R/feature_selection.R

In amphibian-dev/Rana-kukunoris: credit scorecard Modeling tools

#
# library(lubridate)
# library(magrittr)
# library(dplyr)
# library(randomForest)
# library(pROC)
# library(car)
# library(ROCR)
# library(smbinning)
# # function: bmp, dev.off
# library(discretization)
# # function: chiSq
# library(glmnet)
# # function: cv.glmnet, coef.glmnet
# library(doMC)
# # function: registerDoMC
# registerDoMC(cores=4)
# # fucntion: ks_stat, ks_plot
# library(InformationValue)

##################自动计算vif ####################################    v5版本加入
# data中需要target
auto_vif<-function(data){
  library(car)
  data[data=="NaN"]<-0
  data[data=="Inf"]<-0
  data[is.na(data)]<-0
  target<- data$target
  lm1<- lm(target~.,data = data)

  after_delete<-na.omit(lm1$coefficients)
  m<-names(after_delete)[1]
  for (i in 2:length(after_delete)) {
    m<-paste0(m,"|",names(after_delete)[i])
  }
  m<-sub(" ","",m)
  col<-grep(m,names(data))

  data<- data[,col]
  while(1){

    lm<-lm(target~.,data)
    if(is.matrix(vif(lm))){
      tmp<-vif(lm)%>%as.data.frame()
      if(max(tmp$`GVIF^(1/(2*Df))`)>3){
        data<-data[,-grep(rownames(tmp[which.max(tmp$`GVIF^(1/(2*Df))`),]),names(data))]
      }else{
        break
      }
    }else{
      tmp<-vif(lm)
      if(max(tmp)>3){
        data<-data[,-grep(names(tmp)[which.max(tmp)],names(data))]
      }else{
        break
      }
    }
  }
  return(tmp)
}