如何使用XGboost优化sklearn管道,用于不同的`eval_metric`？

我试图用XGBoost,优化eval_metric的auc(如描述在这里).

这在直接使用分类器时工作正常,但在我尝试将其用作管道时失败.

将.fit参数传递给sklearn管道的正确方法是什么？

例:

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_iris
from xgboost import XGBClassifier
import xgboost
import sklearn

print('sklearn version: %s' % sklearn.__version__)
print('xgboost version: %s' % xgboost.__version__)

X, y = load_iris(return_X_y=True)

# Without using the pipeline: 
xgb = XGBClassifier()
xgb.fit(X, y, eval_metric='auc')  # works fine

# Making a pipeline with this classifier and a scaler:
pipe = Pipeline([('scaler', StandardScaler()), ('classifier', XGBClassifier())])

# using the pipeline, but not optimizing for 'auc': 
pipe.fit(X, y)  # works fine

# however this does not work (even after correcting the underscores): 
pipe.fit(X, y, classifier__eval_metric='auc')  # fails

错误:
TypeError: before_fit() got an unexpected keyword argument 'classifier__eval_metric'

关于xgboost的版本:
xgboost.__version__显示0.6
pip3 freeze | grep xgboost显示xgboost==0.6a2.