XGBRegressor比GradientBoostingRegressor慢得多

Question

我对xgboost很陌生，并试图通过将它与传统的gbm进行比较来学习如何使用它。但是，我注意到xgboost比gbm慢得多。例子是：

from sklearn.model_selection import KFold, GridSearchCV
from sklearn.ensemble import GradientBoostingRegressor
from xgboost import XGBRegressor
from sklearn.datasets import load_boston
import time

boston = load_boston()
X = boston.data
y = boston.target

kf = KFold(n_splits = 5)
cv_params = {'cv': kf, 'scoring': 'r2', 'n_jobs': 4, 'verbose': 1}

gbm = GradientBoostingRegressor()
xgb = XGBRegressor()

grid = {'n_estimators': [100, 300, 500], 'max_depth': [3, 5]}

timer = time.time()
gbm_cv = GridSearchCV(gbm, param_grid = grid, **cv_params).fit(X, y)
print('GBM time: ', time.time() - timer)

timer = time.time()
xgb_cv = GridSearchCV(xgb, param_grid = grid, **cv_params).fit(X, y)
print('XGB time: ', time.time() - timer)

在具有8个核的Macbook Pro上，输出如下：

Fitting 5 folds for each of 6 candidates, totalling 30 fits
[Parallel(n_jobs=4)]: Done  30 out of  30 | elapsed:    1.9s finished
GBM time:  2.262791872024536
Fitting 5 folds for each of 6 candidates, totalling 30 fits
[Parallel(n_jobs=4)]: Done  30 out of  30 | elapsed:   16.4s finished
XGB time:  17.902266025543213

我认为xgboost应该快得多，所以我肯定做错了什么。有人能帮我指出我做的不对吗？

Answer 1

这是在我的机器上运行时的输出，没有在n_jobs中设置cv_params参数。

Fitting 5 folds for each of 6 candidates, totalling 30 fits
[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    4.1s finished
('GBM time: ', 4.248916864395142)
Fitting 5 folds for each of 6 candidates, totalling 30 fits
('XGB time: ', 2.934467077255249)
[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    2.9s finished

当n_jobs设置为4时，输出为GBM的2.5s，但对于XGB则需要很长时间。

所以，也许这是n_jobs的一个问题！也许XGBoost库没有很好地配置为使用GridSearchCV运行n_jobs。