汇总函数插入符号分类的自定义度量（hmeasure）

我正在尝试使用hmeasure度量作为我的自定义度量，用于在插入符号中训练支持向量机。因为我对使用R比较陌生，所以我尝试调整twoClassSummary函数。我所需要的只是将真实的类别标签和预测的类别概率从模型（一个svm）传递到HMeasure包中的HMeasure函数，而不是在插入符号中使用ROC或其他分类性能度量

例如，在R-HMeasure（true.class，predictedProbs[，2]）中调用HMeasure函数会导致计算HMeasure。使用下面的twoClassSummary代码的自适应会导致返回错误：“x”必须是数字

也许训练函数不能“看到”预测的概率来评估HMeasure函数。我怎样才能解决这个问题

我已经阅读了文档，并将提出的问题联系起来。这给了我一些帮助。如果您能为我提供帮助或建议，我将不胜感激

library(caret)
library(doMC)
library(hmeasure)
library(mlbench)

set.seed(825)

data(Sonar)
table(Sonar$Class) 
inTraining <- createDataPartition(Sonar$Class, p = 0.75, list = FALSE)
training <- Sonar[inTraining, ]
testing <- Sonar[-inTraining, ]


# using caret
fitControl <- trainControl(method = "repeatedcv",number = 2,repeats=2,summaryFunction=twoClassSummary,classProbs=TRUE)

svmFit1 <- train(Class ~ ., data = training,method = "svmRadial",trControl =    fitControl,preProc = c("center", "scale"),tuneLength = 8,metric = "ROC")

predictedProbs <- predict(svmFit1, newdata = testing , type = "prob")
true.class<-testing$Class
hmeas<- HMeasure(true.class,predictedProbs[,2]) # suppose its Rocks we're interested in predicting
hmeasure.probs<-hmeas$metrics[c('H')] # returns the H measure metric 

hmeasureCaret<-function (data, lev = NULL, model = NULL,...) 
{ 
# adaptation of twoClassSummary
require(hmeasure)
if (!all(levels(data[, "pred"]) == levels(data[, "obs"]))) 
 stop("levels of observed and predicted data do not match")
#lev is a character string that has the outcome factor levels taken from the training   data
hObject <- try(hmeasure::HMeasure(data$obs, data[, lev[1]]),silent=TRUE)
hmeasH <- if (class(hObject)[1] == "try-error") {
NA
} else {hObject$metrics[[1]]  #hObject$metrics[c('H')] returns a dataframe, need to    return a vector 
}
out<-hmeasH 
names(out) <- c("Hmeas")
#class(out)
}
environment(hmeasureCaret) <- asNamespace('caret')

库（插入符号）
图书馆（doMC）
图书馆（hmeasure）
图书馆（mlbench）
种子集（825）
数据（声纳）
表（声纳$Class）
intraning此代码有效。我正在发布一个解决方案，以防其他人想要使用/改进此解决方案。
这些问题是由于对Hmeasure对象的引用不正确以及函数返回值的输入错误/注释造成的

library(caret)
library(doMC)
library(hmeasure)
library(mlbench)

set.seed(825)
registerDoMC(cores = 4)

data(Sonar)
table(Sonar$Class) 

inTraining <- createDataPartition(Sonar$Class, p = 0.5, list = FALSE)
training <- Sonar[inTraining, ]
testing <- Sonar[-inTraining, ]

hmeasureCaret<-function (data, lev = NULL, model = NULL,...) 
{ 
  # adaptation of twoClassSummary
  require(hmeasure)
  if (!all(levels(data[, "pred"]) == levels(data[, "obs"]))) 
    stop("levels of observed and predicted data do not match")
  hObject <- try(hmeasure::HMeasure(data$obs, data[, lev[1]]),silent=TRUE)
  hmeasH <- if (class(hObject)[1] == "try-error") {
    NA
  } else {hObject$metrics[[1]]  #hObject$metrics[c('H')] returns a dataframe, need to return a vector 
  }
  out<-hmeasH 
  names(out) <- c("Hmeas")
  out 
}
#environment(hmeasureCaret) <- asNamespace('caret')


ctrl <- trainControl(method = "repeatedcv",number = 10, repeats = 5, summaryFunction = hmeasureCaret,classProbs=TRUE,allowParallel = TRUE,
                     verboseIter=FALSE,returnData=FALSE,savePredictions=FALSE)
set.seed(123)

svmTune <- train(Class ~ ., data = training,method = "svmRadial",trControl = ctrl,preProc = c("center", "scale"),tuneLength = 15,metric="Hmeas",
                 verbose = FALSE)
svmTune

predictedProbs <- predict(svmTune, newdata = testing , type = "prob")

true.class<-testing$Class

hmeas.check<- HMeasure(true.class,predictedProbs[,2])

summary(hmeas.check)

库（插入符号）
图书馆（doMC）
图书馆（hmeasure）
图书馆（mlbench）
种子集（825）
寄存器DOMC（核心=4）
数据（声纳）
表（声纳$Class）
我不确定接受你自己的回答的礼节是什么。有人能告诉我吗？如果答案对你有帮助，那没关系，别担心。
library(caret)
library(doMC)
library(hmeasure)
library(mlbench)

set.seed(825)
registerDoMC(cores = 4)

data(Sonar)
table(Sonar$Class) 

inTraining <- createDataPartition(Sonar$Class, p = 0.5, list = FALSE)
training <- Sonar[inTraining, ]
testing <- Sonar[-inTraining, ]

hmeasureCaret<-function (data, lev = NULL, model = NULL,...) 
{ 
  # adaptation of twoClassSummary
  require(hmeasure)
  if (!all(levels(data[, "pred"]) == levels(data[, "obs"]))) 
    stop("levels of observed and predicted data do not match")
  hObject <- try(hmeasure::HMeasure(data$obs, data[, lev[1]]),silent=TRUE)
  hmeasH <- if (class(hObject)[1] == "try-error") {
    NA
  } else {hObject$metrics[[1]]  #hObject$metrics[c('H')] returns a dataframe, need to return a vector 
  }
  out<-hmeasH 
  names(out) <- c("Hmeas")
  out 
}
#environment(hmeasureCaret) <- asNamespace('caret')


ctrl <- trainControl(method = "repeatedcv",number = 10, repeats = 5, summaryFunction = hmeasureCaret,classProbs=TRUE,allowParallel = TRUE,
                     verboseIter=FALSE,returnData=FALSE,savePredictions=FALSE)
set.seed(123)

svmTune <- train(Class ~ ., data = training,method = "svmRadial",trControl = ctrl,preProc = c("center", "scale"),tuneLength = 15,metric="Hmeas",
                 verbose = FALSE)
svmTune

predictedProbs <- predict(svmTune, newdata = testing , type = "prob")

true.class<-testing$Class

hmeas.check<- HMeasure(true.class,predictedProbs[,2])

summary(hmeas.check)