参考这里：
https://blog.csdn.net/zjm750617105/article/details/52558779

我的1.txt长这个样子：（可参考local/prepare_for_eer.py，但我是用excel弄出来的，哈哈），一共14232行

5.1663/target
-32.37284/nontarget
-38.94157/nontarget
-58.89211/nontarget
-69.29233/nontarget

下面先用kaldi的方法计算一下eer：

def read_file(filepath):with open(filepath, 'r') as f:lines = f.readlines()return linesscore_path = r"E:\share_with_ubuntu\aishell-v1\1.txt"
score_lines = read_file(score_path)
target_scores = []
nontarget_scores = []
for line in score_lines:splits = line.strip().split('/')if splits[1] == 'target':target_scores.append(eval(splits[0]))else:nontarget_scores.append(eval(splits[0]))
print(len(target_scores),len(nontarget_scores))

结果：

7116 135204

kaldi中对于阈值的选择是对target_scores的每一个分数进行迭代，也就是把每一个target_scores中的分数都当做阈值来算一遍。但是也有文章说，直接在target_scores的最小值和最大值之间取10000个点，把每一个点都当做阈值进行迭代。设一万个点的可以参考下面：
https://blog.csdn.net/zjm750617105/article/details/60503253

#从小到大排序
target_scores = sorted(target_scores)
nontarget_scores = sorted(nontarget_scores)# 下面是kaldi中compute-eer的方法
target_size = len(target_scores)
target_position = 0
for target_position in range(target_size):nontarget_size = len(nontarget_scores)nontarget_n = nontarget_size * target_position * 1.0 / target_sizenontarget_position = int(nontarget_size - 1 - nontarget_n)if nontarget_position < 0:nontarget_position = 0if nontarget_scores[nontarget_position] < target_scores[target_position]:print ("nontarget_scores[nontarget_position] is",  nontarget_position, nontarget_scores[nontarget_position])print ("target_scores[target_position] is",  target_position, target_scores[target_position])breakthreshold = target_scores[target_position]
print ("threshold is --> ",  threshold)
eer = target_position * 1.0 / target_size
print ("eer is --> ",  eer)

结果：

nontarget_scores[nontarget_position] is 133246 -13.30128
target_scores[target_position] is 103 -13.16873
threshold is -->  -13.16873
eer is -->  0.01447442383361439

但是我不太理解上面的方法，根据下面kaldi中的注释，我们来自己写一下python代码。
ComputeEer computes the Equal Error Rate (EER) for the given scores
and returns it as a proportion beween 0 and 1.
If we set the threshold at x, then the target error-rate is the
proportion of target_scores below x; and the non-target error-rate
is the proportion of non-target scores above x. We seek a
threshold x for which these error rates are the same; this
error rate is the EER.
We compute this by iterating over the positions in target_scores: 0, 1, 2,
and so on, and for each position consider whether the cutoff could be here.
For each of these position we compute the corresponding position in
nontarget_scores where the cutoff would be if the EER were the same.
For instance, if the vectors had the same length, this would be position
length() - 1, length() - 2, and so on. As soon as the value at that
position in nontarget_scores at that position is less than the value from target_scores, we have our EER.

这里再来明确一下FR和FA的定义：
FR = 所有真正例中被判为负例的数目 / 所有真正例的数目
FA = 所有真负例中被判为正例的数目 / 所有真负例的数目

阈值一旦设定，所有小于阈值的都被预测为负例，所有大于阈值的都被预测为正例
这里（真实值中）所有正例的数目就是7116，所有负例的数目就是135204
由于FR和FA不可能完全相等，下面计算他们差的绝对值的最小值

def count_xiaoyu_x(list1,x): # 要求list1是从小到大排列的num =0for i in range(len(list1)):if (list1[i]<x):num+=1else:breakreturn numdef count_dayu_x(list1,x): # 要求list1是从小到大排列的num =0length = len(list1)for i in range(length):if (list1[length-i-1]>x):num+=1else:breakreturn numfr_list = []
fa_list = []
cha_list = []
for position in range(7116):FR = count_xiaoyu_x(target_scores,target_scores[position])/7116FA = count_dayu_x(nontarget_scores,target_scores[position])/135204cha = abs(FR-FA)fr_list.append(FR)fa_list.append(FA)cha_list.append(cha)a = min(cha_list)
b = cha_list.index(a)
print(target_scores[b],fr_list[b],fa_list[b],'%.10f'%a)

结果：

-13.22655 0.01433389544688027 0.014363480370403242 0.0000295849

这里发现与kaldi的结果不太一样，刚好是kaldi结果的上一个点
这个cha的变化趋势是怎样的呢，是不是只有一个最低点呢，画个图看看

import numpy as np
import matplotlib.pyplot as pltfig, ax = plt.subplots()
plt.xlabel('threshhold')
plt.ylabel('cha')  ax.set_xlim([min(target_scores), max(target_scores)])
ax.set_ylim([min(cha_list), max(cha_list)])plt.plot(target_scores,cha_list,'.',label=" cha with threshold")plt.legend(loc='upper right')
plt.show()

确实只有一个最低点，下面这个才是kaldi的结果

print(target_scores[b+1],fr_list[b+1],fa_list[b+1],'%.10f'%cha_list[b+1])

结果：

-13.16873 0.01447442383361439 0.014274725599834325 0.0001996982

比较两个cha，0.0000295849和0.0001996982，我的结果确实更小一些呢。

现在来画fa和fr随阈值变化的曲线，最好能从曲线上就找到FR和FA离的最近的两个点

#!coding=utf-8
import numpy as np
import matplotlib.pyplot as pltfig, ax = plt.subplots()
plt.title(" curve")
plt.xlabel('threshhold')
plt.ylabel('y')  ax.set_xlim([min(target_scores), max(target_scores)])
ax.set_ylim([0, 1])plt.plot(target_scores,fr_list,'.',label=" FR with threshold")
plt.plot(target_scores,fa_list,'.',label=" FA with threshold")plt.legend(loc='upper right')
plt.show()

上面的图太不清楚了，怎么能从图上就找到这个交点呢，更改刻度再画一次

a=[]
b=[]
c=[]for i in range(len(target_scores)):if((-20<target_scores[i]) & (target_scores[i]<-10)):a.append(target_scores[i])b.append(fr_list[i])c.append(fa_list[i])
print(len(a),len(b),len(c))import numpy as np
import matplotlib.pyplot as pltfig, ax = plt.subplots()
plt.title(" curve")
plt.xlabel('threshhold')
plt.ylabel('y')  ax.set_xlim([-20, -10])
ax.set_ylim([0, 0.1])plt.plot(a,b,'.',label="curve of FR with threshold")
plt.plot(a,c,'.',label="curve of FA with threshold")plt.legend(loc='upper right')
plt.show()

结果：

# 再画一次
a=[]
b=[]
c=[]for i in range(len(target_scores)):if((-14<target_scores[i]) & (target_scores[i]<-12)):a.append(target_scores[i])b.append(fr_list[i])c.append(fa_list[i])print(len(a),len(b),len(c))plt.title(" curve")
plt.xlabel('threshhold')
plt.ylabel('y')  ax.set_xlim([-14, -12])
ax.set_ylim([0, 0.04])plt.plot(a,b,'.',label="curve of FR with threshold")
plt.plot(a,c,'.',label="curve of FA with threshold")plt.legend(loc='upper right')
plt.show()
# 这时有38个点，还是不太清楚

# 再画一次
a=[]
b=[]
c=[]for i in range(len(target_scores)):if((-13.5<target_scores[i]) & (target_scores[i]<-13)):a.append(target_scores[i])b.append(fr_list[i])c.append(fa_list[i])print(len(a),len(b),len(c))plt.title(" curve")
plt.xlabel('threshhold')
plt.ylabel('y')  ax.set_xlim([-13.5, -13])
ax.set_ylim([0.013, 0.016])plt.plot(a,b,'.',label="curve of FR with threshold")
plt.plot(a,c,'.',label="curve of FA with threshold")plt.legend(loc='upper right')
plt.show()

这时只有6个点，可以很清楚的看到第4个点，黄色和蓝色是离的最近的

print(a[3],b[3],c[3])
# 打印出来的阈值，fr，fa和我们上面的计算结果是一样的

结果：

-13.22655 0.01433389544688027 0.014363480370403242

下面画DET曲线，横轴是fa，纵轴是fr

# 先来看下坐标范围
print(min(fa_list),max(fa_list))
print(min(fr_list),max(fr_list))
print(len(fa_list),len(fr_list))

0.0 0.07365906334132126
0.0 0.9998594716132658
7116 7116

# 下面画DET曲线，横轴是fa，纵轴是fr
plt.xlabel('fa')
plt.ylabel('fr')  ax.set_xlim([0, max(fa_list)])
ax.set_ylim([0, max(fr_list)])plt.plot(fa_list,fr_list,'.',label="curve of DET")plt.legend(loc='upper right')
plt.show()

怎么把我们找到的eer的值标记上去呢
参考：python显示图上每个点的坐标：https://zhidao.baidu.com/question/1388281986500209380.html
参考：Python:给图形中添加文本注释（text函数）：https://blog.csdn.net/weixin_38725737/article/details/82664096

# 怎么把我们找到的eer的值标记上去呢
# 参考：python显示图上每个点的坐标：https://zhidao.baidu.com/question/1388281986500209380.html
# 参考：Python:给图形中添加文本注释（text函数）：https://blog.csdn.net/weixin_38725737/article/details/82664096plt.xlabel('fa')
plt.ylabel('fr')  ax.set_xlim([0, max(fa_list)])
ax.set_ylim([0, max(fr_list)])plt.plot(fa_list,fr_list,'.',label="curve of DET")
plt.text(0.01, 0.2, "EER=0.01433", size = 15, alpha = 0.7)plt.legend(loc='upper right')
plt.show()

但是下面这个曲线与y=x的交点处横坐标，明显小于0.01啊，为什么呢，难道是因为7116个点太密了？

那再放大一点看细节

m=[]
n=[]for i in range(len(fa_list)):if((0<fa_list[i]) & (fa_list[i]<0.002)):m.append(fa_list[i])n.append(fr_list[i])print(len(m),len(n))plt.title(" curve")
plt.xlabel('fa')
plt.ylabel('fr')  ax.set_xlim([0,0.002])
ax.set_ylim([0,0.2])plt.plot(m,n,'.',label="curve of DET")plt.legend(loc='upper right')
plt.show()

本页还有两个问题没解决：
1.如何理解kaldi中的方法？
2.为啥最后这个曲线与y=x的交点处横坐标，明显小于0.01啊？
坐标应该是（0.01433389544688027 0.014363480370403242）啊