我是var searchresponse = [{
"items": [{
"employeeId": "ABC",
"type": "D",
"alive": "Yes"
}, {
"employeeId": "DEF",
"type": "D",
"alive": "Yes"
}, {
"employeeId": "NPK",
"type": "D",
"alive": "Yes"
}, {
"employeeId": "PKN",
"type": "A",
"alive": "Yes"
}],
"more": false
}];
var data1 = ["ABC", "NPK", "PKN"];
var data1 = ["ABC", "NPK"];
var items = searchresponse[0].items;
for (var i = items.length - 1; i >= 0; i--) {
if (data1.indexOf(items[i].employeeId) != -1) {
items.splice(i, 1);
}
}
console.log(searchresponse[0].items);的新手，我正在尝试通过将其与传统xgboost进行比较来学习如何使用它。但是，我注意到gbm比xgboost慢得多。例子是：
gbm在具有8个内核的Macbook Pro上，输出为：
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)
我认为xgboost应该快得多，所以我一定做错了。有人可以帮助指出我做错了什么吗？
答案 0 :(得分：1)
这是在我的机器上运行时的输出，未在n_jobs
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，GBM的输出 2.5s ，但XGB需要很长时间。
所以这可能是n_jobs的问题！也许XGBoost库没有很好地配置为使用GridSearchCV运行n_jobs。