eval.py 文件源码

def dcg_at_k(r, k):
    """
    Args:
        r: Relevance scores (list or numpy) in rank order
            (first element is the first item)
        k: Number of results to consider
    Returns:
        Discounted cumulative gain
    """
    r = np.asfarray(r)[:k]
    if r.size:
        return np.sum(r / np.log2(np.arange(2, r.size + 2)))
    return 0.