GridSearchCV-XGBoost-提前停止

问题描述

我正在尝试通过在XGBoost上使用scikit-learn的GridSearchCV进行超参数表搜索。在进行网格搜索时，我希望它早点停止，因为它可以大大减少搜索时间，并且（预期）在我的预测/回归任务中有更好的结果。我正在通过Scikit-Learn API使用XGBoost。

i am trying to do hyperparemeter search with using scikit-learn's GridSearchCV on XGBoost. During gridsearch i'd like it to early stop, since it reduce search time drastically and (expecting to) have better results on my prediction/regression task. I am using XGBoost via its Scikit-Learn API.

    model = xgb.XGBRegressor()
    GridSearchCV(model, paramGrid, verbose=verbose ,fit_params={'early_stopping_rounds':42}, cv=TimeSeriesSplit(n_splits=cv).get_n_splits([trainX, trainY]), n_jobs=n_jobs, iid=iid).fit(trainX,trainY)

我尝试使用fit_params给出早期停止参数，但是随后抛出此错误，主要是因为缺少早期停止所需的验证集：

I tried to give early stopping parameters with using fit_params, but then it throws this error which is basically because of lack of validation set which is required for early stopping:

/opt/anaconda/anaconda3/lib/python3.5/site-packages/xgboost/callback.py in callback(env=XGBoostCallbackEnv(model=<xgboost.core.Booster o...teration=4000, rank=0, evaluation_result_list=[]))
    187         else:
    188             assert env.cvfolds is not None
    189
    190     def callback(env):
    191         """internal function"""
--> 192         score = env.evaluation_result_list[-1][1]
        score = undefined
        env.evaluation_result_list = []
    193         if len(state) == 0:
    194             init(env)
    195         best_score = state['best_score']
    196         best_iteration = state['best_iteration']

如何使用early_stopping_rounds在XGBoost上应用GridSearch？

How can i apply GridSearch on XGBoost with using early_stopping_rounds?

注意：模型在没有gridsearch的情况下也有效，在没有'fit_params = { 'early_stopping_rounds'：42}

note: model is working without gridsearch, also GridSearch works without 'fit_params={'early_stopping_rounds':42}

推荐答案

对@glao答案的更新和对@Vasim的评论/问题的答复，如sklearn 0.21.3（请注意， fit_params 已从 GridSearchCV 的实例化中移出，并已移至 fit（）方法；同样，导入专门从xgboost提取sklearn包装器模块）：

An update to @glao's answer and a response to @Vasim's comment/question, as of sklearn 0.21.3 (note that fit_params has been moved out of the instantiation of GridSearchCV and been moved into the fit() method; also, the import specifically pulls in the sklearn wrapper module from xgboost):

import xgboost.sklearn as xgb
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import TimeSeriesSplit

cv = 2

trainX= [[1], [2], [3], [4], [5]]
trainY = [1, 2, 3, 4, 5]

# these are the evaluation sets
testX = trainX
testY = trainY

paramGrid = {"subsample" : [0.5, 0.8]}

fit_params={"early_stopping_rounds":42,
            "eval_metric" : "mae",
            "eval_set" : [[testX, testY]]}

model = xgb.XGBRegressor()

gridsearch = GridSearchCV(model, paramGrid, verbose=1,
         cv=TimeSeriesSplit(n_splits=cv).get_n_splits([trainX, trainY]))

gridsearch.fit(trainX, trainY, **fit_params)

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