我正在尝试使用从http://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/改编的以下代码在XGBC分类器上执行多类分类问题的交叉验证

import numpy as np
import pandas as pd
import xgboost as xgb
from xgboost.sklearn import  XGBClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn import cross_validation,metrics
from sklearn.grid_search import GridSearchCV


def modelFit(alg,X,y,useTrainCV=True,cvFolds=5,early_stopping_rounds=50):
    if useTrainCV:
        xgbParams = alg.get_xgb_params()
        xgTrain = xgb.DMatrix(X,label=y)
        cvresult = xgb.cv(xgbParams,xgTrain,num_boost_round=alg.get_params()['n_estimators'],nfold=cvFolds,stratified=True,metrics={'mlogloss'},early_stopping_rounds=early_stopping_rounds,seed=0,callbacks=[xgb.callback.print_evaluation(show_stdv=False),xgb.callback.early_stop(3)])

        print cvresult
        alg.set_params(n_estimators=cvresult.shape[0])

    # Fit the algorithm
    alg.fit(X,eval_metric='mlogloss')

    # Predict
    dtrainPredictions = alg.predict(X)
    dtrainPredProb = alg.predict_proba(X)

    # Print model report:
    print "\nModel Report"
    print "Classification report: \n"
    print(classification_report(y_val,y_val_pred))
    print "Accuracy : %.4g" % metrics.accuracy_score(y,dtrainPredictions)
    print "Log Loss score (Train): %f" % metrics.log_loss(y,dtrainPredProb)
    feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)
    feat_imp.plot(kind='bar',title='Feature Importances')
    plt.ylabel('Feature Importance score')


# 1) Read training set
print('>> Read training set')
train = pd.read_csv(trainFile)

# 2) Extract target attribute and convert to numeric
print('>> Preprocessing')
y_train = train['OutcomeType'].values
le_y = LabelEncoder()
y_train = le_y.fit_transform(y_train)
train.drop('OutcomeType',axis=1,inplace=True)

# 4) Extract features and target from training set
X_train = train.values

# 5) First classifier
xgb = XGBClassifier(learning_rate =0.1,n_estimators=1000,max_depth=5,min_child_weight=1,gamma=0,subsample=0.8,colsample_bytree=0.8,scale_pos_weight=1,objective='multi:softprob',seed=27)

modelFit(xgb,X_train,y_train)

其中y_train包含从0到4的标签.但是,当我运行此代码时,我从xgb.cv函数xgboost.core.XGBoostError得到以下错误：参数num_class的值0应该大于等于1.在XGBoost doc上我读了在多类情况下,xgb从目标向量中的标签中推断出类的数量,所以我不明白发生了什么.