]jWLSrSSKJr SSKrSSKJrJr SSKJ r SSjr S Sjr g) z Common code for all metrics. ) combinationsN) check_arraycheck_consistent_length)type_of_targetc SnX5;a[SRU55e[U5nUS;a[SRU55eUS:XaU"XUS9$[XU5 [ U5n[ U5nSnUnSn US :XaGUb#[ R "XRS5nUR5nUR5nOUS :XaUb@[ R"[ R"U[ R"US 55S S 9n O[ R"US S 9n [ R"U R5S5(ag O US:XaUn SnS nURS:XaURS 5nURS:XaURS 5nURUn [ R"U 45n [U 5HJn UR!U /US 9R5n UR!U /US 9R5nU"XUS9X'ML Ub=U b[ R""U 5n S XS :H'[%[ R&"XS95$U $)aAverage a binary metric for multilabel classification. Parameters ---------- y_true : array, shape = [n_samples] or [n_samples, n_classes] True binary labels in binary label indicators. y_score : array, shape = [n_samples] or [n_samples, n_classes] Target scores, can either be probability estimates of the positive class, confidence values, or binary decisions. average : {None, 'micro', 'macro', 'samples', 'weighted'}, default='macro' If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: ``'micro'``: Calculate metrics globally by considering each element of the label indicator matrix as a label. ``'macro'``: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. ``'weighted'``: Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). ``'samples'``: Calculate metrics for each instance, and find their average. Will be ignored when ``y_true`` is binary. sample_weight : array-like of shape (n_samples,), default=None Sample weights. binary_metric : callable, returns shape [n_classes] The binary metric function to use. Returns ------- score : float or array of shape [n_classes] If not ``None``, average the score, else return the score for each classes. )Nmicromacroweightedsampleszaverage has to be one of {0})binaryzmultilabel-indicatorz{0} format is not supportedr ) sample_weightNrr )rr)axisgr weights) ValueErrorformatrrrnprepeatshaperavelsummultiplyreshapeisclosendimzerosrangetakeasarrayfloataverage) binary_metricy_truey_scorer#r average_optionsy_typenot_average_axis score_weightaverage_weight n_classesscorecy_true_c y_score_cs R/home/mande/repo/quber/.venv/lib/python3.13/site-packages/sklearn/metrics/_base.py_average_binary_scorer2sDVFO%7>>OPP F #F 776==fEFF VMJJF];  F'"G LN'  #99\<<?CL--/ J   #VV FBJJ|W$EFQN VVF3N ::n((*C 0 0 1 I %  {{a(||q//'* ./I HHi\ "E 9 ;;s)9;:@@BLL!+;L<BBD  LQ   % ZZ7N)*EA% &RZZ>?? c&[X5 [R"U5nURSnXUS- -S-n[R"U5nUS:HnU(a[R"U5OSn [ [ US55Hqun upX:Hn X:Hn[R"X5nU(a[R"U5X'XnXnU"UX/U 45nU"UX/U 45nUU-S- Xz'Ms [R"XyS9$)aAverage one-versus-one scores for multiclass classification. Uses the binary metric for one-vs-one multiclass classification, where the score is computed according to the Hand & Till (2001) algorithm. Parameters ---------- binary_metric : callable The binary metric function to use that accepts the following as input: y_true_target : array, shape = [n_samples_target] Some sub-array of y_true for a pair of classes designated positive and negative in the one-vs-one scheme. y_score_target : array, shape = [n_samples_target] Scores corresponding to the probability estimates of a sample belonging to the designated positive class label y_true : array-like of shape (n_samples,) True multiclass labels. y_score : array-like of shape (n_samples, n_classes) Target scores corresponding to probability estimates of a sample belonging to a particular class. average : {'macro', 'weighted'}, default='macro' Determines the type of averaging performed on the pairwise binary metric scores: ``'macro'``: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. Classes are assumed to be uniformly distributed. ``'weighted'``: Calculate metrics for each label, taking into account the prevalence of the classes. Returns ------- score : float Average of the pairwise binary metric scores. rrr Nr) rruniquerempty enumerater logical_orr#)r$r%r&r# y_true_uniquer,n_pairs pair_scores is_weighted prevalenceixaba_maskb_maskab_maska_trueb_true a_true_score b_true_scores r1_average_multiclass_ovo_scorerI~s PF,IIf%M##A&Iq=)Q.G((7#KZ'K&1'"tJ ]A >? FQ--/ ZZ0JN$VWaZ-@A $VWaZ-@A ',6!; @ ::k 66r3)N)r ) __doc__ itertoolsrnumpyr sklearn.utilsrrsklearn.utils.multiclassrr2rIr3r1rPs%#>3jZC7r3