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Raymond King
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HFmeRisk
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[d2c46b]: / diff_sex / diff_sexR.R

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#diff sex################

#train old: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("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","SEX",id3)]

  X = X[,c(2,1,3:32)]

  X <- data.frame(X)

  X <- rownames_to_column(X,"ID")

  colnames(X)[2] <- c("target")#chf

  write.table(pdata,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210817deepfm_pdata_sex_train.txt",row.names = F)

  write.table(X,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210817deepfm_feature_dmp_lassoxgboost_sex.csv",row.names = F,sep=",")

  # write.table(X[,colnames(X) %in% c("ID","target","AGE8","Sulfonamides","BMI8","Albumin_urine","CREAT8")],

  #             "D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210817deepfm_feature_dmp_lassoxgboost_ehr_sex.csv",row.names = F,sep=",")

  # write.table(X[,!colnames(X) %in% c("AGE8","Sulfonamides","BMI8","Albumin_urine","CREAT8")],

  #             "D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210817deepfm_feature_dmp_lassoxgboost_cpg_sex.csv",row.names = F,sep=",")

}



#test old sex:DMP+clin lasso+xgboost

{

  load(file="JHU_DMP_new.Rdata")

  X <- JHU_DMP_new[,colnames(JHU_DMP_new) %in% c("SEX",id3)]

  table(X$SEX)

  # 0   1 

  # 121  50 

  X <- data.frame(X)

  X <- rownames_to_column(X,"ID")

  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\\20210817deepfm_pdata_sex_test.txt",row.names = F)

  write.table(X,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210817deepfm_feature_dmp_lassoxgboost_sex_test.csv",row.names = F,sep=",")

  write.table(X[,colnames(X) %in% c("ID","AGE8","Sulfonamides","BMI8","Albumin_urine","CREAT8")],

              "D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210817deepfm_feature_dmp_lassoxgboost_ehr_sex_test.csv",row.names = F,sep=",")

  write.table(X[,!colnames(X) %in% c("AGE8","Sulfonamides","BMI8","Albumin_urine","CREAT8")],

              "D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210817deepfm_feature_dmp_lassoxgboost_cpg_sex_test.csv",row.names = F,sep=",")

}



#add sex feature###########

#train old: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("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","SEX",id3)]

  X = X[,c(2,1,3:32)]

  X <- data.frame(X)

  X <- rownames_to_column(X,"ID")

  colnames(X)[2] <- c("target")#chf

  write.table(pdata,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210817deepfm_pdata_sex_train.txt",row.names = F)

  write.table(X,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210817deepfm_feature_dmp_lassoxgboost_sex.csv",row.names = F,sep=",")

  # write.table(X[,colnames(X) %in% c("ID","target","AGE8","Sulfonamides","BMI8","Albumin_urine","CREAT8")],

  #             "D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210817deepfm_feature_dmp_lassoxgboost_ehr_sex.csv",row.names = F,sep=",")

  # write.table(X[,!colnames(X) %in% c("AGE8","Sulfonamides","BMI8","Albumin_urine","CREAT8")],

  #             "D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210817deepfm_feature_dmp_lassoxgboost_cpg_sex.csv",row.names = F,sep=",")

}



#test old sex:DMP+clin lasso+xgboost

{

  load(file="JHU_DMP_new.Rdata")

  X <- JHU_DMP_new[,colnames(JHU_DMP_new) %in% c("SEX",id3)]

  table(X$SEX)

  # 0   1 

  # 121  50 

  X <- data.frame(X)

  X <- rownames_to_column(X,"ID")

  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\\20210817deepfm_pdata_sex_test.txt",row.names = F)

  write.table(X,"D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210817deepfm_feature_dmp_lassoxgboost_sex_test.csv",row.names = F,sep=",")

  write.table(X[,colnames(X) %in% c("ID","AGE8","Sulfonamides","BMI8","Albumin_urine","CREAT8")],

              "D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210817deepfm_feature_dmp_lassoxgboost_ehr_sex_test.csv",row.names = F,sep=",")

  write.table(X[,!colnames(X) %in% c("AGE8","Sulfonamides","BMI8","Albumin_urine","CREAT8")],

              "D:\\anaconda-python\\learn_DL\\Basic-DeepFM-model\\data\\new_1126\\20210817deepfm_feature_dmp_lassoxgboost_cpg_sex_test.csv",row.names = F,sep=",")

}