U 9%e|@sLdZddlmZddlZddlmZmZddlm Z d ddZ d d d Z dS)z Common code for all metrics. ) combinationsN) check_arraycheck_consistent_length)type_of_targetcCsd}||krtd|t|}|dkr8td||dkrN||||dSt|||t|}t|}d}|}d} |d kr|dk rt||jd}|}|}nj|d kr|dk rtj t |t |d d d } ntj |d d } t | drd Sn|dkr|} d}d }|j dkr*| d }|j dkr@| d }|j|} t| f} t| D]@} |j| g|d } |j| g|d }|| ||d| | <q^|dk r| dk rt| } d | | d k<tj| | dS| SdS)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)Zaxisgr weights) ValueErrorformatrrrnprepeatshapeZravelsummultiplyZreshapeisclosendimZzerosrangeZtakeZasarrayaverage) binary_metricy_truey_scorerr Zaverage_optionsZy_typeZnot_average_axisZ score_weightZaverage_weight n_classesZscorecZy_true_cZ y_score_cr!T/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/sklearn/metrics/_base.py_average_binary_scores`+              r#rcCst||t|}|jd}||dd}t|}|dk}|rNt|nd} tt|dD]~\} \} } || k} || k}t| |}|rt|| | <| |}||}||||| f}||||| f}||d|| <q`tj|| 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)rrrrZ y_true_uniquerZn_pairsZ pair_scoresZ is_weightedZ prevalenceZixabZa_maskZb_maskZab_maskZa_trueZb_trueZ a_true_scoreZ b_true_scorer!r!r"_average_multiclass_ovo_scores&(     r*)N)r) __doc__ itertoolsrnumpyrutilsrrZutils.multiclassrr#r*r!r!r!r"s    m