examples/DBRM supplementaries/kepler and mass/kepler G1.r

In aliaksah/EMJMCMC2016: EMJMCMC

#read in the package most recent version
source("https://raw.githubusercontent.com/aliaksah/EMJMCMC2016/master/R/the_mode_jumping_package4.r")

#a function for cleaning up after the parallel computing is finished
library(inline)
includes = '#include <sys/wait.h>'
code = 'int wstat; while (waitpid(-1, &wstat, WNOHANG) > 0) {};'
wait = cfunction(body=code, includes=includes, convention='.C')

#specify the estimator function returning p(Y|m)p(m), model selection criteria and the vector of the modes for the beta coefficients
estimate.gamma.cpen = function(formula, data,r = 1.0/223.0,logn=log(223.0),relat=c("cosi","sigmoid","tanh","atan","sini","troot"))
{
  fparam=NULL
  fmla.proc=as.character(formula)[2:3]
  fobserved = fmla.proc[1]
  fmla.proc[2]=stri_replace_all(str = fmla.proc[2],fixed = " ",replacement = "")
  fmla.proc[2]=stri_replace_all(str = fmla.proc[2],fixed = "\n",replacement = "")
  fparam =stri_split_fixed(str = fmla.proc[2],pattern = "+I",omit_empty = F)[[1]]
  sj=(stri_count_fixed(str = fparam, pattern = "*"))
  sj=sj+(stri_count_fixed(str = fparam, pattern = "+"))
  for(rel in relat)
    sj=sj+(stri_count_fixed(str = fparam, pattern = rel))
  sj=sj+1
  tryCatch(capture.output({
    out = glm(formula = formula,data = data, family = gaussian)
    mlik = (-(out$deviance -2*log(r)*sum(sj)))/2
    waic = -(out$deviance + 2*out$rank)
    dic =  -(out$deviance + logn*out$rank)
    summary.fixed =list(mean = coefficients(out))

  }, error = function(err) {
    print(err)
    mlik = -10000
    waic = -10000
    dic =  -10000
    summary.fixed =list(mean = array(0,dim=length(fparam)))
  }))
  return(list(mlik = mlik,waic = waic , dic = dic,summary.fixed =summary.fixed))

}

#specify the function for parallel computations
parall.gmj <- mclapply


#a function for postproceeding the features at the end of the run
simplifyposteriors=function(X,posteriors,th=0.0001,thf=0.2)
{
  posteriors=posteriors[-which(posteriors[,2]<th),]
  rhash=hash()
  for(i in 1:length(posteriors[,1]))
  {
    expr=posteriors[i,1]
    print(expr)
    res=model.matrix(data=X,object = as.formula(paste0("RadiusJpt~",expr)))
    ress=c(stri_flatten(round(res[,2],digits = 4),collapse = ""),stri_flatten(res[,2],collapse = ""),posteriors[i,2],expr)
    if(!((ress[1] %in% values(rhash))))
      rhash[[ress[1]]]=ress
    else
    {
      if(ress[1] %in% keys(rhash))
      {
        rhash[[ress[1]]][3]= (as.numeric(rhash[[ress[1]]][3]) + as.numeric(ress[3]))
        if(stri_length(rhash[[ress[1]]][4])>stri_length(expr))
          rhash[[ress[1]]][4]=expr
      }
    }

  }
  res=as.data.frame(t(values(rhash)[c(3,4),]))
  res$V1=as.numeric(as.character(res$V1))
  res=res[which(res$V1>thf),]
  res=res[order(res$V1, decreasing = T),]
  clear(rhash)
  rm(rhash)
  res[which(res[,1]>1),1]=1
  colnames(res)=c("posterior","tree")
  return(res)
}

#a set of nonlinearities that will be used in the DBRM model
cosi<-function(x)cos(x/180*pi)
sini<-function(x)sin(x/180*pi)
troot<-function(x)abs(x)^(1/3)

#specify the number of runs
MM = 100
#specify the number of threads used
M = 64
NM= 1000
#specify the number of features + 1 per model
compmax = 16
#specify some preliminary filtration of the features' tresholds
th=(10)^(-5)
thf=0.05


#a function that is run to perform analyses on each of the addressed threads
runpar=function(vect)
{

  set.seed(as.integer(vect[22]))
  do.call(runemjmcmc, vect[1:21])
  vals=values(hashStat)
  fparam=mySearch$fparam
  cterm=max(vals[1,],na.rm = T)
  ppp=mySearch$post_proceed_results_hash(hashStat = hashStat)
  post.populi=sum(exp(values(hashStat)[1,][1:NM]-cterm),na.rm = T)
  clear(hashStat)
  rm(hashStat)
  rm(vals)
  gc()
  return(list(post.populi = post.populi, p.post =  ppp$p.post, cterm = cterm, fparam = fparam))
}

#perform MM runs of GMJMCMC/RGMJMCMC on M threads each
for(j in 1:MM)
{
  tryCatch({

    set.seed(j)

    #read and prepare the data
    X=read.csv("exa1.csv")
    data.example = as.data.frame(X)

    #specify the initial formula
    formula1 = as.formula(paste(colnames(X)[5],"~ 1 +",paste0(colnames(X)[-5],collapse = "+")))

    #specify tuning parameters of the algorithm for exploring DBRM of interest
    #notice that allow_offsprings=3 corresponds to the GMJMCMC runs and
    #allow_offsprings=4 -to the RGMJMCMC runs
    vect=list(formula = formula1,data = data.example,estimator = estimate.gamma.cpen,estimator.args =  list(data = data.example),recalc_margin = 249, save.beta = F,interact = T,outgraphs=F,relations=c("cosi","sigmoid","tanh","atan","sini","troot"),relations.prob =c(0.1,0.1,0.1,0.1,0.1,0.1),interact.param=list(allow_offsprings=3,mutation_rate = 250,last.mutation=10000, max.tree.size = 5, Nvars.max =15,p.allow.replace=0.9,p.allow.tree=0.01,p.nor=0.9,p.and = 0.9),n.models = 10000,unique =T,max.cpu = 4,max.cpu.glob = 4,create.table = F,create.hash = T,pseudo.paral = T,burn.in = 100,print.freq = 1000,advanced.param = list(
      max.N.glob=as.integer(10),
      min.N.glob=as.integer(5),
      max.N=as.integer(3),
      min.N=as.integer(1),
      printable = F))

    params = list(vect)[rep(1,M)]

    #specify additional information to be used on different threads (e.g. seeds)
    for(i in 1:M)
    {
      params[[i]]$cpu=i*j
      params[[i]]$simul="scenario_JM_"
      params[[i]]$simid=j
    }
    #perform garbage collection
    gc()
    #explore DBRM on M threads in parallel using the GMJMCMC/RGMJMCMC algorithm
    print(paste0("begin simulation ",j))
    results=parall.gmj(X = params,FUN = runpar,mc.preschedule = F, mc.cores = M)
    wait()

    #prepare the data structures for final analysis of the runs
    resa=array(data = 0,dim = c(compmax,M*3))
    post.popul = array(0,M)
    max.popul = array(0,M)
    nulls=NULL

    #check which threads had non-zero exit status
    not.null=1
    for(k in 1:M)
    {
      if(is.character(results[[k]]))
      {
        nulls=c(nulls,k)
        next
      }
      if(length(results[[k]])==0)
      {
        nulls=c(nulls,k)
        next
      }
      else
      {
        not.null = k
      }

    }

    #for all of the successful runs collect the results into the data structures
    for(k in 1:M)
    {
      if(k %in% nulls)
      {
        results[[k]]=results[[not.null]]
      }
      max.popul[k]=results[[k]]$cterm
      post.popul[k]=results[[k]]$post.populi
      if(length(resa[,k*3-2])==(length(results[[k]]$fparam)+1))
      {
        resa[,k*3-2]=c(results[[k]]$fparam,"Post.Gen.Max")
        resa[,k*3-1]=c(results[[k]]$p.post,results[[k]]$cterm)
        resa[,k*3]=rep(post.popul[k],length(results[[k]]$p.post)+1)
      }else
      {
        resa[,k*3-2]=rep(results[[k]]$fparam[1],length(resa[,k*3-2]))
        resa[,k*3-1]=rep(0,length(resa[,k*3-1]))
        resa[,k*3]=rep(-10^9,length(resa[,k*3]))
      }

    }

    #delete the unused further variables and perfrom garbage collection
    gc()
    rm(results)
    #renormalize estimates of the marginal inclusion probabilities
    #based on all of the runs
    ml.max=max(max.popul)
    post.popul=post.popul*exp(-ml.max+max.popul)
    p.gen.post=post.popul/sum(post.popul)
    hfinal=hash()
    for(ii in 1:M)
    {
      resa[,ii*3]=p.gen.post[ii]*as.numeric(resa[,ii*3-1])
      resa[length(resa[,ii*3]),ii*3]=p.gen.post[ii]
      if(p.gen.post[ii]>0)
      {
        for(jj in 1:(length(resa[,ii*3])-1))
        {
          if(resa[jj,ii*3]>0)
          {
            if(as.integer(has.key(hash = hfinal,key =resa[jj,ii*3-2]))==0)
              hfinal[[resa[jj,ii*3-2]]]=as.numeric(resa[jj,ii*3])
            else
              hfinal[[resa[jj,ii*3-2]]]=hfinal[[resa[jj,ii*3-2]]]+as.numeric(resa[jj,ii*3])
          }

        }
      }
    }

    posteriors=values(hfinal)
    print(posteriors)
    #delete the unused variables
    clear(hfinal)
    rm(hfinal)
    rm(resa)
    rm(post.popul)
    rm(max.popul)
    posteriors=as.data.frame(posteriors)
    posteriors=data.frame(X=row.names(posteriors),x=posteriors$posteriors)
    posteriors$X=as.character(posteriors$X)
    #simplify the found features and their posteriors
    tryCatch({
      res1=simplifyposteriors(X = X,posteriors = posteriors, th,thf)
      row.names(res1)=1:dim(res1)[1]
      write.csv(x =res1,row.names = F,file = paste0("postJA64_",j,".csv"))
    },error = function(err){
      print("error")
      write.csv(x =posteriors,row.names = F,file = paste0("posteriorsJA64_",j,".csv"))
    },finally = {

      print(paste0("end simulation ",j))

    })
    #delete the unused further variables and perfrom garbage collection
    rm(X)
    rm(data.example)
    rm(vect)
    rm(params)
    gc()
    print(paste0("end simulation ",j))
  },error = function(err){
    print("error")
    j=j-1
    print(paste0("repeat  simulation ",j))
  },finally = {

    print(paste0("end simulation ",j))
    #delete the unused further variables and perfrom garbage collection
    rm(X)
    rm(data.example)
    rm(vect)
    rm(params)
    gc()
  })

}