10 fold plot ROC with many classifers python3.6

I want to apply ROC curve for 10 fold cross validation with two classifier in python.

I follow some code but I still have trouble to present mean of 10 fold that present two classifier one for decision tree and other for regression

Here is my code: as you know 10 fold will test 10 times I want to present average of 10 times as ROC curve for decision tree and regression

iris= datasets.load_iris()

X,y = iris.data[50:,[1,2]], iris.target[50:]

X_train, X_test,y_train,y_test=\

train_test_split(X,y,test_size=0.5,random_state=1)

#Run classifier with cross-validation and plot ROC curves

cv = StratifiedKFold(n_splits=10)

#classifier = svm.SVC(kernel='linear', probability=True,

classifier1 = DecisionTreeClassifier(max_depth=1,criterion='entropy',random_state=0) #random_state=random_state)

classifier2 = LogisticRegression()

classifier3=[classifier1,classifier2]

mean_tpr = 0.0

mean_fpr = np.linspace(0, 1, 100)

colors = cycle(['cyan', 'indigo'])

lw = 2

i = 0

for (train, test), color,classifier in zip(cv.split(X, y), colors,classifier3):

probas_ = classifier.fit(X[train], y[train]).predict_proba(X[test])

# Compute ROC curve and area the curve

fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])

mean_tpr += interp(mean_fpr, fpr, tpr)

mean_tpr[0] = 0.0

roc_auc = auc(fpr, tpr)

plt.plot(fpr, tpr, lw=lw, color=color,

label='ROC fold %d (area = %0.2f)' % (i, roc_auc))

i += 1

plt.plot([0, 1], [0, 1], linestyle='--', lw=lw, color='k',

label='Luck')

mean_tpr /= cv.get_n_splits(X, y)

mean_tpr[-1] = 1.0

mean_auc = auc(mean_fpr, mean_tpr)

plt.plot(mean_fpr, mean_tpr, color='g', linestyle='--',

label='Mean ROC (area = %0.2f)' % mean_auc, lw=lw)

plt.xlim([-0.05, 1.05])

plt.ylim([-0.05, 1.05])

plt.xlabel('False Positive Rate')

plt.ylabel('True Positive Rate')

plt.title('Receiver operating characteristic example')

plt.legend(loc="lower right")

plt.show()

10 r 0.8 0.6 0.4 0.2 0.0 0.0 Receiver operating characteristic example ROC fold 0 (area 0.900 ROC fold 1 (area 1.00) Luck Mean ROC (area 0.190 10 0.2 0.8 0.4 0.6 False Positive Rate