bi_eval.py 文件源码

def check_predict():
    Best = namedtuple("Best", ["U", "values", "min_value"])
    values = [((0, 0), 0) for _ in range(N_TOP_PER_CHUNK)]
    bests = []
    bests.append(Best(U_sinh, list(values), 0))
    bests.append(Best(U_rr, list(values), 0))
    user_indices = random.choices(range(len(users)), k=N_PER_CHUNK)
    repo_indices = random.choices(range(len(repos)), k=N_PER_CHUNK)
    for index in range(N_PER_CHUNK):
        pos = (user_indices[index], repo_indices[index])
        if pos in nonzero_positions:    # Link already exists.
            continue
        for i in range(len(bests)):
            new = bests[i].U[pos[0], :] @ V[:, pos[1]]
            if new > bests[i].min_value:
                bests[i].values.append((pos, new))
                bests[i].values.sort(key=lambda p: p[1], reverse=True)
                bests[i].values.pop()
                bests[i] = bests[i]._replace(min_value=bests[i].values[-1][1])
    for i in range(len(bests)):
        assert len(bests[i].values) == N_TOP_PER_CHUNK
    return bests[0].values, bests[1].values