XGBClasifier和GridSearchCV / cross_val_score：得分为“ neg_log_loss”的问题

如何解决XGBClasifier和GridSearchCV / cross_val_score：得分为“ neg_log_loss”的问题

我正在XGBClassifier上执行GridSearchCV，并且要尽早停止，我想将“ neg_log_loss”用作评分函数。如果我运行以下代码：

from xgboost import XGBClassifier
xgb_clsf = XGBClassifier()

X_train,X_val,y_train,y_val = train_test_split(dataset_prepared_stand,dataset_labels,random_state=42)

param_grid = {
    'n_estimators': [2000],'learning_rate': [0.05,0.5,1.],'max_depth': [5,10,20],}

fit_params={"early_stopping_rounds": 250,"eval_metric": "logloss","eval_set": [[X_val,y_val]],"verbose": 0}

scores = ['neg_log_loss','roc_auc','accuracy','f1']

grid_search_xgb_clsf = GridSearchCV(xgb_clsf,param_grid,cv=KFold(n_splits=3,random_state=42,shuffle=True),scoring=scores,refit='neg_log_loss',return_train_score=True,verbose=100)

grid_search_xgb_clsf.fit(X_train,**fit_params)

我收到以下错误：

 RuntimeWarning: divide by zero encountered in logloss = -(transformed_labels * np.log(y_pred)).sum(axis=1)
 RuntimeWarning: invalid value encountered in multiply loss = -(transformed_labels * np.log(y_pred)).sum(axis=1)

为了避免发生此错误，我尝试用scores变量代替度量log_loss来定义负对数丢失，并且该变量有效（未显示错误）：

def _score_func(estimator,X,y):
    score = log_loss(y,estimator.predict_proba(X))
    return -score
scores = {'neg_log_loss': _score_func,'roc_auc': make_scorer(roc_auc_score),'accuracy': make_scorer(accuracy_score),'f1': make_scorer(f1_score)}

这是一个错误还是我做错了？我想保持相同的行为，以避免为XGBClassifier定义与其他模型不同的函数。 如果我只是在评分中使用“ neg_log_loss”运行cross_validate，就会发生同样的情况。