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Cindex_sim_01.R
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Cindex_sim_01.R
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#generate train data
library(xgboost)
n <- 400; p <- 100
beta <- c(rep(1,10),rep(0,p-10))
x <- matrix(rnorm(n*p),n,p)
real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta)))
cens.time <- rexp(n,rate=1/10)
status <- ifelse(real.time <= cens.time,1,0)
obs.time <- ifelse(real.time <= cens.time,real.time,cens.time)
#convert to xgboost data structure
t=200
dtrain<-list(data=x[1:t,],label=obs.time[1:t])
attr(dtrain,'censor')<-status[1:t]
Dtrain<-xgb.DMatrix(dtrain$data,label=dtrain$label)
attr(Dtrain,"censor")<-attr(dtrain,"censor")
#Dtrain2<-xgb.DMatrix(dtrain$data[,1:10],label=dtrain$label)
#attr(Dtrain2,"censor")<-attr(dtrain,"censor")
#generate test data
#n2 <- 200; p2 <- 100
#beta2 <- c(rep(1,10),rep(0,p2-10))
#x2 <- matrix(rnorm(n2*p2),n2,p2)
#real.time2 <- -(log(runif(n2)))/(10*exp(drop(x2 %*% beta2)))
#cens.time2 <- rexp(n2,rate=1/10)
#status2 <- ifelse(real.time2 <= cens.time2,1,0)
#obs.time2 <- ifelse(real.time2 <= cens.time2,real.time2,cens.time2)
#convert to xgboost data structure
t=200
dtest<-list(data=x[(t+1):n,],label=obs.time[(t+1):n])
attr(dtest,'censor')<-status[(t+1):n]
Dtest<-xgb.DMatrix(dtest$data,label=dtest$label)
attr(Dtest,"censor")<-attr(dtest,"censor")
#Dtest2<-xgb.DMatrix(dtest$data[,1:10],label=dtest$label)
#attr(Dtest2,"censor")<-attr(dtest,"censor")
library(xgboost)
#define objective function and evaluation function
mylossobj2<-function(preds, dtrain) {
alpha=2
labels <- getinfo(dtrain, "label") #labels<-dtrain$label
#print(labels)
censor<-attr(dtrain,"censor")
ord<-order(labels)
ran=rank(labels)
#print(ord)
#foo<<-censor
#compute the first gradient
d=censor[ord] #status
etas=preds[ord] #linear predictor
n=length(etas)
temp1l=matrix(etas[1:(n-1)],nrow=n-1,ncol=n-1)-matrix(etas[2:n],nrow=n-1,ncol=n-1,byrow=1)
#print(temp1l)
temp2l=exp(alpha)^temp1l
temp2l[lower.tri(temp2l)]=0
temp3l=(1+temp2l)^2
#print(temp3l)
temp4l=-alpha*temp2l/temp3l
temp4l[lower.tri(temp4l)]=0
#print(temp4l)
temp4r=-temp4l
gl=c(apply(temp4l,1,sum),0)*d
gr=c(0,-apply(temp4l,2,sum))
grad=gl+gr
grad=grad[ran]
#print(grad)
#derive hessian
# H1=outer(haz,rsk^2,'/')
# H1=t(t(H1)*rsk)
temp5l=-(alpha*temp2l*temp3l-temp2l*2*temp3l*alpha*temp2l)/(1+temp2l)^4
temp5l[lower.tri(temp5l)]=0
hl=c(apply(temp5l,1,sum),0)*d
hr=c(0,apply(temp5l,2,sum))
hess=hl+hr
hess=hess[ran]
#hess=rep(0,length(grad))
# Return the result as a list
return(list(grad = grad, hess = hess))
}
evalerror2 <- function(preds, dtrain) {
labels <- getinfo(dtrain, "label") #labels<-dtrain$label
censor<-attr(dtrain,"censor") #not working!
#foo<<-censor
#compute the first gradient
ord<-order(labels)
d=censor[ord] #status
etas=preds[ord] #linear predictor
haz<-as.numeric(exp(etas)) #w[i]
rsk<-rev(cumsum(rev(haz)))
err <- -sum(d*(etas-log(rsk)))
return(list(metric = "deviance", value = err))
}
#fit1<-xgboost(data = Dtrain, nrounds=350,objective = mylossobj2,eval_metric = evalerror2)
#xgb.importance(model=fit1)
##parameter tuning
best_param = list()
best_seednumber = 1234
best_loss = Inf
best_loss_index = 0
for (iter in 1:500) {
param <- list(objective = mylossobj2,
eval_metric = evalerror2,
#num_class = 12,
max_depth = sample(6:13, 1),
eta = runif(1, .01, .3),
gamma = runif(1, 0.0, 0.2),
subsample = runif(1, .6, .9),
colsample_bytree = runif(1, .5, 1),
min_child_weight = sample(1:40, 1),
max_delta_step = sample(1:10, 1),
colsample_bylevel=runif(1, .5, 1),
lambda=runif(1,0,2),
alpha=runif(1,0,2)
)
cv.nround = 500
cv.nfold = 5
seed.number = sample.int(10000, 1)[[1]]
set.seed(seed.number)
mdcv <- xgb.cv(data=Dtrain, params = param, nthread=6,
nfold=cv.nfold, nrounds=cv.nround,
verbose = F)
min_loss = min(mdcv$evaluation_log[,'test_deviance_mean'])
min_loss_index = which.min(as.numeric(unlist(mdcv$evaluation_log[,'test_deviance_mean'])))
if (min_loss < best_loss) {
best_loss = min_loss
best_loss_index = min_loss_index
best_seednumber = seed.number
best_param = param
}
print(iter)
}
nround = best_loss_index
set.seed(best_seednumber)
best_param$objective=mylossobj2
md <- xgboost(data=Dtrain, params=best_param, nrounds=nround,nthread=6)
#md2 <- xgboost(data=Dtrain2, params=best_param, nrounds=nround, nthread=6)
xgb.importance(model=md)
#predict(md,Dtrain)
#order(predict(md,Dtrain2))
#rev(order(obs.time))
#concordance index for test data
library(survival)
survConcordance(Surv(obs.time[1:t], status[1:t]) ~predict(md,Dtrain))
survConcordance(Surv(obs.time[(t+1):n], status[(t+1):n]) ~predict(md,Dtest))
#compare with gbm cox
library(gbm)
library(survival)
gbm1 <- gbm(Surv(obs.time[1:t],status[1:t])~ ., # formula
data=as.data.frame(x[1:t,]), # dataset
#weights=w,
#var.monotone=c(0,0,0), # -1: monotone decrease, +1: monotone increase, 0: no monotone restrictions
distribution="coxph",
n.trees=1000, # number of trees
shrinkage=0.001, # shrinkage or learning rate, 0.001 to 0.1 usually work
#interaction.depth=3, # 1: additive model, 2: two-way interactions, etc
bag.fraction = 0.5, # subsampling fraction, 0.5 is probably best
train.fraction = 0.8, # fraction of data for training, first train.fraction*N used for training
cv.folds = 5, # do 5-fold cross-validation
#n.minobsinnode = 10, # minimum total weight needed in each node
keep.data = TRUE,
verbose = TRUE) # print progress
summary(gbm1)
#aa=predict(gbm1,data=as.data.frame(x2))
survConcordance(Surv(obs.time[(t+1):n], status[(t+1):n]) ~ predict(gbm1,data=as.data.frame(x[(t+1):n,])))
#compare with coxboost
library(CoxBoost)
cbfit <- CoxBoost(time=obs.time[1:t],status=status[1:t],x=x[1:t,])
summary(cbfit)
#survConcordance(Surv(obs.time[1:t], status[1:t]) ~ as.vector(predict(cbfit)))
survConcordance(Surv(obs.time[(t+1):n], status[(t+1):n]) ~ as.vector(predict(cbfit,newdata=x[(t+1):n,])))