# ssh -X -Y -l aliaksah abel.uio.no
# scp -r /usit/abel/u1/aliaksah/simulations/scenario1 aliaksah@pittheus.uio.no://mn/sarpanitu/ansatte-u2/aliaksah/Desktop/package/simulations
library(pracma)
source("https://raw.githubusercontent.com/aliaksah/EMJMCMC2016/master/R/the_mode_jumping_package2.r")
library(inline)
includes <- '#include <sys/wait.h>'
code <- 'int wstat; while (waitpid(-1, &wstat, WNOHANG) > 0) {};'
wait <- cfunction(body=code, includes=includes, convention='.C')
estimate.logic.bern.tCCH <- function(formula = NULL,y.id = 51, data, n=1000, m=50, r = 1, p.a = 1, p.b = 2, p.r = 1.5, p.s = 0, p.v=-1, p.k = 1)
{
if(is.null(formula))
return(list(mlik = -10000 + rnorm(1,0,1),waic =10000 , dic = 10000,summary.fixed =list(mean = 1)))
X <- scale(model.matrix(object = formula,data = data),center = T,scale = F)
X[,1] = 1
fmla.proc<-as.character(formula)[2:3]
#out <- glm.fit(x =X,y = data[,y.id],family = binomial())
#out <- glm(formula = formula,data=data,family = binomial())
out <- glm(formula = as.formula(paste0(fmla.proc[1],"~X+0")),data=data,family = binomial())
beta=coef(out)[-1]
if(length(which(is.na(beta)))>0)
{
return(list(mlik = -10000+ rnorm(1,0,1),waic = 10000 , dic = 10000,summary.fixed =list(mean = 0)))
}
p <- out$rank
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = " ",replacement = "")
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = "\n",replacement = "")
fparam <-stri_split_fixed(str = fmla.proc[2],pattern = "+",omit_empty = F)[[1]]
sj<-(stri_count_fixed(str = fparam, pattern = "&"))
sj<-sj+(stri_count_fixed(str = fparam, pattern = "|"))
sj<-sj+1
p.v = (n+1)/(p+1)
sout = summary(out)
J.a.hat = 1/sout$cov.unscaled[1,1]
if(length(beta)>0&&length(beta)==(dim(sout$cov.unscaled)[1]-1)&&length(which(is.na(beta)))==0)
{
Q = t(beta)%*%solve(sout$cov.unscaled[-1,-1])%*%beta
}else{
return(list(mlik = -10000+ rnorm(1,0,1),waic = 10000 , dic = 10000,summary.fixed =list(mean = 0)))
}
Jprior <- sum(log(factorial(sj)/((m^sj)*2^(2*sj-2))))
mlik = (logLik(out)- 0.5*log(J.a.hat) - 0.5*p*log(p.v) -0.5*Q/p.v + log(beta((p.a+p)/2,p.b/2)) + log(phi1(p.b/2,p.r,(p.a+p.b+p)/2,(p.s+Q)/2/p.v,1-p.k))+Jprior + p*log(r)+n)
if(is.na(mlik)||mlik==-Inf)#||mlik==Inf)
mlik = -10000+ rnorm(1,0,1)
return(list(mlik = mlik,waic = AIC(out) , dic = BIC(out),summary.fixed =list(mean = coefficients(out))))
}
estimate.logic.bern.tCCH.alt <- function(formula = NULL,y.id = 51, data, n=1000, m=50, r = 1, p.a = 1, p.b = 2, p.r = 1.5, p.s = 0, p.v=-1, p.k = 1)
{
if(is.null(formula))
return(list(mlik = -10000 + rnorm(1,0,1),waic =10000 , dic = 10000,summary.fixed =list(mean = 1)))
X <- scale(model.matrix(object = formula,data = data),center = T,scale = F)
X[,1] = 1
fmla.proc<-as.character(formula)[2:3]
#out <- glm.fit(x =X,y = data[,y.id],family = binomial())
#out <- glm(formula = formula,data=data,family = binomial())
out <- glm(formula = as.formula(paste0(fmla.proc[1],"~X+0")),data=data,family = binomial())
beta=coef(out)[-1]
if(length(which(is.na(beta)))>0)
{
return(list(mlik = -10000+ rnorm(1,0,1),waic = 10000 , dic = 10000,summary.fixed =list(mean = 0)))
}
p <- out$rank
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = " ",replacement = "")
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = "\n",replacement = "")
fparam <-stri_split_fixed(str = fmla.proc[2],pattern = "+",omit_empty = F)[[1]]
sj<-(stri_count_fixed(str = fparam, pattern = "&"))
sj<-sj+(stri_count_fixed(str = fparam, pattern = "|"))
sj<-sj+1
p.v = (n+1)/(p+1)
sout = summary(out)
J.a.hat = 1/sout$cov.unscaled[1,1]
if(length(beta)>0&&length(beta)==(dim(sout$cov.unscaled)[1]-1)&&length(which(is.na(beta)))==0)
{
Q = t(beta)%*%solve(sout$cov.unscaled[-1,-1])%*%beta
}else{
return(list(mlik = -10000+ rnorm(1,0,1),waic = 10000 , dic = 10000,summary.fixed =list(mean = 0)))
}
Jprior <- sum(log(factorial(sj)/((m^sj)*2^(2*sj-2))))
mlik = (logLik(out) - 0.5*log(J.a.hat) + 0.5*log(p.v) - 0.5*(p+1)*log(Q/2) + log(gammainc(x = Q/2/p.v, a = (p+1)/2)[1])+Jprior +n)
if(length(mlik)==0||mlik==-Inf||mlik==Inf)
mlik = -10000+ rnorm(1,0,1)
return(list(mlik = mlik,waic = AIC(out) , dic = BIC(out),summary.fixed =list(mean = coefficients(out))))
}
estimate.logic.glm <- function(formula, data, family = binomial(), n=1000, m=50, r = 1)
{
out <- glm(formula = formula,data = data, family=family)
p <- out$rank
fmla.proc<-as.character(formula)[2:3]
fobserved <- fmla.proc[1]
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = " ",replacement = "")
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = "\n",replacement = "")
fparam <-stri_split_fixed(str = fmla.proc[2],pattern = "+",omit_empty = F)[[1]]
sj<-(stri_count_fixed(str = fparam, pattern = "&"))
sj<-sj+(stri_count_fixed(str = fparam, pattern = "|"))
sj<-sj+1
Jprior <- sum(log(factorial(sj)/((m^sj)*2^(2*sj-2))))
mlik = (-(out$deviance + log(n)*(out$rank)) + 2*(Jprior))/2+n
if(mlik==-Inf)
mlik = -10000
return(list(mlik = mlik,waic = -(out$deviance + 2*out$rank) , dic = -(out$deviance + log(n)*out$rank),summary.fixed =list(mean = coefficients(out))))
}
parall.gmj <<- mclapply
simplifyposteriors<-function(X,posteriors,th=0.0001,thf=0.5)
{
posteriors<-posteriors[-which(posteriors[,2]<th),]
rhash<-hash()
for(i in 1:length(posteriors[,1]))
{
expr<-posteriors[i,1]
print(expr)
res<-model.matrix(data=X,object = as.formula(paste0("Y1~",expr)))
res[,1]<-res[,1]-res[,2]
ress<-c(stri_flatten(res[,1],collapse = ""),stri_flatten(res[,2],collapse = ""),posteriors[i,2],expr)
if(!(ress[1] %in% values(rhash)||(ress[2] %in% values(rhash))))
rhash[[ress[1]]]<-ress
else
{
if(ress[1] %in% keys(rhash))
{
rhash[[ress[1]]][3]<- (as.numeric(rhash[[ress[1]]][3]) + as.numeric(ress[3]))
if(stri_length(rhash[[ress[1]]][4])>stri_length(expr))
rhash[[ress[1]]][4]<-expr
}
else
{
rhash[[ress[2]]][3]<- (as.numeric(rhash[[ress[2]]][3]) + as.numeric(ress[3]))
if(stri_length(rhash[[ress[2]]][4])>stri_length(expr))
rhash[[ress[2]]][4]<-expr
}
}
}
res<-as.data.frame(t(values(rhash)[c(3,4),]))
res$V1<-as.numeric(as.character(res$V1))
res<-res[which(res$V1>thf),]
res<-res[order(res$V1, decreasing = T),]
clear(rhash)
rm(rhash)
res[which(res[,1]>1),1]<-1
colnames(res)<-c("posterior","tree")
return(res)
}
MM = 100
M = 64
NM= 1000
compmax = 16
th<-(10)^(-5)
thf<-0.05
paral<-function(X,FUN)
{
return(mclapply(X = X,FUN = FUN,mc.preschedule = F, mc.cores = 32))
}
runpar<-function(vect)
{
tryCatch({
set.seed(as.integer(vect[24]))
do.call(runemjmcmc, vect[1:23])
vals<-values(hashStat)
fparam<-mySearch$fparam
cterm<-max(vals[1,],na.rm = T)
ppp<-mySearch$post_proceed_results_hash(hashStat = hashStat)
post.populi<-sum(exp(values(hashStat)[1,][1:NM]-cterm),na.rm = T)
ret <- list(post.populi = post.populi, p.post = ppp$p.post, cterm = cterm, fparam = fparam)
if(length(cterm)==0){
print(ppp$p.post)
print(fparam)
print(cterm)
print(vals[1,1:50])
print(paste0("warning in thread",vect[24]))
vect[24]<-as.integer(vect[24])+as.integer(runif(1,1,10000))
ret <- runpar(vect)
}
},error = function(err){
print(paste0("error in thread",vect[24]))
print(err)
vect[24]<-as.integer(vect[24])+as.integer(runif(1,1,10000))
ret <- runpar(vect)
},finally = {
clear(hashStat)
rm(hashStat)
rm(vals)
gc()
# print(ret)
return(ret)
})
}
#print("wait 2 hours")
#Sys.sleep(7200)
for(j in 69:91)
{
tryCatch({
resa<-array(data = 0,dim = c(compmax,M*3))
post.popul <- array(0,M)
max.popul <- array(0,M)
set.seed(j)
X1<- as.data.frame(array(data = rbinom(n = 50*1000,size = 1,prob = runif(n = 50*1000,0,1)),dim = c(1000,50)))
Y1<- 0.4 -9*(X1$V4*X1$V17*X1$V30*X1$V10) + 9*(X1$V7*X1$V20*X1$V12) - 5*(X1$V9*X1$V2)
X1$Y1<-round(1.0/(1.0+exp(-Y1)))
formula1 = as.formula(paste(colnames(X1)[51],"~ 1 +",paste0(colnames(X1)[-c(51)],collapse = "+")))
data.example = as.data.frame(X1)
vect<-list(formula = formula1,data = X1,presearch = T,locstop = F ,estimator = estimate.logic.bern.tCCH,estimator.args = list(data = data.example,n = 1000, m = 50,r=1),recalc_margin = 250, save.beta = F,interact = T,relations = c("","lgx2","cos","sigmoid","tanh","atan","erf"),relations.prob =c(0.4,0.0,0.0,0.0,0.0,0.0,0.0),interact.param=list(allow_offsprings=1,mutation_rate = 300,last.mutation = 10000, max.tree.size = 4, Nvars.max = (compmax-1),p.allow.replace=0.9,p.allow.tree=0.4,p.nor=0.0,p.and = 0.9),n.models = 15000,unique = T,max.cpu = 4,max.cpu.glob = 4,create.table = F,create.hash = T,pseudo.paral = T,burn.in = 50,outgraphs=F,print.freq = 1000,advanced.param = list(
max.N.glob=as.integer(10),
min.N.glob=as.integer(5),
max.N=as.integer(3),
min.N=as.integer(1),
printable = F))
params <- list(vect)[rep(1,M)]
for(i in 1:M)
{
params[[i]]$cpu<-i
params[[i]]$simul<-"scenario_log_1_"
params[[i]]$simid<-j
}
gc()
print(paste0("begin simulation ",j))
results<-mclapply(X = params,FUN = runpar,mc.preschedule = FALSE, mc.cores = 32)
gc()
wait()
print(results)
resa<-array(data = 0,dim = c(compmax,M*3))
post.popul <- array(0,M)
max.popul <- array(0,M)
nulls<-NULL
not.null<-1
for(k in 1:M)
{
if(length(results[[k]])<=1||length(results[[k]]$cterm)==0)
{
nulls<-c(nulls,k)
next
}
else
{
not.null <- k
}
}
for(k in 1:M)
{
if(k %in% nulls)
{
results[[k]]<-results[[not.null]]
}
max.popul[k]<-results[[k]]$cterm
post.popul[k]<-results[[k]]$post.populi
resa[,k*3-2]<-c(results[[k]]$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]]$p.post)+1)
}
gc()
rm(results)
ml.max<-max(max.popul)
post.popul<-post.popul*exp(-ml.max+max.popul)
p.gen.post<-post.popul/sum(post.popul)
hfinal<-hash()
for(ii in 1:M)
{
resa[,ii*3]<-p.gen.post[ii]*as.numeric(resa[,ii*3-1])
resa[length(resa[,ii*3]),ii*3]<-p.gen.post[ii]
if(p.gen.post[ii]>0)
{
for(jj in 1:(length(resa[,ii*3])-1))
{
if(resa[jj,ii*3]>0)
{
#print(paste0(ii," and ",jj))
if(as.integer(has.key(hash = hfinal,key =resa[jj,ii*3-2]))==0)
hfinal[[resa[jj,ii*3-2]]]<-as.numeric(resa[jj,ii*3])
else
hfinal[[resa[jj,ii*3-2]]]<-hfinal[[resa[jj,ii*3-2]]]+as.numeric(resa[jj,ii*3])
}
}
}
}
posteriors<-values(hfinal)
clear(hfinal)
rm(hfinal)
rm(resa)
rm(post.popul)
rm(max.popul)
posteriors<-as.data.frame(posteriors)
posteriors<-data.frame(X=row.names(posteriors),x=posteriors$posteriors)
posteriors$X<-as.character(posteriors$X)
tryCatch({
res1<-simplifyposteriors(X = X1,posteriors = posteriors, th,thf)
write.csv(x =res1,row.names = F,file = paste0("postLog3etaG_",j,".csv"))},error = function(err){
print("error")
write.csv(x =posteriors,row.names = F,file = paste0("posteriorsLog3etaG_",j,".csv"))},finally = {
print(paste0("end simulation ",j))
})
rm(X1)
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))
})
}
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