python 绘制声纹识别DET曲线

import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve
from scipy.stats import norm
import numpy as npdef plot_DET_curve():# 设置刻度范围pmiss_min = 0.001pmiss_max = 0.6  pfa_min = 0.001pfa_max = 0.6# 刻度设置pticks = [0.00001, 0.00002, 0.00005, 0.0001, 0.0002, 0.0005,0.001, 0.002, 0.005, 0.01, 0.02, 0.05,0.1, 0.2, 0.4, 0.6, 0.8, 0.9,0.95, 0.98, 0.99, 0.995, 0.998, 0.999,0.9995, 0.9998, 0.9999, 0.99995, 0.99998, 0.99999]# 刻度*100xlabels = [' 0.001', ' 0.002', ' 0.005', ' 0.01 ', ' 0.02 ', ' 0.05 ','  0.1 ', '  0.2 ', ' 0.5  ', '  1   ', '  2   ', '  5   ','  10  ', '  20  ', '  40  ', '  60  ', '  80  ', '  90  ','  95  ', '  98  ', '  99  ', ' 99.5 ', ' 99.8 ', ' 99.9 ',' 99.95', ' 99.98', ' 99.99', '99.995', '99.998', '99.999']ylabels = xlabels# 确定刻度范围n = len(pticks)# 倒叙 for k, v in enumerate(pticks[::-1]):if pmiss_min <= v:tmin_miss = n - k - 1   # 移动最小值索引位置if pfa_min <= v:tmin_fa = n - k - 1   # 移动最小值索引位置# 正序for k, v in enumerate(pticks):if pmiss_max >= v:   tmax_miss = k+1         # 移动最大值索引位置if pfa_max >= v:            tmax_fa = k+1            # 移动最大值索引位置# FRRplt.figure()plt.xlim(norm.ppf(pfa_min), norm.ppf(pfa_max))plt.xticks(norm.ppf(pticks[tmin_fa:tmax_fa]), xlabels[tmin_fa:tmax_fa])plt.xlabel('False Alarm probability (in %)')# FARplt.ylim(norm.ppf(pmiss_min), norm.ppf(pmiss_max))plt.yticks(norm.ppf(pticks[tmin_miss:tmax_miss]), ylabels[tmin_miss:tmax_miss])plt.ylabel('Miss probability (in %)')return plt# 计算EER
def compute_EER(frr,far):threshold_index = np.argmin(abs(frr - far))  # 平衡点eer = (frr[threshold_index]+far[threshold_index])/2print("eer=",eer)return eer# 计算minDCF P_miss = frr  P_fa = far
def compute_minDCF2(P_miss,P_fa):C_miss = C_fa = 1P_true = 0.01P_false = 1-P_truenpts = len(P_miss)if npts != len(P_fa):print("error,size of Pmiss is not euqal to pfa")DCF = C_miss * P_miss * P_true + C_fa * P_fa*P_falsemin_DCF = min(DCF)print("min_DCF_2=",min_DCF)return min_DCF# 计算minDCF P_miss = frr  P_fa = far
def compute_minDCF3(P_miss,P_fa,min_DCF_2):C_miss = C_fa = 1P_true = 0.001P_false = 1-P_truenpts = len(P_miss)if npts != len(P_fa):print("error,size of Pmiss is not euqal to pfa")DCF = C_miss * P_miss * P_true + C_fa * P_fa*P_false# 该操作是我自己加的，因为论文中的DCF10-3指标均大于DCF10-2且高于0.1以上，所以通过这个来过滤一下,错误请指正min_DCF = 1for dcf in DCF:if dcf > min_DCF_2+0.1 and dcf < min_DCF:min_DCF = dcfprint("min_DCF_3=",min_DCF)return min_DCFif __name__ == "__main__":# 读文件获取y_true和y_scorey_true = np.load('./dataset/y_true.npy')y_score = np.load('./dataset/y_pre.npy')# 计算FAR和FRRfpr, tpr, thres = roc_curve(y_true, y_score)frr = 1 - tprfar = fprfrr[frr <= 0] = 1e-5far[far <= 0] = 1e-5frr[frr >= 1] = 1-1e-5far[far >= 1] = 1-1e-5# 画图plt = plot_DET_curve()x, y = norm.ppf(frr), norm.ppf(far)plt.plot(x, y)plt.plot([-40, 1], [-40, 1])# plt.plot(np.arange(0,40,1),np.arange(0,40,1))plt.show()eer = compute_EER(frr,far)min_DCF_2 = compute_minDCF2(frr*100,far*100)min_DCF_3 = compute_minDCF3(frr*100,far*100,min_DCF_2)

效果图：