我正在使用scikit-learn训练一些分类器.我做交叉验证,然后计算AUC.但是,每次运行测试时,我都会获得不同的AUC编号,尽管我确保使用种子和RandomState.我希望我的测试是确定性的.这是我的代码：

from sklearn.utils import shuffle
SEED = 0
random_state = np.random.RandomState(SEED)
X,y = shuffle(data,Y,random_state=random_state)
X_train,X_test,y_train,y_test = \
        cross_validation.train_test_split(X,y,test_size=test_size,random_state=random_state)
clf = linear_model.LogisticRegression()
kfold = cross_validation.KFold(len(X),n_folds=n_folds)
mean_tpr = 0.0
mean_fpr = np.linspace(0,1,100)

for train,test in kfold:
        probas_ = clf.fit(X[train],Y[train]).predict_proba(X[test])
        fpr,tpr,thresholds = roc_curve(Y[test],probas_[:,1])
        mean_tpr += interp(mean_fpr,fpr,tpr)
        mean_tpr[0] = 0.0

mean_tpr /= len(kfold)
mean_tpr[-1] = 1.0
mean_auc = auc(mean_fpr,mean_tpr)

我的问题：
1-我的代码中是否有错误导致每次运行时结果都不同？
2-是否存在使scikit具有确定性的全局方法？

编辑：

我刚试过这个：

test_size = 0.5
X = np.random.randint(10,size=(10,2))
Y = np.random.randint(2,size=(10))
SEED = 0
random_state = np.random.RandomState(SEED)
X_train,y_test = \
    cross_validation.train_test_split(X,random_state=random_state)

print X_train # I recorded the result

然后我做了：

X_train,random_state=6) #notice the change in random_state

然后我做了：

X_train,random_state=random_state)

print X_train #the result is different from the first one!!!!

如你所见,虽然我使用了相同的random_state,但我得到了不同的结果！怎么解决这个？