Scikit learn optunity中的精度度量返回全零？

我在Ubuntu上使用scikit。我能够运行此SVM分类示例：

这将返回roc_auc度量，我对准确性感兴趣。为了测试它，我尝试替换：

return optunity.metrics.roc_auc(y_test, decision_values)

与：

在下面的函数中：

def svm_tuned_auroc(x_train, y_train, x_test, y_test, kernel='linear', C=0, logGamma=0, degree=0, coef0=0):
    model = train_model(x_train, y_train, kernel, C, logGamma, degree, coef0)
    decision_values = model.decision_function(x_test)
    return optunity.metrics.roc_auc(y_test, decision_values)

但出乎意料的是，所有参数组合的精度指标都是0。我肯定我只是做错了什么。。。一个有效的例子会很有帮助

def svm_tuned_auroc(x_train, y_train, x_test, y_test, kernel='linear', C=0, logGamma=0, degree=0, coef0=0):
    model = train_model(x_train, y_train, kernel, C, logGamma, degree, coef0)
    decision_values = model.decision_function(x_test)
    return optunity.metrics.roc_auc(y_test, decision_values)