563f6564727f0b5c3d945d82402bcb7766dee00a,aif360/sklearn/metrics/metrics.py,,specificity_score,#,159

Before Change

            the data should be binary.
        sample_weight (array-like, optional): Sample weights.
    
    return recall_score(y_true, y_pred, pos_label=neg_label,
                        sample_weight=sample_weight)

def base_rate(y_true, y_pred=None, pos_label=1, sample_weight=None):
    rCompute the base rate, :math:`Pr(Y = \text{pos_label}) = \frac{P}{P+N}`.

After Change

                                      sample_weight=sample_weight)
    tn, fp, fn, tp = MCM.ravel()
    negs = tn + fp
    if negs == 0:
        warnings.warn("specificity_score is ill-defined and being set to 0.0 "
                      "due to no negative samples.", UndefinedMetricWarning)
        return 0.
    return tn / negs

def base_rate(y_true, y_pred=None, pos_label=1, sample_weight=None):
    rCompute the base rate, :math:`Pr(Y = \text{pos_label}) = \frac{P}{P+N}`.

Instances