title: Different AUC score from xgb and sklearn.metrics
body: <p>I'm using xgboost's sklearn wrapper for a binary classifcation task and then use sklearn.metrics' auc for scoring</p>
  
  <pre><code>df = preprocessing.MinMaxScaler().fit_transform(df)
  X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.33)#, random_state=7)
  
  params={
      'n_estimators':500,
      'objective': 'binary:logistic',
      'learning_rate': 0.05,
      'gamma':0.1,
      'subsample':0.8,
      'colsample_bytree':0.3,
      'min_child_weight':3,
      'max_depth':6,
      'seed':1024,
      }
  
  clf = XGBClassifier(**params)
  clf.fit(X_train, y_train, \
          eval_set=[(X_train, y_train), (X_test, y_test)], \
                    eval_metric='auc', early_stopping_rounds = 50)
  </code></pre>
  
  <p>the output gives the result as :</p>
  
  <pre><code>[499]   validation_0-auc:0.991635       validation_1-auc:0.939632
  </code></pre>
  
  <p>but when I use auc scoring function by following code:</p>
  
  <pre><code>y_pred = clf.predict(X_test)
  print metrics.roc_auc_score(y_test,y_pred)
  </code></pre>
  
  <p>the result is :</p>
  
  <pre><code>0.855832401454
  </code></pre>
  
  <p>anything wrong with my code?</p>