]ju/^SrSSKrSSKJr SSKJr SSKJrJ r Sr Sr S Sjr S S jr g) 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_fittedcpUS:Xa,URSS:a[SURS35eUS:Xa$[R"X#:H5SnUSS2U4$US:XaN[ U[ 5(a7[R "UVs/sH oUSS2S 4PM sn5R$U$U$s snf) a_Get the response values when the response method is `predict_proba`. This function process the `y_pred` array in the binary and multi-label cases. In the binary case, it selects the column corresponding to the positive class. In the multi-label case, it stacks the predictions if they are not in the "compressed" format `(n_samples, n_outputs)`. Parameters ---------- y_pred : ndarray Output of `estimator.predict_proba`. The shape depends on the target type: - for binary classification, it is a 2d array of shape `(n_samples, 2)`; - for multiclass classification, it is a 2d array of shape `(n_samples, n_classes)`; - for multilabel classification, it is either a list of 2d arrays of shape `(n_samples, 2)` (e.g. `RandomForestClassifier` or `KNeighborsClassifier`) or an array of shape `(n_samples, n_outputs)` (e.g. `MLPClassifier` or `RidgeClassifier`). target_type : {"binary", "multiclass", "multilabel-indicator"} Type of the target. classes : ndarray of shape (n_classes,) or list of such arrays Class labels as reported by `estimator.classes_`. pos_label : int, float, bool or str Only used with binary and multiclass targets. Returns ------- y_pred : ndarray of shape (n_samples,), (n_samples, n_classes) or (n_samples, n_output) Compressed predictions format as requested by the metrics. binaryzGot predict_proba of shape z', but need classifier with two classes.rNzmultilabel-indicator)shape ValueErrornp flatnonzero isinstancelistvstackT)y_pred target_typeclasses pos_labelcol_idxps T/home/mande/repo/quber/.venv/lib/python3.13/site-packages/sklearn/utils/_response.py_process_predict_probarsHh6<<?Q#6)&,,8+ +  h..!56q9aj!! . . fd # #99712h78:: :M M 8s B3c,US:Xa X2S:XaSU-$U$)aGet the response values when the response method is `decision_function`. This function process the `y_pred` array in the binary and multi-label cases. In the binary case, it inverts the sign of the score if the positive label is not `classes[1]`. In the multi-label case, it stacks the predictions if they are not in the "compressed" format `(n_samples, n_outputs)`. Parameters ---------- y_pred : ndarray Output of `estimator.decision_function`. The shape depends on the target type: - for binary classification, it is a 1d array of shape `(n_samples,)` where the sign is assuming that `classes[1]` is the positive class; - for multiclass classification, it is a 2d array of shape `(n_samples, n_classes)`; - for multilabel classification, it is a 2d array of shape `(n_samples, n_outputs)`. target_type : {"binary", "multiclass", "multilabel-indicator"} Type of the target. classes : ndarray of shape (n_classes,) or list of such arrays Class labels as reported by `estimator.classes_`. pos_label : int, float, bool or str Only used with binary and multiclass targets. Returns ------- y_pred : ndarray of shape (n_samples,), (n_samples, n_classes) or (n_samples, n_output) Compressed predictions format as requested by the metrics. rrr rrrrs r_process_decision_functionrLs%Fh9 #:F{ McdSSKJnJn U"U5(a[X5nURn[ U5n U S;a5Ub$X8R 5;a[SUSU35eUc U S:XaUSnU"U5n URS ;a [U U UUS 9n OURS :Xa [U U UUS 9n OeU"U5(a[X5nU"U5Sp:OCUS :wa([URRS USUS35eURnU"U5Sp:U(aXUR4$X4$)a' Compute the response values of a classifier, an outlier detector, 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 outlier detection, it is a 1d array of shape `(n_samples,)`; - 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, outlier detector, or regressor or a fitted :class:`~sklearn.pipeline.Pipeline` in which the last estimator is a classifier, an outlier detector, or a regressor. 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 metrics. 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 the metrics. Returns `None` if `estimator` is a regressor or an outlier detector. 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. If the response method can be applied to a classifier only and `estimator` is a regressor. r)ris_outlier_detector)r multiclassNz pos_label=z+ is not a valid label: It should be one of rr ) predict_probapredict_log_probardecision_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.baserr"rclasses_rtolistr __name__rr __class__r') estimatorXresponse_methodrreturn_response_method_usedrr"prediction_methodrrrs r_get_response_valuesr3tsd@Y29N$$$W- 2 2$..:J)J   ,%Y("{h'>#BK "1%  % %)O O+'# F  ' '+> >/'# F Y ' '29N-a0$ i '&&//01--<,=>M"#9.  &---a0$ ""3"<"<<<  r c2Sn[U5 [U5(d&[USURRS3-5e[ UR 5S:wa%[US[ UR 5S3-5eUS:XaSS/n[UUUUUS 9$) aCompute the response values of a binary classifier. Parameters ---------- estimator : estimator instance Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline` in which the last estimator is a binary classifier. X : {array-like, sparse matrix} of shape (n_samples, n_features) Input values. response_method : {'auto', 'predict_proba', 'decision_function'} Specifies whether to use :term:`predict_proba` or :term:`decision_function` as the target response. If set to 'auto', :term:`predict_proba` is tried first and if it does not exist :term:`decision_function` is tried next. pos_label : int, float, bool or str, default=None The class considered as the positive class when computing the metrics. By default, `estimators.classes_[1]` is considered as the positive class. return_response_method_used : bool, default=False Whether to return the response method used to compute the response values. .. versionadded:: 1.5 Returns ------- y_pred : ndarray of shape (n_samples,) Target scores calculated from the provided response_method and pos_label. pos_label : int, float, bool or str The class considered as the positive class when computing the metrics. response_method_used : str The response method used to compute the response values. Only returned if `return_response_method_used` is `True`. .. versionadded:: 1.5 z/Expected 'estimator' to be a binary classifier.z Got r(r z classes instead.autor$r&)rr1)rrr r-r,lenr*r3)r.r/r0rr1classification_errors r_get_response_values_binaryr8s^MI  # # U9+>+>+G+G*H #R R   Y   A % U3y/A/A+B*CCT#U U  & *,?@  $?  r )NF)__doc__numpyrr)rsklearn.utils.multiclassrsklearn.utils.validationrrrrr3r8rr rr=s@&3L9x%X % BLPUDr