ROC plot

def plotROC(X_train_res, y_train_res, X_test, y_test):
    cl = MultinomialNB()
    cl.fit(X_train_res, y_train_res)
    res = cl.predict_proba(X_test)



    x = np.transpose(res)[0]
    rc = roc_curve(y_test, x)
    print(y_test[:5])
    print(x[:5])
    aucl = auc(rc[0], rc[1])



    lw=2
    plt.plot(rc[0],rc[1], color='darkorange',
                        lw=lw, label='ROC curve (area = %0.2f)' % aucl)
    #plt.plot(rc2[0],rc2[1], color='deeppink',
    #                    lw=lw, label='ROC curve (area = %0.2f)' % aucl)
    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 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()