KdW@dZddlZddlmZddlmZmZ d dZd dZ dS) zwUtilities to get the response values of a classifier or a regressor. It allows to make uniform checks and validation. N is_classifier)_check_response_methodcheck_is_fittedcddlm}||rt||}|j}t |dkrdnd}|+||vrt d|d|||dkr ||n|d }||}|jd krn|dkrg|jd dkrV|jd dkr+tj ||kd} |dd| f}n}d |jd } t | nb|jdkr|dkr||dkr|d z}n?|dkr"t |j jd|d|d| |d}}||fS)aCompute the response values of a classifier or a regressor. The response values are predictions, one scalar value for each sample in X that depends on the specific choice of `response_method`. If `estimator` is a binary classifier, also return the label for the effective positive class. .. versionadded:: 1.3 Parameters ---------- estimator : estimator instance Fitted classifier or regressor or a fitted :class:`~sklearn.pipeline.Pipeline` in which the last estimator is a classifier or a regressor. X : {array-like, sparse matrix} of shape (n_samples, n_features) Input values. response_method : {"predict_proba", "decision_function", "predict"} or list of such str Specifies the response method to use get prediction from an estimator (i.e. :term:`predict_proba`, :term:`decision_function` or :term:`predict`). Possible choices are: - if `str`, it corresponds to the name to the method to return; - if a list of `str`, it provides the method names in order of preference. The method returned corresponds to the first method in the list and which is implemented by `estimator`. pos_label : int, float, bool or str, default=None The class considered as the positive class when computing the metrics. By default, `estimators.classes_[1]` is considered as the positive class. Returns ------- y_pred : ndarray of shape (n_samples,) Target scores calculated from the provided response_method and `pos_label`. pos_label : int, float, bool, str or None The class considered as the positive class when computing the metrics. Returns `None` if `estimator` is a regressor. Raises ------ ValueError If `pos_label` is not a valid label. If the shape of `y_pred` is not consistent for binary classifier. If the response method can be applied to a classifier only and `estimator` is a regressor. rrrbinary multiclassNz pos_label=z+ is not a valid label: It should be one of predict_probarzGot predict_proba of shape z', but need classifier with two classes.decision_functionpredictz? should either be a classifier to be used with response_method=zR or the response_method should be 'predict'. Got a regressor with response_method= instead.) sklearn.baserrclasses_lentolist ValueError__name__shapenp flatnonzero __class__r) estimatorXresponse_method pos_labelrprediction_methodclasses target_typey_predcol_idxerr_msgs 7lib/python3.11/site-packages/sklearn/utils/_response.py_get_response_valuesr& sv+*****}Y%729oNN$"%g,,!"3"3hh  Ygnn6F6F%F%F$Y$$!$$  ;(#:#:%.%:  I""1%%  % 8 8h&&6<?a+?+?<?a'' nW -ABB1EG#AAAwJ/FF7fl777%W---  '+> > >h&& **bLF i ' '&/..-<..#...  &--a00$  9 c<d}t|t|s t|d|jjdzt |jdkr(t|dt |jdz|dkrddg}t|||| S) aCompute the response values of a binary classifier. Parameters ---------- estimator : estimator instance Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline` in which the last estimator is a binary classifier. X : {array-like, sparse matrix} of shape (n_samples, n_features) Input values. response_method : {'auto', 'predict_proba', 'decision_function'} Specifies whether to use :term:`predict_proba` or :term:`decision_function` as the target response. If set to 'auto', :term:`predict_proba` is tried first and if it does not exist :term:`decision_function` is tried next. pos_label : int, float, bool or str, default=None The class considered as the positive class when computing the metrics. By default, `estimators.classes_[1]` is considered as the positive class. Returns ------- y_pred : ndarray of shape (n_samples,) Target scores calculated from the provided response_method and pos_label. pos_label : int, float, bool or str The class considered as the positive class when computing the metrics. z/Expected 'estimator' to be a binary classifier.z Got rrz classes instead.autor r)r)rrrrrrrr&)rrrrclassification_errors r%_get_response_values_binaryr+rsBMI  # #  #R9+>+G#R#R#R R    Y  A % % #U3y/A+B+B#U#U#U U   &  *,?@      r')N) __doc__numpyrbaser validationrrr&r+r'r%r1s ???????? ddddN555555r'