Question

我正在使用scikit-learn进行Metaheuristics练习，我有一个疑问：我需要使用knn，所以我有一个带有n_jobs = -1的KNearestNeighbors对象。正如文档所说，我必须将多处理模式设置为forkserver。但是，使用n_jobs = -1比使用n_jobs = 1时，knn更慢。

这是一段代码

### Some initialization here ###
skf = StratifiedKFold(target, n_folds=2, shuffle=True)

for train_index, test_index in skf:
       data_train, data_test = data[train_index], data[test_index]
       target_train, target_test = target[train_index], target[test_index]

       start = time()
       selected_features, score = SFS(data_train, data_test, target_train, target_test, knn)
       end = time()

       logger.info("SFS - Time elapsed: " + str(end-start) + ". Score: " + str(score) + ". Selected features: " + str(sum(selected_features)))
if __name__ == "__main__":
    import multiprocessing as mp; mp.set_start_method('forkserver', force = True)
    main()

这是SFS功能

def SFS(data_train, data_test, target_train, target_test, classifier):
    rowsize = len(data_train[0])
    selected_features = np.zeros(rowsize, dtype=np.bool)
    best_score = 0
    best_feature = 0

    while best_feature is not None:
        end = True
        best_feature = None

        for idx in range(rowsize):
            if selected_features[idx]:
                continue

            selected_features[idx] = True
            classifier.fit(data_train[:,selected_features], target_train)
            score = classifier.score(data_test[:,selected_features], target_test)
            selected_features[idx] = False

            if score > best_score:
                best_score = score
                best_feature = idx

        if best_feature is not None:
            selected_features[best_feature] = True

    return selected_features, best_score

我不明白n_jobs怎么样＆gt; 1比n_jobs = 1慢。任何人都可以解释一下吗？我试过3个数据集。

Answer 1

我发现很多人像您一样遇到了同样的问题：n_jobs在sklearn的KNearestNeighbors中不起作用。他们还抱怨说只有1个CPU内核被装载。

在我的实验中，无论n_jobs> 1为否，拟合过程仅使用单个核。因此，无论您是否将n_jobs设置为大数，如果火车数据样本很大，训练时间将是巨大的，而且不会减少。

n_jobs> 1比n_jobs = 1还要慢的原因是因为要分配用于多处理的资源。

n_jobs＆gt;为什么scikit-learn邻居的速度较慢1和forkserver

1 个答案: