本文介绍了使用R中的pROC使用单个阈值和0.5的阈值梯度可改变灵敏度和特异性的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在尝试为多类图像模型计算ROC。但是由于找不到最佳的多类分类方法，因此我将其转换为二进制类。我有31类图片。我试图使用二进制方法分别查找每个31个类的ROC。
I am trying to calculate ROC for a model of multi-class image. But since I didn't find any best way for multi-class classification, I have converted it to binary class. I have 31 classes of image. Using binary methods I am trying to find ROC of each 31 classes individually.
df <- read.xlsx("data.xlsx",sheetName = 1,header = F)
dn <- as.vector(df$X1) # 31 class
model_info <- read.csv("all_new.csv",stringsAsFactors = F) # details of
model output (Actual labels, Model labels, probabablity values)
head（model_info）
head(model_info)
Actual_labels App_labels X1st
1 tinea cruris and corporis tinea cruris and corporis tinea cruris and corporis
2 tinea cruris and corporis tinea cruris and corporis tinea cruris and corporis
3 tinea cruris and corporis no diagnosis acne vulgaris
4 eczema eczema eczema
5 eczema no diagnosis psoriasis
6 folliculitis impetigo and pyodermas impetigo and pyodermas
X2nd X3rd X.st.. X2nd.. X3rd..
1 psoriasis herpes zoster 0.89 0.05 0.03
2 psoriasis eczema 0.89 0.03 0.02
3 psoriasis molluscum contagiosum 0.29 0.16 0.14
4 tinea cruris and corporis psoriasis 0.62 0.09 0.08
5 melasma tinea cruris and corporis 0.27 0.27 0.25
6 acne vulgaris psoriasis 0.73 0.07 0.03
head（dn）
[1] "acne vulgaris" "alopecia areata" "anogenital warts"
[4] "bullous pemphigoid" "candidiasis" "chicken pox"
App_call函数基本上根据模型调用是否正确将概率值转换为0或1
App_call function basically converts the probability values to 0 or 1 based on whether model call is true or not
app_call <- function(cut_off, category){
labels_thr <- rep(0,nrow(app_res))
ind <- which(model_info$X.st.. >= cut_off) # index of instances
above threshold
true_val <- which(app_res$App.Diagnosis[ind] == category) # index of instances where actual labels are similar to model labels for 1st class out of 31 class.
labels_thr[ind[true_val]] <- 1
return(labels_thr)}
index0 <- grep(pattern = paste0("^",dn[i],"$"),x = model_info$Actual_labels)
actual_labels <- rep(0,nrow(model_info))
if(length(index)>= 1){
actual_labels[index0] <- 1
actual_labels[-index0] <- 0}
app_labels <- app_call(cut_off = 0.5,category = dn[i])
res <- roc(actual_labels,app_labels)
res1 <- roc(actual_labels,model_info$X.st..)
dput(actual_labels)
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dput(app_labels)
c(0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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dput(model_info$X.st..)
c(0.89, 0.89, 0.29, 0.62, 0.27, 0.73, 0.44, 0.7, 0.42, 0.56,
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0.34, 0.95, 0.91, 0.36, 0.55, 0.55, 0.29, 0.86, 0.31, 0.48, 0.48,
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0.19, 0.58, 0.94, 0.51, 0.39, 0.84, 0.95, 0.85, 0.72, 0.35, 0.83,
0.5, 0.91, 0.83, 0.61, 0.79, 0.5, 0.87, 0.3, 0.5, 0.53, 0.22,
0.82, 0.74, 0.73, 0.65, 0.88, 0.31, 0.75, 0.74, 0.92, 0.38, 0.47,
0.26, 0.77, 0.78, 0.82, 0.59, 0.59, 0.33, 0.67, 0.31, 0.67, 0.44,
0.77, 0.61, 0.44, 0.77, 0.83, 0.58, 0.6, 0.78, 0.76, 0.47, 0.72,
0.47, 0.29, 0.14, 0.32, 0.17, 0.56, 0.68, 0.3, 0.46, 0.56, 0.68,
0.61, 0.7, 0.23, 0.39, 0.79, 0.38, 0.32, 0.58, 0.46, 0.5, 0.57,
0.93, 0.4, 0.37, 0.75, 0.76, 0.36, 0.84, 0.19, 0.18, 0.94, 0.53,
0.53, 0.24, 0.23, 0.51, 0.53, 0.84, 0.23, 0.44, 0.85, 0.53, 0.23,
0.56, 0.26, 0.38, 0.78, 0.93, 0.65, 0.22, 0.52, 0.35, 0.47, 0.33,
0.31, 0.65, 0.72, 0.46, 0.44, 0.74, 0.92, 0.99, 0.72, 0.41, 0.18,
0.85, 0.89, 0.31, 0.4, 0.98, 0.46, 0.16, 0.58, 0.25, 0.21, 0.32,
0.43, 0.56, 0.34, 0.35, 0.7, 0.43, 0.17, 0.25, 0.33, 0.44, 0.44,
0.58, 0.74, 0.37, 0.68, 0.52, 0.8, 0.96, 0.52, 0.25, 0.81, 0.94,
1, 0.58, 0.42, 0.46, 0.41, 0.18, 0.37, 0.9, 0.54, 0.29, 0.38,
0.38, 0.53, 0.99, 0.57, 0.44, 0.33, 0.45, 0.95, 0.85, 0.75, 0.19,
0.97, 0.27, 0.94, 0.77, 0.79, 0.57, 0.33, 0.98, 0.47, 0.55, 0.27,
0.43, 0.66, 1, 0.62, 0.34, 0.81, 0.4, 0.56, 0.33, 0.25, 0.4,
0.25, 0.91, 0.28, 0.4, 0.73, 0.32, 0.49, 0.37, 0.19, 0.35, 0.29,
0.77, 0.36, 0.31, 0.85, 0.33, 0.61, 0.63, 0.41, 0.98, 0.28, 0.31,
0.91, 0.34, 0.24, 0.82, 0.46, 0.5, 0.39, 0.72, 0.67, 0.51, 0.41,
0.81, 0.74, 0.5, 0.97, 0.65, 0.44, 0.71, 0.35, 0.84, 0.97, 0.42,
0.75, 0.91, 0.61, 0.94, 0.48, 0.42, 0.63, 0.81, 0.83, 0.66, 0.55,
0.61, 0.41, 0.63, 1, 0.63, 0.41, 0.75, 0.27, 0.28, 0.24, 0.55,
0.35, 0.85, 0.97, 0.64, 0.79, 0.92, 0.47, 0.81, 0.23, 0.16, 0.75,
0.12, 0.43, 0.18, 0.69, 0.21, 0.39, 0.19, 0.85, 0.57, 0.97, 0.56,
0.81, 0.13, 0.4, 0.47, 0.95, 0.43, 0.9, 0.67, 0.36, 0.38, 0.83,
0.97, 0.48, 0.93, 0.67, 0.44, 0.34, 0.83, 0.77, 0.39, 0.56, 0.85,
0.55, 0.22, 0.48, 0.46, 0.59, 0.89, 0.99, 0.57, 0.96, 0.97, 0.95,
0.98, 0.24, 0.89, 0.5, 0.94, 0.6, 0.41, 0.71, 0.5, 0.2, 0.96,
0.18, 0.93, 0.92, 0.85, 0.92, 0.82, 0.48, 0.62, 0.53, 0.59, 0.38,
0.8, 0.49, 0.91, 0.58, 0.94, 0.68, 0.15, 0.96, 0.98, 0.89, 0.84,
0.5, 0.88, 0.29, 0.24, 0.31, 0.29, 0.33, 0.49, 0.33, 0.76, 0.54,
0.88, 0.78, 0.26, 0.52, 0.75, 0.97, 0.93, 0.27, 0.69, 0.19, 0.69,
0.2, 0.21, 0.84, 0.31, 0.19, 0.8, 0.6, 0.19, 0.51, 0.98, 0.27,
0.39, 0.77, 0.95, 0.73, 0.28, 0.79, 0.19, 0.98, 0.77, 0.31, 0.84,
0.35, 0.19, 0.26, 0.82, 0.63, 0.38, 0.38, 0.26, 0.63, 0.65, 0.55,
0.88, 0.6, 0.71, 0.85, 0.99, 0.28, 0.42, 0.65, 0.58, 0.97, 0.35,
0.36, 0.32, 0.79, 0.68, 0.39, 0.45, 0.71, 0.98, 0.34, 0.62, 0.24,
0.55, 0.43, 0.95, 0.32, 0.6, 0.63, 0.98, 0.2, 0.31, 0.9, 0.3,
0.32, 0.37, 0.52, 0.64, 0.9, 0.22, 0.31, 0.39, 0.21, 0.93, 0.64,
0.4, 0.96, 0.31, 0.46, 0.86, 0.56, 0.99, 0.83, 0.87, 0.36, 0.59,
0.98, 0.72, 0.21, 0.52, 0.17, 0.21, 0.42, 0.97, 0.34, 0.96, 0.18,
0.63, 0.45, 0.36, 0.31, 0.48, 0.94, 0.86, 0.16, 0.32, 0.97, 0.29,
0.9, 0.38, 0.88, 0.6, 0.17, 0.19, 0.44, 0.98, 0.35, 0.36, 0.2,
0.39, 0.53, 0.35, 0.57, 0.18, 0.26, 0.17, 0.77, 0.51, 1, 0.17,
0.57, 0.48, 0.58, 0.25, 0.32, 0.33, 0.76, 0.16, 0.13, 0.46, 0.44,
0.31, 0.56, 0.46, 0.6, 0.17, 0.36, 0.34, 0.44, 0.43, 0.86, 0.86,
0.44, 0.34, 0.92, 0.32, 0.78, 0.21, 0.46, 0.92, 0.27, 0.98, 0.52,
0.34, 0.27, 0.59, 0.45, 0.58, 0.27, 0.48, 0.21, 0.24, 0.29, 0.89,
0.25, 0.33, 0.96, 0.56, 0.29, 0.97, 0.98, 0.59, 0.28, 0.22, 0.76,
0.91, 0.92, 0.91, 0.94, 0.83, 0.48, 0.53, 0.56, 0.5, 0.75, 0.4,
0.98, 0.6, 0.74, 0.66, 0.97, 0.62, 0.99, 0.39, 0.89, 0.86, 0.66,
0.92, 0.34, 0.99, 0.69, 0.71, 0.8, 0.47, 0.5, 0.83, 0.83, 0.41,
0.72, 0.98, 0.76, 0.65, 0.71, 0.9, 0.9, 1, 0.4, 0.46, 0.35, 0.72,
0.92, 0.74, 0.44, 0.67, 0.97, 0.88, 0.84, 0.71, 0.45, 0.78, 0.9,
0.72, 0.57, 0.68, 0.85, 0.84, 0.46, 0.91, 0.53, 0.96, 0.49, 0.93,
0.49, 0.37, 0.95, 0.47, 0.87, 0.49, 0.58, 0.64, 0.84, 0.8, 0.49,
0.67, 0.75, 0.44, 0.87, 0.71, 0.47, 0.46, 0.83, 0.74, 0.99, 0.86,
0.64, 0.74, 0.43, 0.44, 0.57, 0.89, 0.67, 0.59, 0.89, 0.45, 0.62,
0.81, 0.93, 0.81, 0.98, 0.95, 0.63, 0.64, 0.96, 0.55, 0.49, 0.59,
0.47, 0.42, 0.6, 0.51, 0.4, 0.3, 0.29, 0.45, 0.94, 0.29, 0.33,
0.14, 0.71, 0.41, 0.6, 0.31, 0.95, 0.94, 0.87, 0.8, 0.53, 0.66,
0.71, 0.19, 0.49, 0.97, 0.48, 0.43, 0.38, 0.4, 0.22, 0.38, 0.27,
0.25, 0.45, 0.75, 0.38, 0.23, 0.92, 0.7, 0.68, 0.17, 0.39, 0.65,
0.38, 0.39, 0.21, 0.28, 0.55, 0.89, 0.24, 0.34, 0.92, 0.31, 0.64,
0.86, 0.94, 0.28, 0.43, 0.44, 0.82, 0.23, 0.81, 0.71, 0.53, 0.96,
0.9, 0.55, 0.83, 0.64, 0.51, 0.32, 0.66, 0.45, 0.72, 0.28, 0.34,
0.98, 0.76, 0.52, 0.95, 0.83, 0.47, 0.9, 0.31, 0.23, 0.61, 0.94,
0.61, 0.42, 0.34, 0.55, 0.33, 0.93, 0.24, 0.51, 0.65, 0.17, 0.81,
0.68, 0.51, 0.78, 0.37, 0.37, 0.99, 0.94, 0.64, 0.59, 0.61, 0.9,
0.88, 0.64, 0.49, 0.09, 0.51, NA, 0.86, 0.45, 0.61, 0.24, 0.85,
0.26, 0.29, 0.21, 0.66, 0.26, 0.47, 0.19, 0.99, 0.51, 0.91, 0.37,
0.56, 0.71, 0.47, 0.44, 0.48, 0.52, 0.22, 0.52, 0.29, 0.46, 0.54,
0.94, 0.24, 0.24, 0.47, 0.37, 0.9, 0.79, 0.81, 0.41, 0.38, 0.71,
0.34, 0.46, 0.23, 0.54, 0.43, 0.85, 0.56, 0.26, 0.9, 0.25, 0.3,
0.39, 0.89, 0.38, 0.18, 0.78, 0.37, 0.45, 0.51, 0.8, 0.61, 0.52,
0.84, 0.4, 0.31, 0.28, 0.24, 0.23, 0.43, 0.77, 0.78, 0.95, 0.9,
0.81, 0.15, 0.77, 0.77, 0.87, 0.75, 0.16, 0.49, 0.23, 0.93, 0.45,
0.33, 0.75, 0.32, 0.75, 0.41, 0.24, 0.46, 0.17, 0.41, 0.45, 0.48,
0.15, 0.66, 0.53, 0.75, 0.57, 0.46, 0.78, 0.24, 0.29, 0.95, 0.77,
0.66, 0.94, 0.27, 0.29, 0.58, 0.6, 0.46, 0.58, 0.84, 0.69, 0.47,
0.45, 0.48, 0.35, 0.89, 0.98, 0.93, 0.2, 0.94, 0.91, 0.75, 0.5,
0.44, 0.69, 0.8, 0.76, 0.85, 0.84, 0.72, 0.25, 0.73, 0.26, 0.93,
0.15, 0.33, 0.3, 0.6, 0.24, 0.21, 0.28, 0.51, 0.79, 0.77, 0.85,
0.52, 0.39, 0.68, 0.83, 0.36, 0.15, 0.87, 0.55)
res1 = roc(actual_labels,app_labels)
res2= roc(actual_labels,model_info$X.st..)
在实际标签类中为 1并具有proba的调用blity阈值（model_info $ X.st ..）值大于0.5的app_labels命名为 1，其余全部为零
The calls in the actual label class where it is "1" and have have a probablity threshold (model_info$X.st..) value more than 0.5 is named as "1" for app_labels and rest all zero
res1和res2都具有。
推荐答案
ROC曲线显示敏感性和特异性之间的权衡作为决策的阈值分类器是多种多样的。通常，ROC曲线函数希望获得预测值和真实值作为输入。
A ROC curve shows the sensitivity and specificity tradeoff as the decision threshold of a classifier is varied. Typically ROC curve functions expect to get the prediction and the truth value as input.
这正是您在运行时所做的：
This is exactly what you do when you run:
res2= roc(actual_labels,model_info$X.st..)
但是您的 app_labels 具有非常不同的性质：您已经在正确的分类方面进行了合并，这使其更像是扁平化的意外事件表超出了ROC函数所期望的预测。因此，您不再可以使用常规的ROC函数，而需要手动计算灵敏度和特异性。
However your app_labels is of a very different nature: you have already merged in the "correct classification" aspect, which makes it more like a flattened contingency table than the "predictions" the ROC function expects. So you can no longer use a regular ROC function and need to calculate the sensitivity and specificity manually.
TP <- sum(app_labels & actual_labels)
TN <- sum(app_labels & !(actual_labels))
FP <- sum(!(app_labels) & !(actual_labels))
FN <- sum(!(app_labels) & actual_labels)
# Sensitivity:
TP / (TP+FN)
# Specificity:
TN / (TN + FP)
这篇关于使用R中的pROC使用单个阈值和0.5的阈值梯度可改变灵敏度和特异性的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！