在评判分类器性能的时候，做类似LFW数据的一堆正负人脸对，使用k-fold方法输出最终的识别精度的python代码：
#coding : utf-8
import os
import sys
import csv
import numpy as np
def getAccu(score_list, begin, end, medium, tmp_thres):
accu = 0.0
issame = True
counter = 0
for i in range(begin, end):
if i < medium:
issame = True
else:
issame = False
if (score_list[i] >= tmp_thres) == issame:
counter += 1
accu = counter * 1.0 / (end - begin)
return accu
def findBestThres(k, batch, score_list):
begin = k * batch
end = (k + 1) * batch
medium = (begin + end) * 0.5
thres_range = np.arange(0.2, 0.99, 0.01)
best_thres = 0.0
best_accu = 0.0
for tmp_thres in thres_range:
tmp_accu = getAccu(score_list, begin, end, medium, tmp_thres)
if tmp_accu >= best_accu:
best_accu = tmp_accu
best_thres = tmp_thres
return best_thres, best_accu
def readCSV(path):
score_list = []
csv_file = csv.reader((open(path,'r')))
for line in csv_file:
score_list.append(float(line[5]))
return score_list
def testOtherAccu(k, score_list, folds, batch, best_thres):
counter = 0
issame = True
accu = 0.0
# left 0 ~ k
for i in range(k):
begin = i * batch
end = (i + 1) * batch
medium = (begin + end) * 0.5
for j in range(begin, end):
if j < medium:
issame = True
else:
issame = False
if (score_list[j] >= best_thres) == issame:
counter += 1
for m in range(k + 1, folds):
begin = m * batch
end = (m + 1) * batch
medium = (begin + end) * 0.5
for n in range(begin, end):
if n < medium:
issame = True
else:
issame = False
if (score_list[n] >= best_thres) == issame:
counter += 1
accu = counter * 1.0 / ((folds - 1) * batch)
return accu
if __name__ == '__main__':
# param ini
batch = 2400
folds = 10
# read csv file
score_list = readCSV('C:/Users/Administrator/Desktop/FaceOnline/kuangshi_score.csv')
sum_accu = 0.0
for k in range(folds):
best_thres, best_accu = findBestThres(k, batch, score_list)
other_accu = testOtherAccu(k, score_list, folds, batch, best_thres)
sum_accu += other_accu
print('k=%d, best_thres=%f, other_accu=%f\n'%(k, best_thres, other_accu))
ave_accu = sum_accu / folds
print('ave_accu=%f'% ave_accu)
