classification_metrics.py 文件源码

def bac_metric(solution, prediction, task=BINARY_CLASSIFICATION):
    """
    Compute the normalized balanced accuracy.

    The binarization and
    the normalization differ for the multi-label and multi-class case.
    :param solution:
    :param prediction:
    :param task:
    :return:
    """

    label_num = solution.shape[1]
    score = np.zeros(label_num)
    bin_prediction = binarize_predictions(prediction, task)
    [tn, fp, tp, fn] = acc_stat(solution, bin_prediction)
    # Bounding to avoid division by 0
    eps = 1e-15
    tp = sp.maximum(eps, tp)
    pos_num = sp.maximum(eps, tp + fn)
    tpr = tp / pos_num  # true positive rate (sensitivity)
    if (task != MULTICLASS_CLASSIFICATION) or (label_num == 1):
        tn = sp.maximum(eps, tn)
        neg_num = sp.maximum(eps, tn + fp)
        tnr = tn / neg_num  # true negative rate (specificity)
        bac = 0.5 * (tpr + tnr)
        base_bac = 0.5  # random predictions for binary case
    else:
        bac = tpr
        base_bac = 1. / label_num  # random predictions for multiclass case
    bac = np.mean(bac)  # average over all classes
    # Normalize: 0 for random, 1 for perfect
    score = (bac - base_bac) / sp.maximum(eps, (1 - base_bac))
    return score