我正在尝试在 XGBoost 上使用 scikit-learn 的 GridSearchCV 进行超参数搜索。在网格搜索期间,我希望它尽早停止,因为它大大减少了搜索时间并且(期望)在我的预测/回归任务上获得更好的结果。我通过其 Scikit-Learn API 使用 XGBoost。
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 提供提前停止参数,但随后抛出此错误,这基本上是因为缺少提前停止所需的验证集:
/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?
注意:模型在没有 gridsearch 的情况下工作,GridSearch 也在没有 'fit_params={'early_stopping_rounds':42} 的情况下工作
最佳答案
这是一个在带有 GridSearchCV 的管道中工作的解决方案。当您拥有预处理训练数据所需的管道时,就会出现挑战。例如,当 X 是文本文档时,您需要 TFTDFVectorizer 对其进行矢量化。
覆盖 XGBRegressor 或 XGBClssifier.fit() 函数
来自 X 的 eval_set 的验证记录,然后通过
剩余的记录到 fit()。
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import train_test_split
class XGBRegressor_ES(XGBRegressor):
def fit(self, X, y, *, eval_test_size=None, **kwargs):
if eval_test_size is not None:
params = super(XGBRegressor, self).get_xgb_params()
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=eval_test_size, random_state=params['random_state'])
eval_set = [(X_test, y_test)]
# Could add (X_train, y_train) to eval_set
# to get .eval_results() for both train and test
#eval_set = [(X_train, y_train),(X_test, y_test)]
kwargs['eval_set'] = eval_set
return super(XGBRegressor_ES, self).fit(X_train, y_train, **kwargs)
示例用法 下面是一个多步管道,其中包括对 X 的多次转换。管道的 fit() 函数将新的评估参数作为 xgbr__eval_test_size=200 传递给上面的 XGBRegressor_ES 类。在这个例子中:
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_selection import VarianceThreshold
from sklearn.preprocessing import StandardScaler
from sklearn.feature_selection import SelectPercentile, f_regression
xgbr_pipe = Pipeline(steps=[('tfidf', TfidfVectorizer()),
('vt',VarianceThreshold()),
('scaler', StandardScaler()),
('Sp', SelectPercentile()),
('xgbr',XGBRegressor_ES(n_estimators=2000,
objective='reg:squarederror',
eval_metric='mae',
learning_rate=0.0001,
random_state=7)) ])
X_train = train_idxs['f_text'].values
y_train = train_idxs['Pct_Change_20'].values
安装管道示例:%time xgbr_pipe.fit(X_train, y_train,
xgbr__eval_test_size=200,
xgbr__eval_metric='mae',
xgbr__early_stopping_rounds=75)
示例拟合 GridSearchCV:learning_rate = [0.0001, 0.001, 0.01, 0.05, 0.1, 0.2, 0.3]
param_grid = dict(xgbr__learning_rate=learning_rate)
grid_search = GridSearchCV(xgbr_pipe, param_grid, scoring="neg_mean_absolute_error", n_jobs=-1, cv=10)
grid_result = grid_search.fit(X_train, y_train,
xgbr__eval_test_size=200,
xgbr__eval_metric='mae',
xgbr__early_stopping_rounds=75)
关于python-3.x - GridSearchCV - XGBoost - 提前停止,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/42993550/