#train
{
#DMP+clin lasso+xgboost30
{
setwd("E:/workplace/mywork/methy/dbgap/chf/data_chf_contr/early_chf/c1_UMN_JHU/train_UMN_tset_JHU/1123_dataSummary/")
load("train_data.Rdata")
id3 <- read.table("xgblasso_DMP.csv",sep=",",header = T)
head(id3)
id3 <- as.character(id3$Feature)
X <- train_data[,colnames(train_data) %in% c("Heart.failure",id3)]
#X <- subset(X, select=c("Heart.failure",id3))
X = X[,c(2,1,3:31)]
X <- data.frame(X)
X <- rownames_to_column(X,"ID")
#X$ID <- gsub('X','',X$ID)
colnames(X)[2] <- c("target")#chf
write.table(pdata,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_pdata_train.txt",row.names = F)
write.table(X,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_dmp_lassoxgboost.csv",row.names = F,sep=",")
write.table(X[,colnames(X) %in% c("ID","target","Age","Diuretic","BMI","Creatinine.serum","Albumin.urine")],
"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_dmp_lassoxgboost_ehr.csv",row.names = F,sep=",")
write.table(X[,!colnames(X) %in% c("Age","Diuretic","BMI","Creatinine.serum","Albumin.urine")],
"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_dmp_lassoxgboost_cpg.csv",row.names = F,sep=",")
}
}
#test
{
#DMP+clin lasso+xgboost
{
load("test/test_data.Rdata")
X <- test_data[,colnames(test_data) %in% c(id3)]
#X <- subset(X, select=c(id3))
#X = X[,c(1,9:30,2:8)]
X <- data.frame(X)
X <- rownames_to_column(X,"ID")
#X$ID <- gsub('X','',X$ID)
pdata <- data.frame(test_data$Heart.failure)
pdata$ID <- rownames(test_data)
#pdata$ID <- gsub('X','',pdata$ID)
write.table(pdata,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_pdata_test.txt",row.names = F)
write.table(X,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_dmp_lassoxgboost_test.csv",row.names = F,sep=",")
write.table(X[,colnames(X) %in% c("ID","Age","Diuretic","BMI","Creatinine.serum","Albumin.urine")],
"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_dmp_lassoxgboost_ehr_test.csv",row.names = F,sep=",")
write.table(X[,!colnames(X) %in% c("Age","Diuretic","BMI","Creatinine.serum","Albumin.urine")],
"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_dmp_lassoxgboost_cpg_test.csv",row.names = F,sep=",")
}
}
############################
#feature 30-1
{
load("UMN_DMP_new.Rdata")
id3 <- read.table("xgblasso_DMP.csv",sep=",",header = T)
head(id3)
id3 <- as.character(id3$Feature)
for(i in 3:32){
X <- UMN_DMP_new[,colnames(UMN_DMP_new) %in% c("chf",id3)]
X <- subset(X, select = c("chf",id3))
X <- rownames_to_column(X,"ID")
colnames(X)[2] <- "target"
names = colnames(X)[i]
X <- X[,-i]
X <- data.frame(X)
write.csv(X,paste("D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_feature_rm",names,"dmp_lassoxgboost.csv",sep="_"),row.names = F)
}
load("JHU_DMP_new.Rdata")
id3 <- read.table("xgblasso_DMP.csv",sep=",",header = T)
head(id3)
id3 <- as.character(id3$Feature)
for(i in 2:31){
X <- JHU_DMP_new[,colnames(JHU_DMP_new) %in% c(id3)]
X <- subset(X, select = c(id3))
X <- rownames_to_column(X,"ID")
X$ID <- gsub('X','',X$ID)
names = colnames(X)[i]
X <- X[,-i]
X <- data.frame(X)
write.csv(X,paste("D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_feature_rm",names,"dmp_lassoxgboost_test.csv",sep="_"),row.names = F)
}
}
#50%,75%,25%,60%
{
setwd("E:/workplace/mywork/methy/dbgap/chf/data_chf_contr/early_chf/c1_UMN_JHU/train_UMN_tset_JHU/DMP_pipeline")
load("UMN_DMP_new.Rdata")
id3 <- read.table("xgblasso_DMP.csv",sep=",",header = T)
head(id3)
id3 <- as.character(id3$Feature)
X <- UMN_DMP_new[,colnames(UMN_DMP_new) %in% c("chf",id3)]
samp1=sample(1:nrow(X),round(nrow(X)/4)) # 25%
samp2=sample(1:nrow(X),round(nrow(X)/2)) # 50%
samp3=sample(1:nrow(X),6*round(nrow(X)/10)) # 60%
samp4=sample(1:nrow(X),7.5*round(nrow(X)/10)) # 75%
X=X[samp3,]
X <- data.frame(X)
X <- rownames_to_column(X,"ID")
colnames(X)[3] <- c("target")#chf
pdata <- data.frame(UMN_DMP_new$chf)
#write.table(pdata,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_pdata_UMN.txt",row.names = F)
write.table(X,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_feature_dmp_lassoxgboost_25percentage.csv",row.names = F,sep=",")
write.table(X,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_feature_dmp_lassoxgboost_50percentage.csv",row.names = F,sep=",")
write.table(X,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_feature_dmp_lassoxgboost_60percentage.csv",row.names = F,sep=",")
write.table(X,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_feature_dmp_lassoxgboost_75percentage.csv",row.names = F,sep=",")
}
#train:sample 6/4-years
{
library(dplyr)
library(tibble)
load(file="UMN_meta_beta.Rdata")
#==========================
load("UMN_DMP_new.Rdata")
id3 <- read.table("xgblasso_DMP.csv",sep=",",header = T)
head(id3)
id3 <- as.character(id3$Feature)
X <- UMN_DMP_new[,colnames(UMN_DMP_new) %in% c("chf",id3)]
X <- subset(X, select = c("chf",id3))
X <- data.frame(X)
X <- rownames_to_column(X,"ID")
X$ID <- gsub('X','',X$ID)
colnames(X)[2] <- "target"
X <- merge(UMN_meta_beta[,c("shareid","DATE8","chfdate")],X,by.x = "shareid",by.y="ID")
#================
#6 years-777
X_control <- filter(X, target == 0 ,(chfdate - DATE8 ) > 6*365)
X_chf <- filter(X, target == 1 & (chfdate >= DATE8) & (chfdate - DATE8 ) <6*365)
summary((X_control$chfdate - X_control$DATE8))
summary((X_chf$chfdate - X_chf$DATE8))
X <- rbind(X_chf,X_control)
X <- X[,-c(2,3)]
colnames(X)[1] <- "ID"
samp=sample(1:nrow(X),round(nrow(X)))
X = X[samp,]
write.csv(X,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_feature_dmp_lassoxgboost_6years.csv",row.names = F)
#================
#4 years-761
X_control <- filter(X, target == 0 ,(chfdate - DATE8 ) > 4*365)
X_chf <- filter(X, target == 1 & (chfdate >= DATE8) & (chfdate - DATE8 ) <4*365)
summary((X_control$chfdate - X_control$DATE8))
summary((X_chf$chfdate - X_chf$DATE8))
X <- rbind(X_chf,X_control)
X <- X[,-c(2,3)]
colnames(X)[1] <- "ID"
samp=sample(1:nrow(X),round(nrow(X)))
X = X[samp,]
write.csv(X,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_feature_dmp_lassoxgboost_4years.csv",row.names = F)
}
#test:sample 6/4-years
{
library(dplyr)
library(tibble)
load(file="test/JHU_meta_beta.Rdata")
#==========================
load("test/JHU_DMP_new.Rdata")
id3 <- read.table("xgblasso_DMP.csv",sep=",",header = T)
head(id3)
id3 <- as.character(id3$Feature)
X <- JHU_DMP_new[,colnames(JHU_DMP_new) %in% c("chf",id3)]
X <- subset(X, select = c("chf",id3))
X <- data.frame(X)
X <- rownames_to_column(X,"ID")
X$ID <- gsub('X','',X$ID)
colnames(X)[2] <- "target"
X <- merge(JHU_meta_beta[,c("shareid","DATE8","chfdate")],X,by.x = "shareid",by.y="ID")
#================
#6 years-163
X_control <- filter(X, target == 0 ,(chfdate - DATE8 ) > 6*365)
X_chf <- filter(X, target == 1 & (chfdate >= DATE8) & (chfdate - DATE8 ) <6*365)
summary((X_control$chfdate - X_control$DATE8))
summary((X_chf$chfdate - X_chf$DATE8))
X <- rbind(X_chf,X_control)
X <- X[,-c(2,3)]
colnames(X)[1] <- "ID"
samp=sample(1:nrow(X),round(nrow(X)))
X = X[samp,]
pdata <- data.frame(X$target)
pdata$ID <- X$ID
write.table(pdata,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_pdata_JHU_6years.txt",row.names = F)#为剔除样本做准备
X <- X[,-2]
write.csv(X,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_feature_dmp_lassoxgboost_6years_test.csv",row.names = F)
#================
#4 years-154
X_control <- filter(X, target == 0 ,(chfdate - DATE8 ) > 4*365)
X_chf <- filter(X, target == 1 & (chfdate >= DATE8) & (chfdate - DATE8 ) <4*365)
summary((X_control$chfdate - X_control$DATE8))
summary((X_chf$chfdate - X_chf$DATE8))
X <- rbind(X_chf,X_control)
X <- X[,-c(2,3)]
colnames(X)[1] <- "ID"
samp=sample(1:nrow(X),round(nrow(X)))
X = X[samp,]
pdata <- data.frame(X$target)
pdata$ID <- X$ID
write.table(pdata,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_pdata_JHU_4years.txt",row.names = F)#为剔除样本做准备
X <- X[,-2]
write.csv(X,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_feature_dmp_lassoxgboost_4years_test.csv",row.names = F)
}
#FHS model : test data
{
library("readxl")
setwd("E:/workplace/mywork/methy/dbgap/3_clin")
echo1 <- read_excel("phs000007.v30.pht002572.v6.p11.c1.t_echo_2008_m_0549s.HMB-IRB-MDS.xlsx",sheet=1, na = "", skip = 10)
echo1 <- data.frame(echo1)
setwd("H:/dbgap_CHD/RootStudyConsentSet_phs000007.Framingham.v30.p11.c2.HMB-IRB-NPU-MDS/PhenotypeFiles")
echo2 <- read.table("phs000007.v30.pht002572.v6.p11.c2.t_echo_2008_m_0549s.HMB-IRB-NPU-MDS.txt",sep="\t",header = T)
echo2 <- data.frame(echo2)
echo <- rbind(echo1,echo2)
echo <- echo[,c("shareid","x139","VRNORMAL","sdate")]
colnames(echo)[2] <- c("heart_rate")
colnames(echo)[3] <- c("VALVE_disease")
colnames(echo)[4] <- c("echo_date")
echo <- filter(echo,VALVE_disease != "" )
echo$VALVE_disease <- ifelse(echo$VALVE_disease == "2" ,"1","0")
#LVH
setwd("H:/dbgap_CHD/RootStudyConsentSet_phs000007.Framingham.v30.p11.c1.HMB-IRB-MDS/PhenotypeFiles")
LVH1 <- read.table("phs000007.v30.pht000747.v6.p11.c1.ex1_8s.HMB-IRB-MDS.txt",sep="\t",header = T,skip=10)
LVH1 <- data.frame(LVH1)
setwd("H:/dbgap_CHD/RootStudyConsentSet_phs000007.Framingham.v30.p11.c2.HMB-IRB-NPU-MDS/PhenotypeFiles")
LVH2 <- read.table("phs000007.v30.pht000747.v6.p11.c2.ex1_8s.HMB-IRB-NPU-MDS.txt",sep="\t",header = T,skip=10)
LVH2 <- data.frame(LVH2)
LVH <- rbind(LVH1,LVH2)
LVH <- LVH[,c("shareid","H338")]
colnames(LVH)[2] <- c("LVH")
LVH <- filter(LVH,LVH != "" )
LVH$LVH <- ifelse(LVH$LVH == "0" ,"0","1")
FHS <- merge(echo,LVH,by="shareid")
load("test/JHU_meta_beta.Rdata")
tmp_test = JHU_meta_beta[,colnames(JHU_meta_beta) %in%
c("shareid","chf","SEX",
"SBP8","CURR_DIAB8",
"BMI8","AGE8","chd",id3)]
tmp_test = merge(FHS,tmp_test,by="shareid")
#add col
tmp_test <- filter(tmp_test,CURR_DIAB8 != "" )
tmp_test$vd_diab <- ifelse(tmp_test$CURR_DIAB8 == "1" & tmp_test$VALVE_disease == "1","1","0") #且
tmp_test <- column_to_rownames(tmp_test,"shareid")
#impute
tmp_test <- impute.knn(as.matrix(data.frame(t(tmp_test))))
tmp_test <- data.frame(t(tmp_test$data))
#change 0,1,
for(i in c("heart_rate","BMI8","Albumin_urine")){
tmp_test[,i] <- round(tmp_test[,i],2)
}
#rebulit data
{
FHS_man <- tmp_test[tmp_test$SEX == 0, colnames(tmp_test) %in% c('chd','heart_rate','VALVE_disease','LVH','AGE8','SBP8','CURR_DIAB8')]
FHS_woman <- tmp_test[tmp_test$SEX == 1,colnames(tmp_test) %in% c('chd','heart_rate','VALVE_disease','LVH','AGE8','SBP8','CURR_DIAB8','vd_diab','BMI8')]
normalization<-function(x){
return((x-min(x))/(max(x)-min(x)))}
#c= c("AGE8","BMI8","CREAT8","DBP8","FASTING_BG8","HDL8","SBP8","TC8,Albumin_urine","Hemoglobin_A1c_wholeblood","crp")
for(i in c(1,10:14)){
tmp_test[,i] <- normalization(tmp_test[,i])
}
model_man <- tmp_test[tmp_test$SEX == 0,colnames(tmp_test) %in% id3]
model_man <- data.frame(model_man)
model_man <- rownames_to_column(model_man,"ID")
model_man$ID <- gsub('X','',model_man$ID)
model_woman <- tmp_test[tmp_test$SEX == 1,colnames(tmp_test) %in% id3]
model_woman <- data.frame(model_woman)
model_woman <- rownames_to_column(model_woman,"ID")
model_woman$ID <- gsub('X','',model_woman$ID)
pdata_man <- data.frame(tmp_test$chf[tmp_test$SEX == 0])
pdata_man$ID <- rownames(tmp_test[tmp_test$SEX == 0,])
pdata_woman <- data.frame(tmp_test$chf[tmp_test$SEX == 1])
pdata_woman$ID <- rownames(tmp_test[tmp_test$SEX == 1,])
write.table(pdata_man,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_pdata_JHU_man.txt",row.names = F)
write.table(pdata_woman,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_pdata_JHU_woman.txt",row.names = F)
write.table(FHS_man,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_FHSman_test.csv")
write.table(FHS_woman,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_FHSwoman_test.csv")
write.table(model_man,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_feature_cor_lassoxgboost_man_test.csv",row.names = T,sep=",")
write.table(model_woman,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\20201126deepfm_feature_cor_lassoxgboost_woman_test.csv",row.names = T,sep=",")
}
}
#feature top5,top10,top15,top20,top25
{
load("UMN_DMP_new.Rdata")
load("JHU_DMP_new.Rdata")
id3 <- read.table("xgblasso_DMP.csv",sep=",",header = T)
head(id3)
id3 <- as.character(id3$Feature)
{
X5 <- UMN_DMP_new[,colnames(UMN_DMP_new) %in% c("chf",id3[1:5])]
#X5 <- subset(X5, select = c("chf",id3[1:5]))
X5 <- rownames_to_column(X5,"ID")
colnames(X5)[2] <- "target"
X5 <- data.frame(X5)
write.csv(X5,paste("D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_top5_dmp_lassoxgboost.csv",sep="_"),row.names = F)
X5 <- JHU_DMP_new[,colnames(JHU_DMP_new) %in% c(id3[1:5])]
#X5 <- subset(X5, select = c(id3[1:5]))
X5 <- rownames_to_column(X5,"ID")
X5 <- data.frame(X5)
pdata <- data.frame(JHU_DMP_new$chf)
pdata$ID <- rownames(JHU_DMP_new)
write.table(pdata,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_pdata_test.txt",row.names = F)
write.csv(X5,paste("D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_top5_dmp_lassoxgboost_test.csv",sep="_"),row.names = F)
}
{
X5 <- UMN_DMP_new[,colnames(UMN_DMP_new) %in% c("chf",id3[1:10])]
#X5 <- subset(X5, select = c("chf",id3[1:10]))
X5 <- rownames_to_column(X5,"ID")
colnames(X5)[3] <- "target"
X5 <- data.frame(X5)
write.csv(X5,paste("D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_top10_dmp_lassoxgboost.csv",sep="_"),row.names = F)
X5 <- JHU_DMP_new[,colnames(JHU_DMP_new) %in% c(id3[1:10])]
#X5 <- subset(X5, select = c(id3[1:10]))
X5 <- rownames_to_column(X5,"ID")
X5 <- data.frame(X5)
pdata <- data.frame(JHU_DMP_new$chf)
pdata$ID <- rownames(JHU_DMP_new)
#write.table(pdata,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_pdata_test.txt",row.names = F)
write.csv(X5,paste("D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_top10_dmp_lassoxgboost_test.csv",sep="_"),row.names = F)
}
{
X5 <- UMN_DMP_new[,colnames(UMN_DMP_new) %in% c("chf",id3[1:15])]
#X5 <- subset(X5, select = c("chf",id3[1:15]))
X5 <- rownames_to_column(X5,"ID")
colnames(X5)[3] <- "target"
X5 <- data.frame(X5)
write.csv(X5,paste("D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_top15_dmp_lassoxgboost.csv",sep="_"),row.names = F)
X5 <- JHU_DMP_new[,colnames(JHU_DMP_new) %in% c(id3[1:15])]
#X5 <- subset(X5, select = c(id3[1:15]))
X5 <- rownames_to_column(X5,"ID")
X5 <- data.frame(X5)
pdata <- data.frame(JHU_DMP_new$chf)
pdata$ID <- rownames(JHU_DMP_new)
#write.table(pdata,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_pdata_test.txt",row.names = F)
write.csv(X5,paste("D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_top15_dmp_lassoxgboost_test.csv",sep="_"),row.names = F)
}
{
X5 <- UMN_DMP_new[,colnames(UMN_DMP_new) %in% c("chf",id3[1:20])]
#X5 <- subset(X5, select = c("chf",id3[1:20]))
X5 <- rownames_to_column(X5,"ID")
colnames(X5)[3] <- "target"
X5 <- data.frame(X5)
write.csv(X5,paste("D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_top20_dmp_lassoxgboost.csv",sep="_"),row.names = F)
X5 <- JHU_DMP_new[,colnames(JHU_DMP_new) %in% c(id3[1:20])]
#X5 <- subset(X5, select = c(id3[1:20]))
X5 <- rownames_to_column(X5,"ID")
X5 <- data.frame(X5)
write.csv(X5,paste("D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_top20_dmp_lassoxgboost_test.csv",sep="_"),row.names = F)
}
{
X5 <- UMN_DMP_new[,colnames(UMN_DMP_new) %in% c("chf",id3[1:25])]
#X5 <- subset(X5, select = c("chf",id3[1:25]))
X5 <- rownames_to_column(X5,"ID")
colnames(X5)[3] <- "target"
X5 <- data.frame(X5)
write.csv(X5,paste("D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_top25_dmp_lassoxgboost.csv",sep="_"),row.names = F)
X5 <- JHU_DMP_new[,colnames(JHU_DMP_new) %in% c(id3[1:25])]
#X5 <- subset(X5, select = c(id3[1:25]))
X5 <- rownames_to_column(X5,"ID")
X5 <- data.frame(X5)
write.csv(X5,paste("D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_top25_dmp_lassoxgboost_test.csv",sep="_"),row.names = F)
}
{
X5 <- UMN_DMP_new[,colnames(UMN_DMP_new) %in% c("chf",id3)]
X5 <- subset(X5, select = c("chf",id3))
X5 <- rownames_to_column(X5,"ID")
colnames(X5)[2] <- "target"
colnames(X5)[4] <- "AGE8"
colnames(X5)[8] <- "Sulfonamides"
colnames(X5)[19] <- "BMI8"
colnames(X5)[22] <- "CREAT8"
colnames(X5)[23] <- "Albumin_urine"
X5 <- data.frame(X5)
write.csv(X5,paste("D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_dmp_lassoxgboost.csv",sep="_"),row.names = F)
X5 <- JHU_DMP_new[,colnames(JHU_DMP_new) %in% c(id3)]
X5 <- subset(X5, select = c(id3))
X5 <- rownames_to_column(X5,"ID")
X5 <- data.frame(X5)
colnames(X5)[3] <- "AGE8"
colnames(X5)[7] <- "Sulfonamides"
colnames(X5)[18] <- "BMI8"
colnames(X5)[21] <- "CREAT8"
colnames(X5)[22] <- "Albumin_urine"
write.csv(X5,paste("D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210707deepfm_feature_dmp_lassoxgboost_test.csv",sep="_"),row.names = F)
}
}
Datasets
Models
