test.py

import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle
from sklearn.metrics import roc_curve, auc
from scipy import interp
 
y_label = np.array([
[1, 0, 0], [1, 0, 0], [1, 0, 0],
[0, 1, 0], [0,  1, 0], [0, 1, 0],
[0, 0, 1], [0, 0, 1], [0, 0, 1]
])
 
y_score = np.array([
[0.8, 0.1, 0.1], [0.2, 0.32, 0.48], [0.6, 0.1, 0.3],
[0.2, 0.5, 0.3], [0.1, 0.6, 0.3], [0.2, 0.75, 0.05],
[0.05, 0.05, 0.9], [0.1, 0.3, 0.6], [0.12, 0.8, 0.08],
])
 
n_classes = 3
 
# 计算每一类的ROC
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y_label[:, i], y_score[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])
 
# micro（方法二）
fpr["micro"], tpr["micro"], _ = roc_curve(y_label.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
 
# macro（方法一）
# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))
# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
    mean_tpr += interp(all_fpr, fpr[i], tpr[i])
# Finally average it and compute AUC
mean_tpr /= n_classes
fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])
 
# Plot all ROC curves
lw=2
plt.figure()
 
plt.plot(fpr["macro"], tpr["macro"],
         label='macro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["macro"]),
         color='navy', linestyle=':', linewidth=4)
 
plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('multi-calss ROC')
plt.legend(loc="lower right")
plt.show()