error_analysis.py 文件源码

def margins(doc_scores):
    margin_win = np.zeros_like(doc_scores)
    margin_lose = np.zeros_like(doc_scores)

    for j in range(doc_scores.shape[1]):
        my_scores = doc_scores[:, j]
        others = np.delete(doc_scores, j, axis=1)

        if FROM  == 'second':
            margin_win[:, j] = np.maximum(my_scores - others.max(axis=1), 0)
            margin_lose[:, j] = np.maximum(others.min(axis=1) - my_scores, 0)
        if FROM  == 'other':
            margin_win[:, j] = np.maximum(my_scores - others.min(axis=1), 0)
            margin_lose[:, j] = np.maximum(others.max(axis=1) - my_scores, 0)
        elif FROM == 'median':
            margin_win[:, j] = np.maximum(my_scores - np.median(others,
                                          axis=1), 0)
            margin_lose[:, j] = np.maximum(np.median(others, axis=1) -
                                           my_scores, 0)


    return margin_win, margin_lose