rm(list=ls())

library(tibble)

library(dplyr)

library(glmnet)

library(lars) 

library(VennDiagram)

library(sigFeature)

library(e1071)

library(caret)

library(randomForest)

library(ltm)

#UMN_DMP_new：dmp lasso

{

  setwd("E:\\workplace\\mywork\\methy\\dbgap\\chf\\data_chf_contr\\early_chf\\c1_UMN_JHU\\train_UMN_tset_JHU/1123_dataSummary")

  load("UMN_DMP_new.Rdata")

  X <- as.matrix(UMN_DMP_new[,!colnames(UMN_DMP_new) %in% c("chf","cvd")]) #chf

  y <- as.character(UMN_DMP_new$chf)

  set.seed(2)

  fitCV <- cv.glmnet(X, y, family = "binomial", type.measure = "auc", nfolds = 10)

  #lambda.min

  myCoefs <- coef(fitCV, s = "lambda.min")

  lasso_fea <- myCoefs@Dimnames[[1]][which(myCoefs[,1] != 0 )]

  lasso_fea <- data.frame(lasso_fea)

  lasso_fea$x <- myCoefs@x 

  lasso_fea <- lasso_fea[-1,]

  length(lasso_fea[,1])#103

  #lambda.1se

  myCoefs2 <- coef(fitCV, s="lambda.1se")

  #lambda.1se

  lasso_fea2 <- myCoefs2@Dimnames[[1]][which(myCoefs2[,1] != 0 )]

  lasso_fea2 <- data.frame(lasso_fea2)

  lasso_fea2$x <- myCoefs2@x 

  lasso_fea2 <- lasso_fea2[-1,]

  length(lasso_fea2[,1])#91

  set.seed(2)

  cv_fit <- cv.glmnet(X, y, nfold=10, alpha = 1, family = "binomial", type.measure = "class")

  myCoefs3 <- coef(cv_fit, s="lambda.min")

  #lambda.min

  lasso_fea3 <- myCoefs3@Dimnames[[1]][which(myCoefs3[,1] != 0 )]

  lasso_fea3 <- data.frame(lasso_fea3)

  lasso_fea3$x <- myCoefs3@x 

  lasso_fea3 <- lasso_fea3[-1,]

  length(lasso_fea3[,1])#104

  #2

  myCoefs4 <- coef(cv_fit, s="lambda.1se")

  #lambda.1se

  lasso_fea4 <- myCoefs4@Dimnames[[1]][which(myCoefs4[,1] != 0 )]

  lasso_fea4 <- data.frame(lasso_fea4)

  lasso_fea4$x <- myCoefs4@x 

  lasso_fea4 <- lasso_fea4[-1,]

  length(lasso_fea4[,1])#92

  require("VennDiagram")

  #VENN.LIST=list(lasso2_lambda1se=lasso_fea4$lasso_fea4,lasso2_lambdamin=lasso_fea3$lasso_fea3,lasso1_lambda1se=lasso_fea2$lasso_fea2,lasso1_lambdamin=lasso_fea$lasso_fea)

  VENN.LIST=list(B_lambda1se=lasso_fea4$lasso_fea4,B_lambdamin=lasso_fea3$lasso_fea3,A_lambda1se=lasso_fea2$lasso_fea2,A_lambdamin=lasso_fea$lasso_fea)

  venn.plot <- venn.diagram(VENN.LIST , NULL, 

                            fill=c("darkmagenta", "darkblue","red","black"), 

                            alpha=c(0.5,0.5,0.5,0.5), cex = 2, 

                            cat.fontface="plain", cat.cex = 1.5, 

                            main="Overlap of measure in 1se and min ")

  grid.draw(venn.plot) 

  venn4=intersect(intersect(intersect(lasso_fea$lasso_fea,lasso_fea3$lasso_fea3),lasso_fea4$lasso_fea4),lasso_fea2$lasso_fea2)

  venn3=union(union(union(lasso_fea$lasso_fea,lasso_fea3$lasso_fea3),lasso_fea4$lasso_fea4),lasso_fea2$lasso_fea2)

  setwd("E:\\workplace\\mywork\\methy\\dbgap\\chf\\data_chf_contr\\early_chf\\c1_UMN_JHU\\train_UMN_tset_JHU/1123_dataSummary")

  write.csv(venn4,'lasso_dmp_little.csv', row.names = F)

  write.csv(venn3,'lasso_dmp_large.csv', row.names = F)

  {

    library(UpSetR)

    B_lambda1se=lasso_fea4$lasso_fea4

    B_lambdamin=lasso_fea3$lasso_fea3

    A_lambda1se=lasso_fea2$lasso_fea2

    A_lambdamin=lasso_fea$lasso_fea

    input <- fromList(list(A_lambdamin=lasso_fea$lasso_fea, 

                           A_lambda1se=lasso_fea2$lasso_fea2,

                           B_lambdamin=lasso_fea3$lasso_fea3,

                           B_lambda1se=lasso_fea4$lasso_fea4))

    upset(input, order.by = "freq",nsets = 7,point.size = 3) 

  }

}