h4)XdZddlZddlmZddlmZddlmZm Z dZ d Z 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 and multiclass 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. binaryrrzGot predict_proba of shape z', but need classifier with two classes.rNzmultilabel-indicatorc&g|]}|dddfS)N).0ps _/var/www/html/services/rag-service/venv/lib/python3.11/site-packages/sklearn/utils/_response.py z*_process_predict_proba..@s$7771a2h777)shape ValueErrornp flatnonzero isinstancelistvstackT)y_pred target_typeclasses pos_labelcol_idxs r_process_predict_probar! sHh6<?Q#6#6 +&, + + +   h.I!566q9aaaj!! . . . fd # # 97777788: :M Mrc4|dkr||dkrd|zS|S)awGet the response values when the response method is `decision_function`. This function process the `y_pred` array in the binary and multi-label cases. In the binary case, it inverts the sign of the score if the positive label is not `classes[1]`. In the multi-label case, it stacks the predictions if they are not in the "compressed" format `(n_samples, n_outputs)`. Parameters ---------- y_pred : ndarray Output of `estimator.predict_proba`. The shape depends on the target type: - for binary classification, it is a 1d array of shape `(n_samples,)` where the sign is assuming that `classes[1]` is the positive class; - for multiclass classification, it is a 2d array of shape `(n_samples, n_classes)`; - for multilabel classification, it is a 2d array of shape `(n_samples, n_outputs)`. target_type : {"binary", "multiclass", "multilabel-indicator"} Type of the target. classes : ndarray of shape (n_classes,) or list of such arrays Class labels as reported by `estimator.classes_`. pos_label : int, float, bool or str Only used with binary and multiclass targets. Returns ------- y_pred : ndarray of shape (n_samples,), (n_samples, n_classes) or (n_samples, n_output) Compressed predictions format as requested by the metrics. r rr rrrrrs r_process_decision_functionr$Hs-Fh9 #:#:F{ Mrc ddlm}||rt||}|j}t |}|dvr=|+||vrt d|d|||dkr|d}||}|jd krt|||| }n^|jd krt|||| }n?|d kr"t |j jd |d|d| |d}}||fS)a Compute the response values of a classifier or a regressor. 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, 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 regression, 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 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,), (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 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. rr)r multiclassNz pos_label=z+ is not a valid label: It should be one of r r predict_probar#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_rtolistr__name__r!r$ __class__r)) estimatorXresponse_methodrrprediction_methodrrrs r_get_response_valuesr4psD+*****}Y&729oNN$$W-- 2 2 2$'..:J:J)J)J (((%(("{h'>'>#BK ""1%%  % 8 8+'# FF  '+> > >/'# F i ' '&/..-<..#...  &--a00$  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. 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 r*rz classes instead.autor'r()r)r rrr/r.lenr,r4)r0r1r2rclassification_errors r_get_response_values_binaryr9sBMI  # #  #R9+>+G#R#R#R R    Y  A % % #U3y/A+B+B#U#U#U U   &  *,?@      r)N)__doc__numpyrbaserr&r validationrr r!r$r4r9rrrr>s &&&&&&????????999x%%%X llll^555555r