listwise.py 文件源码

def listmle(x, t):
    """
    The ListMLE loss as in Xia et al (2008), Listwise Approach to Learning to
    Rank - Theory and Algorithm.

    :param x: The activation of the previous layer 
    :param t: The target labels
    :return: The loss
    """

    # Get the ground truth by sorting activations by the relevance labels
    xp = cuda.get_array_module(t)
    t_hat = t[:, 0]
    x_hat = x[xp.flip(xp.argsort(t_hat), axis=0)]

    # Compute MLE loss
    final = logcumsumexp(x_hat)
    return F.sum(final - x_hat)