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Common code for all metrics.

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|t|ddd}	ntj	|dd}	t|		 drdS n|dkr|}	d}d}|jdkr*|d}|jdkr@|d}|j| }
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D ]@}|j|g|d }|j|g|d }| |||d||< q^|dk	r|	dk	rt|	}	d||	dk< tj||	dS |S dS )aM  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 : {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.

    )Nmicromacroweightedsampleszaverage has to be one of {0})binaryzmultilabel-indicatorz{0} format is not supportedr   )sample_weight   Nr   r	   )r   r   )Zaxisg        r
   weights)
ValueErrorformatr   r   r   nprepeatshapeZravelsummultiplyZreshapeisclosendimzerosrangeZtakeZasarrayaverage)binary_metricy_truey_scorer   r   Zaverage_optionsZy_typeZnot_average_axisZscore_weightZaverage_weight	n_classesZscorecZy_true_cZ	y_score_c r"   9/tmp/pip-unpacked-wheel-zrfo1fqw/sklearn/metrics/_base.py_average_binary_score   s`    +
 


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r$   r   c                 C   s   t || t|}|jd }||d  d }t|}|dk}|rNt|nd}	tt|dD ]~\}
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< q`tj||	dS )aL  Average 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.
    r   r   r   r	   Nr   )	r   r   uniquer   empty	enumerater   
logical_orr   )r   r   r   r   Zy_true_uniquer    Zn_pairsZpair_scoresZis_weightedZ
prevalenceixabZa_maskZb_maskZab_maskZa_trueZb_trueZa_true_scoreZb_true_scorer"   r"   r#   _average_multiclass_ovo_score   s&    (
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r,   c                 C   s   t |}| dkr|jjdksht |ddgst |ddgst |dgst |dgst |dgsddd |D }td	| d
n| dkrd} | S )a  Check if `pos_label` need to be specified or not.

    In binary classification, we fix `pos_label=1` if the labels are in the set
    {-1, 1} or {0, 1}. Otherwise, we raise an error asking to specify the
    `pos_label` parameters.

    Parameters
    ----------
    pos_label : int, str or None
        The positive label.
    y_true : ndarray of shape (n_samples,)
        The target vector.

    Returns
    -------
    pos_label : int
        If `pos_label` can be inferred, it will be returned.

    Raises
    ------
    ValueError
        In the case that `y_true` does not have label in {-1, 1} or {0, 1},
        it will raise a `ValueError`.
    NZOUSr   r   r   z, c                 s   s   | ]}t |V  qd S )N)repr).0r!   r"   r"   r#   	<genexpr>   s     z/_check_pos_label_consistency.<locals>.<genexpr>zy_true takes value in {zr} and pos_label is not specified: either make y_true take value in {0, 1} or {-1, 1} or pass pos_label explicitly.)r   r%   ZdtypekindZarray_equaljoinr   )Z	pos_labelr   classesZclasses_reprr"   r"   r#   _check_pos_label_consistency   s*    
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r3   )N)r   )__doc__	itertoolsr   Znumpyr   utilsr   r   Zutils.multiclassr   r$   r,   r3   r"   r"   r"   r#   <module>   s   
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