o }ThD@sLdZddlmZddlZddlmZmZddlm Z d ddZ d d d Z dS)z Common code for all metrics. ) combinationsN) check_arraycheck_consistent_length)type_of_targetcCsd}||vr td|t|}|dvrtd||dkr'||||dSt|||t|}t|}d}|}d} |d krU|durLt||jd}|}|}n4|d kr|durmtj t |t |d d d } ntj |d d } t | dr~d Sn |dkr|} d}d }|j dkr| d }|j dkr| d }|j|} t| f} t| D]} |j| g|d } |j| g|d }|| ||d| | <q|dur| durt| } d | | d k<tj| | dS| S)aMAverage 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 )r r)axisgr weights) ValueErrorformatrrrnprepeatshaperavelsummultiplyreshapeisclosendimzerosrangetakeasarrayaverage) binary_metricy_truey_scorer!r average_optionsy_typenot_average_axis score_weightaverage_weight n_classesscorecy_true_c y_score_cr/V/home/air/segue/gemini/back/venv/lib/python3.10/site-packages/sklearn/metrics/_base.py_average_binary_scores`+           r1rcCst||t|}|jd}||dd}t|}|dk}|r't|nd} tt|dD]?\} \} } || k} || k}t| |}|rMt|| | <| |}||}||||| f}||||| f}||d|| <q0tj|| dS)aLAverage 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. rr rr 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_scorer/r/r0_average_multiclass_ovo_score~s& (    rE)N)r) __doc__ itertoolsrnumpyrutilsrrutils.multiclassrr1rEr/r/r/r0s   m