sklearn里计算roc_auc_score，报错ValueError: bad input shape

用sklearn的DecisionTreeClassifer训练模型，然后用roc_auc_score计算模型的auc。代码如下

clf = DecisionTreeClassifier(criterion='gini', max_depth=6, min_samples_split=10, min_samples_leaf=2)
clf.fit(X_train, y_train)
y_pred = clf.predict_proba(X_test)
roc_auc = roc_auc_score(y_test, y_pred)

报错信息如下

/Users/wgg/anaconda/lib/python2.7/site-packages/sklearn/metrics/ranking.pyc in _binary_clf_curve(y_true, y_score, pos_label, sample_weight)
    297     check_consistent_length(y_true, y_score)
    298     y_true = column_or_1d(y_true)
--> 299     y_score = column_or_1d(y_score)
    300     assert_all_finite(y_true)
    301     assert_all_finite(y_score)

/Users/wgg/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.pyc in column_or_1d(y, warn)
    560         return np.ravel(y)
    561 
--> 562     raise ValueError("bad input shape {0}".format(shape))
    563 
    564 

ValueError: bad input shape (900, 2)

这个是怎么回事？有人遇到过吗？