如何检索 sklearn.model_selection.train_test_split 的 random_state? [英] How to retrieve the random_state of sklearn.model_selection.train_test_split?
问题描述
如何检索sklearn.model_selection.train_test_split的随机状态?
没有设置random_state，我用train_test_split 分割了我的数据集.因为在分割数据集上训练的机器学习模型表现非常好，我想检索用于分割数据集的 random_state.有没有类似 numpy.random.get_state()
Without setting the random_state, I split my dataset with train_test_split. Because the machine learning model trained on the split dataset performs quite well, I want to retrieve the random_state that was used to split the dataset. Is there something like numpy.random.get_state()
推荐答案
如果你跟踪train_test_split的调用栈，你会发现使用了random_state参数像这样:
If you trace through the call stack of train_test_split, you'll find the random_state parameters is used like this:
from sklearn.utils import check_random_state
rng = check_random_state(self.random_state)
print(rng)
check_random_state 的相关部分是
def check_random_state(seed):
if seed is None or seed is np.random:
return np.random.mtrand._rand
如果 random_state=None，你会得到默认的 numpy.random.RandomState 单例，你可以用它来生成新的随机数，例如:
If random_state=None, you get the default numpy.random.RandomState singleton, which you can use to generate new random numbers, e.g.:
print(rng.permutation(10))
print(rng.randn(10))
有关详细信息，请参阅这些问题:
See these questions for more information:
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