rtj,\dZddlZddlmZddlmZddlmZm Z dZ dZ d d Z d d Z dS) zwUtilities to get the response values of a classifier or a regressor. It allows to make uniform checks and validation. N) is_classifier)type_of_target)_check_response_methodcheck_is_fittedcD|dkr)|jddkrtd|jd|dkr*tj||kd}|dd|fS|dkr:t |t r#tjd |DjS|S|S) aGet the response values when the response method is `predict_proba`. This function process the `y_pred` array in the binary and multi-label cases. In the binary case, it selects the column corresponding to the positive class. In the multi-label case, it stacks the predictions if they are not in the "compressed" format `(n_samples, n_outputs)`. Parameters ---------- y_pred : ndarray Output of `estimator.predict_proba`. The shape depends on the target type: - for binary classification, it is a 2d array of shape `(n_samples, 2)`; - for multiclass classification, it is a 2d array of shape `(n_samples, n_classes)`; - for multilabel classification, it is either a list of 2d arrays of shape `(n_samples, 2)` (e.g. `RandomForestClassifier` or `KNeighborsClassifier`) or an array of shape `(n_samples, n_outputs)` (e.g. `MLPClassifier` or `RidgeClassifier`). target_type : {"binary", "multiclass", "multilabel-indicator"} Type of the target. classes : ndarray of shape (n_classes,) or list of such arrays Class labels as reported by `estimator.classes_`. pos_label : int, float, bool or str Only used with binary targets. Returns ------- y_pred : ndarray of shape (n_samples,), (n_samples, n_classes) or (n_samples, n_output) Compressed predictions format as requested by the metrics. binaryzGot predict_proba of shape z', but need classifier with two classes.rNzmultilabel-indicatorc&g|]}|dddfS)N).0ps U/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/sklearn/utils/_response.py z*_process_predict_proba..Ds$7771a2h777)shape ValueErrornp flatnonzero isinstancelistvstackT)y_pred target_typeclasses pos_labelcol_idxs r_process_predict_probar sHh6<?Q#6#6 +&, + + +   h.I!566q9aaaj!! . . . fd # # 97777788: :M Mrc4|dkr||dkrd|zS|S)aGet the response values when the response method is `decision_function`. This function process the `y_pred` array in the binary and multi-label cases. In the binary case, it inverts the sign of the score if the positive label is not `classes[1]`. In the multi-label case, it stacks the predictions if the positive label is not `classes[1]`. `y_pred` is returned unchanged if `target_type` is "multiclass" or "multilabel-indicator". Parameters ---------- y_pred : ndarray Output of `estimator.decision_function`. The shape depends on the target type: - for binary classification, it is a 1d array of shape `(n_samples,)` where the sign is assuming that `classes[1]` is the positive class; - for multiclass classification, it is a 2d array of shape `(n_samples, n_classes)`; - for multilabel classification, it is a 2d array of shape `(n_samples, n_outputs)`. target_type : {"binary", "multiclass", "multilabel-indicator"} Type of the target. classes : ndarray of shape (n_classes,) or list of such arrays Class labels as reported by `estimator.classes_`. pos_label : int, float, bool or str Only used with binary targets. Returns ------- y_pred : ndarray of shape (n_samples,), (n_samples, n_classes) or (n_samples, n_output) Compressed predictions format as requested by the metrics. rrr r rrrrs r_process_decision_functionr#Ls-Hh9 #:#:F{ MrFct||}t|r|j}t|}|dkr7|+||vrt d|d|||d}||}|jdvrt||||}n,|jdkrt||||}n ||d}}|r |||jfS||fS) a& Compute the response values of a classifier, an outlier detector, a regressor or a clusterer. The response values are predictions such that it follows the following shape: - for binary classification, it is a 1d array of shape `(n_samples,)`; - for multiclass classification - with response_method="predict", it is a 1d array of shape `(n_samples,)`; - otherwise, it is a 2d array of shape `(n_samples, n_classes)`; - for multilabel classification, it is a 2d array of shape `(n_samples, n_outputs)`; - for outlier detection, a regressor or a clusterer, it is a 1d array of shape `(n_samples,)`. If `estimator` is a binary classifier, also return the label for the effective positive class. This utility is used primarily in the displays and the scikit-learn scorers. .. versionadded:: 1.3 Parameters ---------- estimator : estimator instance Fitted classifier, outlier detector, regressor, clusterer or a fitted :class:`~sklearn.pipeline.Pipeline` in which the last estimator is a classifier, an outlier detector, a regressor or a clusterer. X : {array-like, sparse matrix} of shape (n_samples, n_features) Input values. response_method : {"predict_proba", "predict_log_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:`predict_log_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 response values. If `None` and target is 'binary', `estimators.classes_[1]` is considered as the positive class. return_response_method_used : bool, default=False Whether to return the response method used to compute the response values. .. versionadded:: 1.4 Returns ------- y_pred : ndarray of shape (n_samples,), (n_samples, n_classes) or (n_samples, n_outputs) 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 binary response values. Returns `None` if `estimator` is a regressor, an outlier detector or a clusterer. response_method_used : str The response method used to compute the response values. Only returned if `return_response_method_used` is `True`. .. versionadded:: 1.4 Raises ------ ValueError If `pos_label` is not a valid label. If the shape of `y_pred` is not consistent for binary classifier. rNz pos_label=z+ is not a valid label: It should be one of r ) predict_probapredict_log_probar"decision_function) rrclasses_rtolistr__name__r r#) estimatorXresponse_methodrreturn_response_method_usedprediction_methodrrrs r_get_response_valuesr0usMf/y/JJY7$$W-- ( " "$'..:J:J)J)J (((%(("#BK ""1%%  %)O O O+'# FF  '+> > >/'# F.-a00$ "=y"3"<<< 9 rc>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. return_response_method_used : bool, default=False Whether to return the response method used to compute the response values. .. versionadded:: 1.5 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. response_method_used : str The response method used to compute the response values. Only returned if `return_response_method_used` is `True`. .. versionadded:: 1.5 z/Expected 'estimator' to be a binary classifier.z Got z instead.r z classes instead.autor%r')rr.)rrr __class__r*lenr(r0)r+r,r-rr.classification_errors r_get_response_values_binaryr6s^MI  # #  #R9+>+G#R#R#R R    Y  A % % #U3y/A+B+B#U#U#U U   &  *,?@  $?    r)NF)__doc__numpyr sklearn.basersklearn.utils.multiclassrsklearn.utils.validationrrr r#r0r6r rrr<s&&&&&&333333LLLLLLLL999x&&&Z % wwwwvPUDDDDDDr