:jndZddlmZddlZddlmcmZddl m Z m Z ddl m Z mZmZddlmZd dZd dZy) z Common code for all metrics. ) combinationsN) check_arraycheck_consistent_length)_average_ravelget_namespace_and_device)type_of_targetc t||\}}}d}||vrtdj|t|} | dvrtdj| | dk(r ||||St |||t |}t |}d} |} d} |d k(r8| |j | |jd} t|}t|}n|d k(r| Q|j|| j }|j|j||j| d d } n|j|d } tj|j| |jd | j|r y |dk(r| } d} d } |j dk(r|j|d }|j dk(r|j|d }|j| } |j#| f|}t%| D]n}t|j'||j|g|| }t|j'||j|g|| }|||| ||<p|!| d || d k(<t)t+|| |S|S)aAverage 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 j/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/sklearn/metrics/_base.py_average_binary_scorer8sX.g}ENB7EOo%7>>OPP F #F 776==fEFF VWMJJFG];  F'"G LN'  #99\6<<?CL/ J   #ZZl.@.@ZAFVV FBJJ|W$EFQ$N VVFV3N ;; FF> " JJq 4 4WJ E  I %  {{aFG,||q**Wg. ./I HHi\'H 2E 9 R GGFBJJs7J;BRG S  GGGRZZGZ? < FQ11--/ ZZ0JrN$VWWaZ-@A $VWWaZ-@A ',6!; B < ::k: 66r9)N)r )__doc__ itertoolsrnumpyr<!sklearn.externals.array_api_extra externalsarray_api_extrar sklearn.utilsrrsklearn.utils._array_apirrrsklearn.utils.multiclassr r8rPr9r7r[s7#//> 4tnC7r9