python 多分类画auc曲线和macro-average ROC curve
最近帮一个人做了一个多分类画auc曲线的东西，不过最后那个人不要了，还被说了一顿，心里很是不爽，anyway，我写代码的还是要继续写代码的，所以我准备把我修改的代码分享开来，供大家研究学习。处理的数据大概是这种xlsx文件：
IMAGE y_real y_predict 0其他 1豹纹 2弥漫 3斑片 4黄斑
/mnt/AI/HM/izy20200531c5/299/train/0其他/IM005111 (Copy).jpg 0 0 1 8.31E-19 7.59E-13 4.47E-15 2.46E-14
/mnt/AI/HM/izy20200531c5/299/train/0其他/IM005201 (Copy).jpg 0 0 1 5.35E-17 4.38E-11 8.80E-13 3.85E-11
/mnt/AI/HM/izy20200531c5/299/train/0其他/IM004938 (4) (Copy).jpg 0 0 1 1.20E-16 3.17E-11 6.26E-12 1.02E-11
/mnt/AI/HM/izy20200531c5/299/train/0其他/IM004349 (3) (Copy).jpg 0 0 1 5.66E-14 1.87E-09 6.50E-09 3.29E-09
/mnt/AI/HM/izy20200531c5/299/train/0其他/IM004673 (5) (Copy).jpg 0 0 1 5.51E-17 9.30E-12 1.33E-13 2.54E-12
/mnt/AI/HM/izy20200531c5/299/train/0其他/IM004450 (5) (Copy).jpg 0 0 1 4.81E-17 3.75E-12 3.96E-13 6.17E-13
- 导入基础的pandas和keras处理函数
import pandas as pd
from keras.utils import to_categorical- 导入数据
data=pd.read_excel('5分类新.xlsx')
data.head()- 导入机器学习库
from sklearn.metrics import precision_recall_curve
import numpy as np
from matplotlib import pyplot
from sklearn.metrics import f1_score
from sklearn.metrics import roc_curve, auc- 把ground truth提取出来
true_y=data[' y_real'].to_numpy()
true_y=to_categorical(true_y)- 把每个类别的数据提取出来
PM_y=data[[' 0其他',' 1豹纹',' 2弥漫',' 3斑片',' 4黄斑']].to_numpy()
PM_y.shape- 计算每个类别的fpr和tpr
n_classes=PM_y.shape[1]
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
fpr[i], tpr[i], _ = roc_curve(true_y[:, i], PM_y[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])- 计算macro auc
from scipy import interp
# 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"])- 画图
import matplotlib.pyplot as plt
from itertools import cycle
from matplotlib.ticker import FuncFormatter
lw = 2
# Plot all ROC curves
plt.figure()
labels=['Category 0','Category 1','Category 2','Category 3','Category 4']
plt.plot(fpr["macro"], tpr["macro"],
label='macro-average ROC curve (area = {0:0.4f})'
''.format(roc_auc["macro"]),
color='navy', linestyle=':', linewidth=4)
colors = cycle(['aqua', 'darkorange', 'cornflowerblue','blue','yellow'])
for i, color in zip(range(n_classes), colors):
plt.plot(fpr[i], tpr[i], color=color, lw=lw,
label=labels[i]+'(area = {0:0.4f})'.format(roc_auc[i]))
plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('1-Specificity (%)')
plt.ylabel('Sensitivity (%)')
plt.title('Some extension of Receiver operating characteristic to multi-class')
def to_percent(temp, position):
return '%1.0f'%(100*temp)
plt.gca().yaxis.set_major_formatter(FuncFormatter(to_percent))
plt.gca().xaxis.set_major_formatter(FuncFormatter(to_percent))
plt.legend(loc="lower right")
plt.show()-
展示
-
上述的代码是在jupyter中运行的，所以是分开的
参考文献
[1].ROC原理介绍及利用python实现二分类和多分类的ROC曲线. ROC原理介绍及利用python实现二分类和多分类的ROC曲线_山不过来，我就过去-CSDN博客_roc曲线python实现