rtjpdZddlmZddlZddlmcmZddl m Z m Z ddl m Z mZmZddlmZd dZd d ZdS) z Common code for all metrics. ) combinationsN) check_arraycheck_consistent_length)_average_ravelget_namespace_and_device)type_of_targetc t||\}}}d}||vr"td|t|} | dvr"td| | dkr||||St |||t |}t |}d} |} d} |d krB| !|| |jd} t|}t|}n|d kr| \| || j }| | || | d d } n| |d } tj| | | d | j |rd Sn |dkr| } d} d } |jdkr| |d }|jdkr| |d }|j| } || f|}t%| D]}t||| |g|| }t||| |g|| }|||| ||<|*| 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 S/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/sklearn/metrics/_base.py_average_binary_scorer8sHX.g}EENB7EOo%%7>>OOPPP F # #F 7776==fEEFFF }VWMJJJJFG];;;  F'""G LN'  #99\6<?CCL// J    #ZZl.@ZAAFVV FBJJ|W$E$EFFQ$NN VVFV33N ; FF> " " JJq 4WJ E E   1   I  %  {aFG,,|q**Wg.. ./I HHi\'H 2 2E 9  RR GGFBJJs7J;;BRG S S   GGGRZZGZ< >?? < < FQ11-//  1Z00JrN$}VWWaZ-@AA $}VWWaZ-@AA ',6!; B :k: 6 6 66r9)N)r )__doc__ itertoolsrnumpyr<!sklearn.externals.array_api_extra externalsarray_api_extrar sklearn.utilsrrsklearn.utils._array_apirrrsklearn.utils.multiclassr r8rPr9r7r[s#"""""/////////>>>>>>>> 433333ttttnC7C7C7C7C7C7r9