解析ROC曲线绘制(python+sklearn+多分类)

from sklearn.metrics import roc_curve, auc
fpr, tpr, thresholds = roc_curve(y_true,y_sore)
roc_auc = auc(fpr, tpr)
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, '#9400D3',label=u'AUC = %0.3f'% roc_auc)

plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.1])
plt.ylim([-0.1,1.1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.grid(linestyle='-.')  
plt.grid(True)
plt.show()
print(roc_auc)

import numpy as np
import matplotlib.pyplot as plt
from scipy import interp
from sklearn.preprocessing import label_binarize
from sklearn.metrics import confusion_matrix,classification_report
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import cohen_kappa_score, accuracy_score

fpr0, tpr0, thresholds0 = roc_curve(y_true0,y_sore0)
fpr1, tpr1, thresholds1 = roc_curve(y_true1,y_sore1)
fpr2, tpr2, thresholds2 = roc_curve(y_true2,y_sore2)
fpr3, tpr3, thresholds3 = roc_curve(y_true3,y_sore3)
fpr4, tpr4, thresholds4 = roc_curve(y_true4,y_sore4)


roc_auc0 = auc(fpr0, tpr0)
roc_auc1 = auc(fpr1, tpr1)
roc_auc2 = auc(fpr2, tpr2)
roc_auc3 = auc(fpr3, tpr3)
roc_auc4 = auc(fpr4, tpr4)

plt.title('Receiver Operating Characteristic')
plt.rcParams['figure.figsize'] = (10.0, 10.0) 
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False     
# 设置标题大小
plt.rcParams['font.size'] = '16'
plt.plot(fpr0, tpr0, 'k-',color='k',linestyle='-.',linewidth=3,markerfacecolor='none',label=u'AA_AUC = %0.5f'% roc_auc0)
plt.plot(fpr1, tpr1, 'k-',color='grey',linestyle='-.',linewidth=3,label=u'A_AUC = %0.5f'% roc_auc1)
plt.plot(fpr2, tpr2, 'k-',color='r',linestyle='-.',linewidth=3,markerfacecolor='none',label=u'B_AUC = %0.5f'% roc_auc2)
plt.plot(fpr3, tpr3, 'k-',color='red',linestyle='-.',linewidth=3,markerfacecolor='none',label=u'C_AUC = %0.5f'% roc_auc3)
plt.plot(fpr4, tpr4, 'k-',color='y',linestyle='-.',linewidth=3,markerfacecolor='none',label=u'D_AUC = %0.5f'% roc_auc4)

plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.1])
plt.ylim([-0.1,1.1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.grid(linestyle='-.')  
plt.grid(True)
plt.show()

y_test_all = label_binarize(true_labels_i, classes=[0,1,2,3,4])

y_score_all=test_Y_i_hat
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(len(classes)):
    fpr[i], tpr[i], thresholds = roc_curve(y_test_all[:, i],y_score_all[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])

# micro-average ROC curve（方法一）
fpr["micro"], tpr["micro"], thresholds = roc_curve(y_test_all.ravel(),y_score_all.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

# macro-average ROC curve 方法二）

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

mean_tpr = np.zeros_like(all_fpr)
for i in range(len(classes)):
    mean_tpr += interp(all_fpr, fpr[i], tpr[i])
# 求平均计算ROC包围的面积AUC
mean_tpr /= len(classes)
fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

#画图部分
plt.figure()
plt.plot(fpr["micro"], tpr["micro"],'k-',color='y',
         label='XXXX ROC curve micro-average(AUC = {0:0.4f})'
               ''.format(roc_auc["micro"]),
          linestyle='-.', linewidth=3)

plt.plot(fpr["macro"], tpr["macro"],'k-',color='k',
         label='XXXX ROC curve macro-average(AUC = {0:0.4f})'
               ''.format(roc_auc["macro"]),
          linestyle='-.', linewidth=3)
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.1])
plt.ylim([-0.1,1.1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.legend(loc="lower right")
plt.grid(linestyle='-.')  
plt.grid(True)
plt.show()