 19:33:18,218:INFO:Copying training dataset 2024-04-21 19:33:18,219:INFO:Defining folds 2024-04-21 19:33:18,220:INFO:Declaring metric variables 2024-04-21 19:33:18,220:INFO:Importing untrained model 2024-04-21 19:33:18,220:INFO:Declaring custom model 2024-04-21 19:33:18,220:INFO:Random Forest Classifier Imported successfully 2024-04-21 19:33:18,220:INFO:Starting cross validation 2024-04-21 19:33:18,221:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:18,316:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,319:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:18,323:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,323:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,323:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,323:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,323:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,323:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:18,328:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:18,329:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,329:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,329:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,330:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,331:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,331:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,332:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,332:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,332:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:18,333:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:18,334:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,334:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,335:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,335:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,336:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,336:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,336:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,337:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:18,337:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,338:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,338:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,338:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,338:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:18,340:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:18,340:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,341:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,341:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,341:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,341:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,343:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,343:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,344:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,345:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,345:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:18,346:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,347:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,348:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,348:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,350:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,351:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,360:INFO:Calculating mean and std 2024-04-21 19:33:18,360:INFO:Creating metrics dataframe 2024-04-21 19:33:18,360:INFO:Finalizing model 2024-04-21 19:33:18,401:INFO:Uploading results into container 2024-04-21 19:33:18,402:INFO:Uploading model into container now 2024-04-21 19:33:18,402:INFO:_master_model_container: 15 2024-04-21 19:33:18,402:INFO:_display_container: 4 2024-04-21 19:33:18,403:INFO:RandomForestClassifier(bootstrap=False, ccp_alpha=0.0, class_weight='balanced_subsample', criterion='entropy', max_depth=9, max_features='log2', max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0.0001, min_samples_leaf=2, min_samples_split=9, min_weight_fraction_leaf=0.0, monotonic_cst=None, n_estimators=30, n_jobs=-1, oob_score=False, random_state=7944, verbose=0, warm_start=False) 2024-04-21 19:33:18,403:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:18,461:INFO:SubProcess create_model() end ================================== 2024-04-21 19:33:18,461:INFO:choose_better activated 2024-04-21 19:33:18,461:INFO:SubProcess create_model() called ================================== 2024-04-21 19:33:18,461:INFO:Initializing create_model() 2024-04-21 19:33:18,461:INFO:create_model(self=, estimator=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=7944, verbose=0, warm_start=False), 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={}) 2024-04-21 19:33:18,461:INFO:Checking exceptions 2024-04-21 19:33:18,461:INFO:Importing libraries 2024-04-21 19:33:18,461:INFO:Copying training dataset 2024-04-21 19:33:18,461:INFO:Defining folds 2024-04-21 19:33:18,461:INFO:Declaring metric variables 2024-04-21 19:33:18,461:INFO:Importing untrained model 2024-04-21 19:33:18,461:INFO:Declaring custom model 2024-04-21 19:33:18,461:INFO:Random Forest Classifier Imported successfully 2024-04-21 19:33:18,461:INFO:Starting cross validation 2024-04-21 19:33:18,461:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 2024-04-21 19:33:18,710:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,713:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:18,714:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,714:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,718:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,726:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) \Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:18,729:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,734:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:18,734:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:18,734:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,734:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:18,734:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,734:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,737:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,737:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,737:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,737:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,739:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,739:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,740:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,740:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,742:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,742:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 11) warnings.warn( 2024-04-21 19:33:18,742:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,744:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,744:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:18,745:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,745:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,745:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,746:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,746:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:18,747:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:18,747:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,748:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,749:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,749:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,750:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,750:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,751:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,753:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,753:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,756:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,760:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py:568: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. values = np.array([convert(v) for v in values]) 2024-04-21 19:33:18,760:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py:196: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method='predict_proba', average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below: Traceback (most recent call last): File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\metrics.py", line 188, in _score return super()._score( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 345, in _score y_pred = method_caller( ^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_scorer.py", line 87, in _cached_call result, _ = _get_response_values( ^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_response.py", line 210, in _get_response_values y_pred = prediction_method(X) ^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\pipeline.py", line 341, in predict_proba Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\utils\_set_output.py", line 295, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\internal\preprocess\transformers.py", line 233, in transform X = to_df(X, index=getattr(y, "index", None)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pycaret\utils\generic.py", line 103, in to_df data = pd.DataFrame(data, index, columns) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\frame.py", line 762, in __init__ mgr = ndarray_to_mgr( ^^^^^^^^^^^^^^^ File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 349, in ndarray_to_mgr _check_values_indices_shape_match(values, index, columns) File "c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\pandas\core\internals\construction.py", line 420, in _check_values_indices_shape_match raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (2, 1), indices imply (2, 10) warnings.warn( 2024-04-21 19:33:18,760:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,765:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,768:WARNING:c:\Users\arpit\anaconda3\envs\arpit-test\Lib\site-packages\sklearn\metrics\_classification.py:1561: UserWarning: Note that pos_label (set to 'Iris-virginica') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class. warnings.warn( 2024-04-21 19:33:18,776:INFO:Calculating mean and std 2024-04-21 19:33:18,776:INFO:Creating metrics dataframe 2024-04-21 19:33:18,776:INFO:Finalizing model 2024-04-21 19:33:18,874:INFO:Uploading results into container 2024-04-21 19:33:18,874:INFO:Uploading model into container now 2024-04-21 19:33:18,874:INFO:_master_model_container: 16 2024-04-21 19:33:18,874:INFO:_display_container: 5 2024-04-21 19:33:18,874: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=7944, verbose=0, warm_start=False) 2024-04-21 19:33:18,874:INFO:create_model() successfully completed...................................... 2024-04-21 19:33:18,927:INFO:SubProcess create_model() end ================================== 2024-04-21 19:33:18,927: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=7944, verbose=0, warm_start=False) result for Accuracy is 1.0 2024-04-21 19:33:18,934:INFO:RandomForestClassifier(bootstrap=False, ccp_alpha=0.0, class_weight='balanced_subsample', criterion='entropy', max_depth=9, max_features='log2', max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0.0001, min_samples_leaf=2, min_samples_split=9, min_weight_fraction_leaf=0.0, monotonic_cst=None, n_estimators=30, n_jobs=-1, oob_score=False, random_state=7944, verbose=0, warm_start=False) result for Accuracy is 0.99 2024-04-21 19:33:18,934: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=7944, verbose=0, warm_start=False) is best model 2024-04-21 19:33:18,934:INFO:choose_better completed 2024-04-21 19:33:18,934: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 19:33:18,940:INFO:_master_model_container: 16 2024-04-21 19:33:18,941:INFO:_display_container: 4 2024-04-21 19:33:18,941: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=7944, verbose=0, warm_start=False) 2024-04-21 19:33:18,941:INFO:tune_model() successfully completed...................................... 2024-04-21 19:33:19,003:INFO:Initializing evaluate_model() 2024-04-21 19:33:19,003:INFO:evaluate_model(self=, estimator=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=7944, verbose=0, warm_start=False), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) 2024-04-21 19:33:19,244:INFO:Initializing plot_model() 2024-04-21 19:33:19,244:INFO:plot_model(self=, estimator=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=7944, verbose=0, warm_start=False), plot=pipeline, scale=1, save=False, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) 2024-04-21 19:33:19,244:INFO:Checking exceptions 2024-04-21 19:33:19,260:INFO:Preloading libraries 2024-04-21 19:33:19,260:INFO:Copying training dataset 2024-04-21 19:33:19,260:INFO:Plot type: pipeline 2024-04-21 19:33:23,490:INFO:Visual Rendered Successfully 2024-04-21 19:33:23,564:INFO:plot_model() successfully completed......................................