python多分类画roc曲线_基于python的多分类ROC曲线生成2

from sklearn.metrics import confusion_matrix, roc_curve, auc

from sklearn.preprocessing import label_binarize

import numpy as np

y_test_bi = label_binarize(y_test, classes=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18, 19,20,21,2,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,3,40,41,42,43,44,45])

y_pred_bi = label_binarize(y_pred, classes=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18, 19,20,21,2,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,3,40,41,42,43,44,45])

# Compute ROC curve and ROC area for each class

fpr = dict()

tpr = dict()

roc_auc = dict()

for i in range(2):

fpr[i], tpr[i], _ = roc_curve(y_test_bi, y_pred_bi)

roc_auc[i] = auc(fpr[i], tpr[i])

y_test_bi和{}的形状都是{}，因为有46个类和300个测试数据点。在

这两个矩阵的格式是每列代表一个类，并且由0s或{}s组成

但我得到了一个错误：

^{pr2}$