单条PR曲线的绘制及相关参数(BEP,AP)的输出

# 姓   名:耿大哥讲算法
# 开发时间:2023/7/4 22:43
import matplotlib.pyplot as plt
from sklearn.metrics import precision_recall_curve,auc
import matplotlib;matplotlib.rc("font",family='Microsoft YaHei')

label=[0  ,1  ,0  ,1  ,1  ,1  ,0  ,0  ,1  ,0  ,0  ,1]
score=[0.2,0.5,0.8,0.6,0.4,0.2,0.9,0.7,0.6,0.5,0.1,0.3]

if label.count(0)==len(label):print('真实类别全为负！')
elif label.count(1)==len(label):print('真实类别全为正！')
else:
   yuzhi=list(set(score))
   yuzhi.sort(reverse=True)
   if 1 not in yuzhi:yuzhi.insert(0,1)
   else:pass
   #不用工具包求Precision,Recall,yuzhi
   Recall,Precision=[],[]
   for k in yuzhi:
       TP,FP=0,0
       for i in range(len(label)):
           if label[i]==1 and score[i]>=k:TP+=1
           elif label[i]==0 and score[i]>=k:FP+=1
           else:pass
       Recall.append(TP/label.count(1))
       if TP+FP==0:Precision.append(1)
       else:Precision.append(TP/(TP+FP))
   #用工具包求Precision,Recall,yuzhi
   #Precision,Recall,yuzhi=precision_recall_curve(label,score)
   X,Y,jiaodian_x=Recall,Precision,[]
   for i in range(len(X)-1):
       if Y[i]>X[i]    and Y[i+1]==X[i+1]:jiaodian_x.append(X[i+1])
       elif Y[i]>X[i]  and Y[i+1]<X[i+1]:
           if X[i]==X[i+1]:jiaodian_x.append(X[i])
           else:jiaodian_x.append((X[i]*Y[i+1]-Y[i]*X[i+1])/(Y[i+1]-Y[i]-X[i+1]+X[i]))
       elif Y[i]==X[i] and Y[i+1]>X[i+1]:jiaodian_x.append(X[i])
       elif Y[i]==X[i] and Y[i+1]==X[i+1]:jiaodian_x.append(X[i+1]);jiaodian_x.append(X[i])
       elif Y[i]==X[i] and Y[i+1]<X[i+1]:jiaodian_x.append(X[i])
       elif Y[i]<X[i]  and Y[i+1]>X[i+1]:
           if X[i]==X[i+1]:jiaodian_x.append(X[i])
           else:jiaodian_x.append((X[i]*Y[i+1]-Y[i]*X[i+1])/(Y[i+1]-Y[i]-X[i+1]+X[i]))
       elif Y[i]<X[i]  and Y[i+1]==X[i+1]:jiaodian_x.append(X[i+1])
       else:pass
   jiaodian_x=list(set(jiaodian_x))
   plt.plot(X,Y,'b*-',label='PR曲线(AP='+str('%.3f'%auc(X,Y))+')')
   for i in jiaodian_x:plt.text(i-0.05,i+0.05,'%.3f'%i)
   plt.plot([0,1],[0,1],'*--',color='black',label='45°线')
   plt.plot(jiaodian_x,jiaodian_x,'ro')
   plt.xlabel('Recall');plt.ylabel('Precision');plt.legend();plt.show()