metrics.py 文件源码

def kendall_tau(y_true, y_pred):
    """
    Calculate Kendall's tau between ``y_true`` and ``y_pred``.

    :param y_true: The true/actual/gold labels for the data.
    :type y_true: array-like of float
    :param y_pred: The predicted/observed labels for the data.
    :type y_pred: array-like of float

    :returns: Kendall's tau if well-defined, else 0
    """
    ret_score = kendalltau(y_true, y_pred)[0]
    return ret_score if not np.isnan(ret_score) else 0.0