ROC Curve Multiclass

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.metrics import roc_curve,auc
from scipy import interp
from itertools import cycle

iris = datasets.load_iris()
x = iris.data[:, 2:]
y = iris.target
x_test = x
y_test = y
n_classes=3
'''NAIVE BAYES'''

from sklearn.naive_bayes import GaussianNB
model=GaussianNB()
model.fit(x,y)
nb=model.score(x,y)

pred1=model.predict(x_test)
t1=sum(x==0 for x in pred1-y_test)/len(pred1)

### MACRO
fpr = dict()
tpr = dict()
roc_auc = dict()

for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(np.array(pd.get_dummies(y_test))[:, i], np.array(pd.get_dummies(pred1))[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])


all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))

mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
    mean_tpr += interp(all_fpr, fpr[i], tpr[i])

mean_tpr /= n_classes

fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

lw=2
plt.figure(figsize=(8,5))
plt.plot(fpr["macro"], tpr["macro"],
         label='macro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["macro"]),
         color='green', linestyle=':', linewidth=4)

colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
for i, color in zip(range(n_classes), colors):
    plt.plot(fpr[i], tpr[i], color=color, lw=lw,
             label='ROC curve of class {0} (area = {1:0.2f})'
             ''.format(i, roc_auc[i]))

plt.plot([0, 1], [0, 1], 'k--',color='red', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.annotate('Random Guess',(.5,.48),color='red')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic for Naive Bayes - IRIS DATASET')
plt.legend(loc="lower right")
plt.show()