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Common code for all metrics

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  D][ } | j | g d | j	   } | j | g d | j	   } |  | | d	 | | | <qW| d k	 rt j | d |	 S| Sd S(   sJ  Average 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 : string, [None, 'micro', 'macro' (default), 'samples', 'weighted']
        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], optional
        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.

    t   microt   macrot   weightedt   sampless   average has to be one of {0}t   binarys   multilabel-indicators   {0} format is not supportedt   sample_weighti   i˙˙˙˙t   axisi    g        t   weightsN(   NR   R   R   R   (   R   s   multilabel-indicator(   i˙˙˙˙i   (   i˙˙˙˙i   (   i˙˙˙˙i   (   t   Nonet
   ValueErrort   formatR   R   R   t   npt   repeatt   shapet   ravelt   sumt   multiplyt   reshapet   iscloset   ndimt   zerost   ranget   taket   average(   t   binary_metrict   y_truet   y_scoreR   R	   t   average_optionst   y_typet   not_average_axist   score_weightt   average_weightt	   n_classest   scoret   ct   y_true_ct	   y_score_c(    (    s3   lib/python2.7/site-packages/sklearn/metrics/base.pyt   _average_binary_score   sZ    ,	$	(   t   __doc__t
   __future__R    t   numpyR   t   utilsR   R   t   utils.multiclassR   R   R)   (    (    (    s3   lib/python2.7/site-packages/sklearn/metrics/base.pyt   <module>   s
   