R/deepsurv.R

In SydneyBioX/SurvBenchmark_package: survival approaches benchmarking

#' deepsurv model
#'
#'
#' @param r a numeric value, a seed to run this method
#' @param data a dataframe, the data used to performance this survival model
#' @param cvK a numeric value, cross-validation fold
#' @param fitform_ogl a Surv object from package survival, the survival function
#' @param formula1 a Surv object from package survival, to calculate a version of the brier score, details please check package pec
#' @param formula2 a Surv object from package survival, to calculate a version of the brier score, details please check package pec
#' @param formula3 a Surv object from package survival, to calculate a version of the brier score, details please check package pec
#' @param formula4 a Surv object from package survival, to calculate a version of the brier score, details please check package pec
#' @param timess a numeric vector of length 15, contains time points to get the time-dependent AUC values
#' @return a data.frame with all evaluation measurements in all columns and rows are each fold results from cross-validation
#' @examples
#' data("exampledt", package = "SurvBenchmark")
#' fitform_ogl=survival::Surv(time,status)~.
#' formula1=fitform_ogl
#' formula2=fitform_ogl
#' formula3=survival::Surv(time,status)~1
#' formula4=survival::Surv(time,status)~1
#' form1=as.formula(~.)
#' timess=seq(as.numeric(summary(cancerdt2_1$time)[2]),as.numeric(summary(cancerdt2_1$time)[5]),(as.numeric(summary(cancerdt2_1$time)[5])-as.numeric(summary(cancerdt2_1$time)[2]))/14)

#' want=deepsurv_fun(1,cancerdt2_1[,-dim(cancerdt2_1)[2]],5,fitform_ogl,formula1,formula2,formula3,formula4,timess);
#' @export



# install_pycox(
#   method = "auto",
#   conda = "auto",
#   pip = TRUE,
#   install_torch = TRUE
# )
#
#
# if (requireNamespaces("reticulate")) {
#   # all defaults
#   deephit(data = simsurvdata(50))
#   # common parameters
#   deephit(data = simsurvdata(50), frac = 0.3, activation = "relu",
#           num_nodes = c(4L, 8L, 4L, 2L), dropout = 0.1, early_stopping = TRUE, epochs = 100L,
#           batch_size = 32L)
# }
#
# model1=deephit(data = simsurvdata(50), frac = 0.3, activation = "relu",
#                num_nodes = c(4L, 8L, 4L, 2L), dropout = 0.1, early_stopping = TRUE, epochs = 100L,
#                batch_size = 32L)
#
# if (requireNamespaces("reticulate")) {
#   fit <- model1
#   # predict survival matrix and relative risks
#   predict(fit, simsurvdata(10), type = "all")
#   # return as distribution
#   if (requireNamespaces("distr6")) {
#     result1=predict(fit, simsurvdata(10), distr6 = FALSE)
#   }
# }
#
# result1
#
# if (requireNamespaces("reticulate")) {
#   # all defaults
#   deepsurv(data = simsurvdata(50))
#   # common parameters
#   model2=deepsurv(data = simsurvdata(50), frac = 0.3, activation = "relu",
#                   num_nodes = c(4L, 8L, 4L, 2L), dropout = 0.1, early_stopping = TRUE, epochs = 100L,
#                   batch_size = 32L)
# }
#
# result2=predict(model2, simsurvdata(10), distr6 = FALSE)
# result2
#

deepsurv_fun=function(r,data,cvK,fitform_ogl,formula1,formula2,formula3, formula4, timess){
  if (! is.numeric(r)) stop("Input seed is wrong")
  if (! is.numeric(cvK)) stop("Input cross-validation fold number is wrong")
  if (is.null(dim(data))) stop("Input data is wrong")
  if (length(timess)!=15) stop("Wrong time vector length")
  if (class(timess)!= "numeric") stop("Wrong time vector type")
  set.seed(r)
  print(r)
  cvSets = cvTools::cvFolds(nrow(data), cvK)  # permute all the data, into 5 folds
  bicfun=purrr::possibly(function(j){
    test_id = cvSets$subsets[cvSets$which == j]
    test = data[test_id, ]
    train = data[-test_id, ]
    x_train=as.matrix(subset(train, select=-c(status,time)))
    y_train = cbind(time = train$time, status = train$status)

    x_test=as.matrix(subset(test,select=-c(status,time)))
    y_test = cbind(time = test$time, status = test$status)

    # # data normalization
    # mean <- apply(as.matrix(x_train), 2, mean)
    # std <- apply(as.matrix(x_train), 2, sd)
    # x_train <- scale(x_train, center = mean, scale = std)
    #
    # # data normalization
    # x_test<- scale(x_test, center = mean, scale = std)

    model=survivalmodels::deepsurv(data = train, frac = 0.3, activation = "relu",
                      num_nodes = c(4L, 8L, 4L, 2L), dropout = 0.1, early_stopping = TRUE, epochs = 100L,
                      batch_size = 32L)

    pred_tr=predict(model, train, distr6 = FALSE)
    pred_tr=pred_tr[,round(dim(pred_tr)[2]/2,digits = 0)]
    pred_te=predict(model, test, distr6 = FALSE)
    pred_te=pred_te[,round(dim(pred_te)[2]/2,digits = 0)]

    #harrel cindex
    harrelC1 <- Hmisc::rcorr.cens(pred_te,with(test,Surv(time,status)))
    hc<-harrelC1["C Index"]
    #begg cindex
    lp<- -pred_tr
    lpnew <- -pred_te
    Surv.rsp <- survival::Surv(train$time, train$status)
    Surv.rsp.new <- survival::Surv(test$time, test$status)
    bc <- NA
    #uno cindex
    unoc<-survAUC::UnoC(Surv.rsp, Surv.rsp.new, lpnew)
    #gh cindex
    ghc<-NA
    #br
    briers1 <- survAUC::predErr(Surv.rsp, Surv.rsp.new, lp, lpnew,times=test$time, type = "brier", int.type = "unweighted")$error
    br1<-sum(na.omit(briers1))
    briers2<-survAUC::predErr(Surv.rsp, Surv.rsp.new, lp, lpnew,times=test$time, type = "brier", int.type = "weighted")$error
    br2<-sum(na.omit(briers2))
    ibsfun1=purrr::possibly(function(modell){
      briers3 <- pec::pec(list("cox1"=modell),data=test,formula=formula1,cens.model="cox")
      return(crps(briers3)[2])
    },otherwise = NA)
    #briers3 <- pec(list("cox1"=original_cox1),data=test,formula=Surv(tx_gperiod,tx_gstatus)~recip_sex+recip_eth+recip_age+recip_height+recip_weight+recip_smoker+recip_lung+recip_coronary+recip_pvd+recip_cvd+recip_diabetes+recip_waittime+donor_age+donor_sex+donor_height+donor_weight+donor_causedeath_cva+donor_dcd+donor_diabetes+donor_ht+donor_smoker+donor_creatinine+tx_ischaemia+tx_misa+tx_misb+tx_misdr,cens.model="cox")
    #bs3[j]<-crps(briers3)[2]
    br3<-ibsfun1(out.rsf.1)
    ibsfun2=purrr::possibly(function(modell){
      briers4 <- pec::pec(list("cox1"=modell),data=test,formula=formula2,cens.model="marginal")
      return(crps(briers4)[2])
    },otherwise = NA)
    br4<-ibsfun2(out.rsf.1)
    ibsfun3=purrr::possibly(function(modell){
      briers5 <- pec::pec(list("cox1"=modell),data=test,formula=formula3,cens.model="cox")
      return(crps(briers5)[2])
    },otherwise = NA)
    br5<-ibsfun3(out.rsf.1)
    ibsfun4=purrr::possibly(function(modell){
      briers6 <- pec::pec(list("cox1"=modell),data=test,formula=formula4,cens.model="marginal")
      return(crps(briers6)[2])
    },otherwise = NA)
    br6<-ibsfun4(out.rsf.1)
    #time-dependent auc
    times <- timess
    AUC_CD <- survAUC::AUC.uno(Surv.rsp, Surv.rsp.new, lpnew, times)
    a1=AUC_CD$auc[1]
    a2=AUC_CD$auc[2]
    a3=AUC_CD$auc[3]
    a4=AUC_CD$auc[4]
    a5=AUC_CD$auc[5]
    a6=AUC_CD$auc[6]
    a7=AUC_CD$auc[7]
    a8=AUC_CD$auc[8]
    a9=AUC_CD$auc[9]
    a10=AUC_CD$auc[10]
    a11=AUC_CD$auc[11]
    a12=AUC_CD$auc[12]
    a13=AUC_CD$auc[13]
    a14=AUC_CD$auc[14]
    a15=AUC_CD$auc[15]
    a=AUC_CD$iauc
    return(c(hc,bc,unoc,ghc,br1,br2,br3,br4,br5,br6,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12,a13,a14,a15,a))},otherwise=NA)

  cv5_result=rbind.data.frame(bicfun(1),bicfun(2),bicfun(3),bicfun(4),bicfun(5))
  #print(dim(bicfun(1)))
  #print(dim(bicfun(2)))
  #print(dim(bicfun(3)))
  #print(dim(bicfun(4)))
  #print(dim(bicfun(5)))
  #colnames(cv5_result)=c("hc","bc","unoc","ghc","br1","br2","br3","br4","br5","br6","a1","a2","a3","a4","a5","a6","a7","a8","a9","a10","a11","a12","a13","a14","a15","a")

  # want=cbind.data.frame(hc_acc5,bc_acc5,unoc_acc5,ghc_acc5,bs1,bs2,bs3,bs4,bs5,bs6,auc1,auc2,auc3,auc4,auc5,auc6,auc7,auc8,auc9,auc10,auc11,auc12,auc13,auc14,auc15,auc)
  return(cv5_result)}