library("xgboost")

library("ipred")

library("survival")

library("survivalROC")

library("glmnet")

library('kernlab')

library('plyr')

library('caret')

setwd('D:/brca')

kx2=read.csv('data_cox2.csv',row.names= 1)

sdata=read.csv('brca_go.csv',row.names= 1)

data1=t(sdata)

j=6

set.seed(j)

k=10

folds <- createFolds(as.data.frame(t(kx2)),k)

results<-matrix(nrow = k, ncol=6)

im=matrix(nrow=500,ncol=30)

eva<-matrix(nrow = nrow(kx2), ncol=k)

count=0

for(i in 1:k){

  testset <- kx2[folds[[i]],] 

  trainset <- kx2[-folds[[i]],] 

  te_xg=data1[folds[[i]],] 

  tr_xg=data1[-folds[[i]],] 

  x=trainset[,-c(1:2)]

  x=as.matrix(x)

  tc=trainset$time

  tc[tc==0]=0.001

  y=Surv(tc,trainset$status)

  cv.fit<-cv.glmnet(x,y,family="cox",maxit=10000,alpha=0,nfold=5)

  fit<-glmnet(x,y,family="cox",alpha=0)

  tt=predict(fit,x,s=cv.fit$lambda.min)

  x2=testset[,-c(1:2)]

  x2=as.matrix(x2)

  tc2=testset$time

  tc2[tc2==0]=0.001

  y2=Surv(tc2,testset$status)

  tt2=predict(fit,x2,s=cv.fit$lambda.min)

  y_xgtr=tt;

  x_xgtr=tr_xg

  x_xgtr=as.matrix(x_xgtr)

  x_xgte=te_xg

  x_xgte=as.matrix(x_xgte)

  bst <- xgboost(x_xgtr, y_xgtr, 

                 max_depth =3, eta =0.25, nrounds =6, min_child_weight=2,

                 objective = "reg:linear",eval_metric = "rmse")

  pred <- predict(bst, x_xgte)

  eva[(count+1):(count+nrow(tt2)),1]<-tc2

  eva[(count+1):(count+nrow(tt2)),2]<-testset$status

  eva[(count+1):(count+nrow(tt2)),3]=tt2

  eva[(count+1):(count+nrow(tt2)),4]=pred

  count=count+nrow(tt2)

}

tc3=eva[,1]

st3=eva[,2]

rk3=eva[,3]

rk4=eva[,4]

y3=Surv(tc3,st3)

ci_Cox=survConcordance(formula = y3~ rk3)

ci_Cox$concordance

ci_XGB=survConcordance(formula = y3~ rk4)

ci_XGB$concordance