classification_metrics.py 文件源码

def acc_metric(solution, prediction, task=BINARY_CLASSIFICATION):
    """
    Compute the accuracy.

    Get the accuracy stats
    acc = (tpr + fpr) / (tn + fp + tp + fn)
    Normalize, so 1 is the best and zero mean random...

    :param solution:
    :param prediction:
    :param task:
    :return:
    """

    label_num = solution.shape[1]
    bin_predictions = binarize_predictions(prediction, task)
    tn, fp, tp, fn = acc_stat(solution, bin_predictions)
    # Bounding to avoid division by 0
    eps = np.float(1e-15)
    tp = np.sum(tp)
    fp = np.sum(fp)
    tn = np.sum(tn)
    fn = np.sum(fn)

    if (task != MULTICLASS_CLASSIFICATION) or (label_num == 1):
        accuracy = (np.sum(tp) + np.sum(tn)) / (
            np.sum(tp) + np.sum(fp) + np.sum(tn) + np.sum(fn)
        )
    else:
        accuracy = np.sum(tp) / (np.sum(tp) + np.sum(fp))

    if (task != MULTICLASS_CLASSIFICATION) or (label_num == 1):
        base_accuracy = 0.5  # random predictions for binary case
    else:
        base_accuracy = 1. / label_num
    # Normalize: 0 for random, 1 for perfect
    score = (accuracy - base_accuracy) / sp.maximum(eps, (1 - base_accuracy))
    return score