与sklearn并行训练多个模型? [英] Train multiple models in parallel with sklearn?

问题描述

我想训练具有不同随机状态的多个LinearSVC模型，但我更喜欢并行进行. sklearn中是否有支持此功能的机制?我知道Gridsearch或某些合奏方法正在隐式地执行操作，但是到底是什么呢?

I want to train multiple LinearSVC models with different random states but I prefer to do it in parallel. Is there an mechanism supporting this in sklearn? I know Gridsearch or some ensemble methods are doing in implicitly but what is the thing under the hood?

推荐答案

内部的东西"是库 joblib ，例如GridSearchCV中的多重处理和一些集成方法.它的Parallel辅助类是非常方便的瑞士刀，用于尴尬地并行循环.

The "thing" under the hood is the library joblib, which powers for example the multi-processing in GridSearchCV and some ensemble methods. It's Parallel helper class is a very handy Swiss knife for embarrassingly parallel for loops.

这是一个使用joblib并行训练具有四个随机过程的具有不同随机状态的多个LinearSVC模型的示例:

This is an example to train multiple LinearSVC models with different random states in parallel with 4 processes using joblib:

from joblib import Parallel, delayed
from sklearn.svm import LinearSVC
import numpy as np

def train_model(X, y, seed):
    model = LinearSVC(random_state=seed)
    return model.fit(X, y)

X = np.array([[1,2,3],[4,5,6]])
y = np.array([0, 1])
result = Parallel(n_jobs=4)(delayed(train_model)(X, y, seed) for seed in range(10))
# result is a list of 10 models trained using different seeds

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