test_ranking.py 文件源码

def _my_lrap(y_true, y_score):
    """Simple implementation of label ranking average precision"""
    check_consistent_length(y_true, y_score)
    y_true = check_array(y_true)
    y_score = check_array(y_score)
    n_samples, n_labels = y_true.shape
    score = np.empty((n_samples, ))
    for i in range(n_samples):
        # The best rank correspond to 1. Rank higher than 1 are worse.
        # The best inverse ranking correspond to n_labels.
        unique_rank, inv_rank = np.unique(y_score[i], return_inverse=True)
        n_ranks = unique_rank.size
        rank = n_ranks - inv_rank

        # Rank need to be corrected to take into account ties
        # ex: rank 1 ex aequo means that both label are rank 2.
        corr_rank = np.bincount(rank, minlength=n_ranks + 1).cumsum()
        rank = corr_rank[rank]

        relevant = y_true[i].nonzero()[0]
        if relevant.size == 0 or relevant.size == n_labels:
            score[i] = 1
            continue

        score[i] = 0.
        for label in relevant:
            # Let's count the number of relevant label with better rank
            # (smaller rank).
            n_ranked_above = sum(rank[r] <= rank[label] for r in relevant)

            # Weight by the rank of the actual label
            score[i] += n_ranked_above / rank[label]

        score[i] /= relevant.size

    return score.mean()