objectives.py 文件源码

def roc_auc_score(y_pred, y_true):
    """ ROC AUC Score.

    Approximates the Area Under Curve score, using approximation based on
    the Wilcoxon-Mann-Whitney U statistic.

    Yan, L., Dodier, R., Mozer, M. C., & Wolniewicz, R. (2003).
    Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic.

    Measures overall performance for a full range of threshold levels.

    Arguments:
        y_pred: `Tensor`. Predicted values.
        y_true: `Tensor` . Targets (labels), a probability distribution.

    """
    with tf.name_scope("RocAucScore"):

        pos = tf.boolean_mask(y_pred, tf.cast(y_true, tf.bool))
        neg = tf.boolean_mask(y_pred, ~tf.cast(y_true, tf.bool))

        pos = tf.expand_dims(pos, 0)
        neg = tf.expand_dims(neg, 1)

        # original paper suggests performance is robust to exact parameter choice
        gamma = 0.2
        p     = 3

        difference = tf.zeros_like(pos * neg) + pos - neg - gamma

        masked = tf.boolean_mask(difference, difference < 0.0)

        return tf.reduce_sum(tf.pow(-masked, p))