问在Python中手工计算AUC

EN

test = data.frame(cbind(dt$DV, predicted_prob))
colnames(test)[1] = 'DV' 
colnames(test)[2] = 'DV_pred_prob' 

TP = rep(NA,101)
FN = rep(NA,101)
FP = rep(NA,101)
TN = rep(NA,101)
Sensitivity = rep(NA,101)
Specificity = rep(NA,101)
AUROC = 0

for(i in 0:100){
  test$temp = 0
  test[test$DV_pred_prob > (i/100),"temp"] = 1
  TP[i+1] = nrow(test[test$DV==1 & test$temp==1,])
  FN[i+1] = nrow(test[test$DV==1 & test$temp==0,])
  FP[i+1] = nrow(test[test$DV==0 & test$temp==1,])
  TN[i+1] = nrow(test[test$DV==0 & test$temp==0,])
  Sensitivity[i+1] = TP[i+1] / (TP[i+1] + FN[i+1] )
  Specificity[i+1] = TN[i+1] / (TN[i+1] + FP[i+1] )
  if(i>0){
    AUROC = AUROC+0.5*(Specificity[i+1] - Specificity[i])*(Sensitivity[i+1] + 
Sensitivity[i])
  }
}

data = data.frame(cbind(Sensitivity,Specificity,id=(0:100)/100))

predictions = pd.DataFrame(predictions[1])
actual = pd.DataFrame(y_test)
test = pd.concat([actual.reset_index(drop=True), predictions], axis=1)
# Rename column Renew to 'actual' and '1' to 'predictions'
test.rename(columns={"Renew": "actual", 1: "predictions"}, inplace=True)

TP = np.repeat('NA', 101)
FN = np.repeat('NA', 101)
FP = np.repeat('NA', 101)
TN = np.repeat('NA', 101)
Sensitivity = np.repeat('NA', 101)
Specificity = np.repeat('NA', 101)
AUROC = 0

for i in range(100):
    test['temp'] = 0
    test[test['predictions'] > (i/100), "temp"] = 1
    TP[i+1] = [test[test["actual"]==1 and test["temp"]==1,]].shape[0]
    FN[i+1] = [test[test["actual"]==1 and test["temp"]==0,]].shape[0]
    FP[i+1] = [test[test["actual"]==0 and test["temp"]==1,]].shape[0]
    TN[i+1] = [test[test["actual"]==0 and test["temp"]==0,]].shape[0]
    Sensitivity[i+1] = TP[i+1] / (TP[i+1] + FN[i+1])
    Specificity[i+1] = TN[i+1] / (TN[i+1] + FP[i+1])
    if(i > 0):
            AUROC = AUROC+0.5*(Specificity[i+1] - Specificity[i])* 
(Sensitivity[i+1] + Sensitivity[i])