MpjrSrSSKJr SSKrSSKJs Jr SSK J r J r SSK J r JrJr SSKJr S SjrS Sjrg) z Common code for all metrics. ) combinationsN) check_arraycheck_consistent_length)_average_ravelget_namespace_and_device)type_of_targetc [X$5upVnSnX8;a[SRU55e[U5n U S;a[SRU 55eU S:XaU"XUS9$[ XU5 [ U5n[ U5nSn Un Sn US :Xa8U bUR XRS5n [U5n[U5nOUS :XaU bIURXRS 9nURURXRU S 55S S9n OURUS S9n [R"URU 5URS U RUS95(ag O US:XaU n Sn S n UR S:XaURUS 5nUR S:XaURUS 5nURU n UR#U 4US9n[%U 5H]n[UR'XRU/US9U S95n[UR'X%RU/US9U S95nU"UUU S9X'M_ UbU bS XS :H'[)[+XUS95$U$)a Average a binary metric for multilabel classification. Parameters ---------- binary_metric : callable, returns shape [n_classes] The binary metric function to use. y_true : array, shape = [n_samples] or [n_samples, n_classes] True binary labels in binary label indicators. y_score : array, shape = [n_samples] or [n_samples, n_classes] Target scores, can either be probability estimates of the positive class or non-thresholded decision values (as returned by :term:`decision_function` on some classifiers). average : {None, 'micro', 'macro', 'samples', 'weighted'}, default='macro' If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: ``'micro'``: Calculate metrics globally by considering each element of the label indicator matrix as a label. ``'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). ``'samples'``: Calculate metrics for each instance, and find their average. Will be ignored when ``y_true`` is binary. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- score : float or array of shape [n_classes] If not ``None``, average the score, else return the score for each classes. )Nmicromacroweightedsampleszaverage has to be one of {0})binaryzmultilabel-indicatorz{0} format is not supportedr) sample_weightNr r )dtype)rr)axis)rdevicer)r)weightsxp)r ValueErrorformatr rrrepeatshaperasarrayrsummultiplyreshapexpxisclosendimzerosrangetakefloatr) binary_metricy_truey_scoreaveragerr__deviceaverage_optionsy_typenot_average_axis score_weightaverage_weight n_classesscorecy_true_c y_score_cs Q/var/www/html/pdf-tiff/venv/lib/python3.13/site-packages/sklearn/metrics/_base.py_average_binary_scorer8sX.gENB7EO%7>>OPP F #F 776==fEFF VMJJF];  F'"G LN'  #99\<<?CL/ J   #ZZ.@.@ZAFVV FJJ|W$EFQ$N VVFV3N ;; FF> " JJq 4 4WJ E    I %  {{aFG,||q**Wg. ./I HHi\'H 2E 9  GGFJJs7J;BRG S  GGGZZGZ? FQ--/ ZZ0JN$VWaZ-@A $VWaZ-@A ',6!; @ ::k 66r9)N)r )__doc__ itertoolsrnumpyr<!sklearn.externals.array_api_extra externalsarray_api_extrar sklearn.utilsrrsklearn.utils._array_apirrrsklearn.utils.multiclassr r8rPr9r7r[s7#//> 4tnC7r9