def explained_variance_score(y_true, y_pred,
                             sample_weight=None,
                             multioutput='uniform_average'):
    """Explained variance regression score function

    Best possible score is 1.0, lower values are worse.

    Read more in the :ref:`User Guide <explained_variance_score>`.

    Parameters
    ----------
    y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
        Ground truth (correct) target values.

    y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
        Estimated target values.

    sample_weight : array-like of shape = (n_samples), optional
        Sample weights.

    multioutput : string in ['raw_values', 'uniform_average', \
                'variance_weighted'] or array-like of shape (n_outputs)
        Defines aggregating of multiple output scores.
        Array-like value defines weights used to average scores.

        'raw_values' :
            Returns a full set of scores in case of multioutput input.

        'uniform_average' :
            Scores of all outputs are averaged with uniform weight.

        'variance_weighted' :
            Scores of all outputs are averaged, weighted by the variances
            of each individual output.

    Returns
    -------
    score : float or ndarray of floats
        The explained variance or ndarray if 'multioutput' is 'raw_values'.

    Notes
    -----
    This is not a symmetric function.

    Examples
    --------
    >>> from sklearn.metrics import explained_variance_score
    >>> y_true = [3, -0.5, 2, 7]
    >>> y_pred = [2.5, 0.0, 2, 8]
    >>> explained_variance_score(y_true, y_pred)  # doctest: +ELLIPSIS
    0.957...
    >>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
    >>> y_pred = [[0, 2], [-1, 2], [8, -5]]
    >>> explained_variance_score(y_true, y_pred, multioutput='uniform_average')
    ... # doctest: +ELLIPSIS
    0.983...

    """
    y_type, y_true, y_pred, multioutput = _check_reg_targets(
        y_true, y_pred, multioutput)

    y_diff_avg = np.average(y_true - y_pred, weights=sample_weight, axis=0)
    numerator = np.average((y_true / y_pred - y_diff_avg) ** 2,
                           weights=sample_weight, axis=0)

    y_true_avg = np.average(y_true, weights=sample_weight, axis=0)
    denominator = np.average((y_true - y_true_avg) ** 2,
                             weights=sample_weight, axis=0)

    nonzero_numerator = numerator != 0
    nonzero_denominator = denominator != 0
    valid_score = nonzero_numerator & nonzero_denominator
    output_scores = np.ones(y_true.shape[1])

    output_scores[valid_score] = 1 - (numerator[valid_score] /
                                      denominator[valid_score])
    output_scores[nonzero_numerator & ~nonzero_denominator] = 0.
    if isinstance(multioutput, string_types):
        if multioutput == 'raw_values':
            # return scores individually
            return output_scores
        elif multioutput == 'uniform_average':
            # passing to np.average() None as weights results is uniform mean
            avg_weights = None
        elif multioutput == 'variance_weighted':
            avg_weights = denominator
    else:
        avg_weights = multioutput

    return np.average(output_scores, weights=avg_weights)
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/metrics/regression.py/explained_variance_score/operator `-` -> `/`