o Ó\iKã @sddlZddlmZddlmZmZmZmZmZddl m Z m Z eeegdgddddddddœ eeggd ¢dd d gdddd œgZ ej ejejejejgd d dgdddddd œej ejejejejgd gdddddd d ¡¡d œgZej de ¡e dd„ƒƒZej de¡e dd„ƒƒZdS)éN)Úmetrics)ÚBaggingClassifierÚBaggingRegressorÚIsolationForestÚStackingClassifierÚStackingRegressor)Úassert_docstring_consistencyÚskip_if_no_numpydocÚ max_samplesFz4The number of samples to draw from X to train each.*) ÚobjectsÚinclude_paramsÚexclude_paramsÚ include_attrsÚ exclude_attrsÚinclude_returnsÚexclude_returnsÚdescr_regex_patternÚ ignore_types)ÚcvÚn_jobsÚ passthroughÚverboseTÚfinal_estimator_)r r r rrrrrÚaverageÚ zero_divisionú a/This parameter is required for multiclass/multilabel targets\. If ``None``, the metrics for each class are returned\. Otherwise, this determines the type of averaging performed on the data: ``'binary'``: Only report results for the class specified by ``pos_label``\. This is applicable only if targets \(``y_\{true,pred\}``\) are binary\. ``'micro'``: Calculate metrics globally by counting the total true positives, false negatives and false positives\. ``'macro'``: Calculate metrics for each label, and find their unweighted mean\. This does not take label imbalance into account\. ``'weighted'``: Calculate metrics for each label, and find their average weighted by support \(the number of true instances for each label\)\. This alters 'macro' to account for label imbalance; it can result in an F-score that is not between precision and recall\.[\s\w]*\.* ``'samples'``: Calculate metrics for each instance, and find their average \(only meaningful for multilabel classification where this differs from :func:`accuracy_score`\)\.ÚcasecCótdi|¤ŽdS)z@Check docstrings parameters consistency between related classes.N©©r©rrrú”/var/www/www-root/data/www/176.119.141.140/sports-predictor/venv/lib/python3.10/site-packages/sklearn/tests/test_docstring_parameters_consistency.pyÚ test_class_docstring_consistencyfór"cCr)zBCheck docstrings parameters consistency between related functions.Nrrr rrr!Ú#test_function_docstring_consistencymr#r$)ÚpytestÚsklearnrÚsklearn.ensemblerrrrrÚsklearn.utils._testingrr Ú!CLASS_DOCSTRING_CONSISTENCY_CASESÚprecision_recall_fscore_supportÚf1_scoreÚ fbeta_scoreÚprecision_scoreÚ recall_scoreÚjoinÚsplitÚ$FUNCTION_DOCSTRING_CONSISTENCY_CASESÚmarkÚ parametrizer"r$rrrr!Ús| ÷ øôûòûçòï >