XGBRegressor比GradientBoostingRegressor慢得多

Question

我是xgboost的新手，並試圖通過將其與傳統gbm進行比較來學習如何使用它。但是，我注意到xgboost比gbm慢得多。這個例子是：

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MacBook Pro上有8個內核，輸出爲：

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 

我想xgboost要快很多，所以我必須做一些錯誤的。有人可以幫助指出我做錯了嗎？

Answer 1

這是輸出我的機器上運行時，沒有當n_jobs設置爲4 cv_params

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 

設置n_jobs參數，輸出爲2.5S爲GBM，但需要很長一段時間對於XGB。

所以也許這是一個n_jobs的問題！也許XGBoost庫不能很好地配置爲使用GridSearchCV運行n_jobs。