from sklearn import metrics
# Utilizando um classificador Naive Bayes

from sklearn.naive_bayes import GaussianNB

# Criando o modelo preditivo

modelo_v1 = GaussianNB()

# Treinando o modelo

classificador = modelo_v1.fit(X_treino, Y_treino.ravel())

# Curve ROC - Algoritmo Naive Bayes
import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle

from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.preprocessing import label_binarize

y_score = classificador

# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(60202):
    fpr[i], tpr[i], _ = roc_curve(Y_teste[:, i], y_score[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])

# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(Y_teste.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])