XGBRegressor：改变random_state没有效果

Question

尽管给出了新的随机种子，xgboost.XGBRegressor 似乎产生了相同的结果。

根据xgboost 文档xgboost.XGBRegressor：

seed : int 随机数种子。 （已弃用，请使用 random_state）

random_state : int 随机数种子。 （替换种子）

random_state 是要使用的，但是，无论我使用什么random_state 或seed，模型都会产生相同的结果。一个错误？

from xgboost import XGBRegressor
from sklearn.datasets import load_boston
import numpy as np
from itertools import product

def xgb_train_predict(random_state=0, seed=None):
    X, y = load_boston(return_X_y=True)
    xgb = XGBRegressor(random_state=random_state, seed=seed)
    xgb.fit(X, y)
    y_ = xgb.predict(X)
    return y_

check = xgb_train_predict()

random_state = [1, 42, 58, 69, 72]
seed = [None, 2, 24, 85, 96]

for r, s in product(random_state, seed):
    y_ = xgb_train_predict(r, s)
    assert np.equal(y_, check).all()
    print('CHECK! \t random_state: {} \t seed: {}'.format(r, s))

[Out]:
    CHECK!   random_state: 1     seed: None
    CHECK!   random_state: 1     seed: 2
    CHECK!   random_state: 1     seed: 24
    CHECK!   random_state: 1     seed: 85
    CHECK!   random_state: 1     seed: 96
    CHECK!   random_state: 42    seed: None
    CHECK!   random_state: 42    seed: 2
    CHECK!   random_state: 42    seed: 24
    CHECK!   random_state: 42    seed: 85
    CHECK!   random_state: 42    seed: 96
    CHECK!   random_state: 58    seed: None
    CHECK!   random_state: 58    seed: 2
    CHECK!   random_state: 58    seed: 24
    CHECK!   random_state: 58    seed: 85
    CHECK!   random_state: 58    seed: 96
    CHECK!   random_state: 69    seed: None
    CHECK!   random_state: 69    seed: 2
    CHECK!   random_state: 69    seed: 24
    CHECK!   random_state: 69    seed: 85
    CHECK!   random_state: 69    seed: 96
    CHECK!   random_state: 72    seed: None
    CHECK!   random_state: 72    seed: 2
    CHECK!   random_state: 72    seed: 24
    CHECK!   random_state: 72    seed: 85
    CHECK!   random_state: 72    seed: 96

Answer 1

似乎（在开始寻找答案之前我自己并不知道:)），xgboost 仅将随机生成器用于子采样，请参阅this Laurae's comment on a similar github issue。否则行为是确定性的。

如果您使用过采样，则 xgboost 中当前 sklearn API 处理 seed/random_state 时会出现问题。 seed 确实声称已弃用，但似乎如果有人提供它，它仍然会在random_state 上使用，如here in the code 所示。此评论仅在您拥有seed not None时才相关