/
du_model_rev5_mcmc.R
executable file
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du_model_rev5_mcmc.R
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#Purposes:
#1) Compare the non-centered parameterization of
print("initializing...")
source("util/get_my_preferred_cores.R")
options(mc.cores = get_my_preferred_cores())
#options(mc.cores = NULL)
#source files
source("nate_files/fitGroupsV3Onegroup.R")
source("data_summarize.R")
#set settings.
models_to_run<-c("double_update_rev5")
estimation_methods<-c(as.character(ESTIMATION_METHOD.MCMC),as.character(ESTIMATION_METHOD.VariationalBayes))
subject_groups<-1:3
times_to_run<-3
#run.
summaryfilepath<-paste0(localsettings$data.dir,"du_model_rev5.RData")
models.with.4.separate.runs.count<-0
models.with.runs.considered.together.count<-1
total.models.count<-length(estimation_methods)*
times_to_run*length(subject_groups)*
(models.with.4.separate.runs.count*4+models.with.runs.considered.together.count)
model.summaries <- vector("list", total.models.count)
model.stanfits <- vector("list", total.models.count)
if(file.exists(summaryfilepath)){
load(file=summaryfilepath)
}
#print("Starting main loop...")
if(any(sapply(model.summaries,is.null))){
for (em in estimation_methods){
if (em==as.character(ESTIMATION_METHOD.MCMC)){
iterations<-5000
warmup_iter=1000
}else if (em==as.character(ESTIMATION_METHOD.VariationalBayes)){
iterations<-10000
warmup_iter=NA
}else{
times_to_run=0
}
for (t in 1:times_to_run){
for (m in models_to_run){
for (g in subject_groups){
print (paste0("g:",g,";m:",m,";t:",t,collapse=", "))
variable_run_lengths=FALSE
#we will loop just once for the model that handles everything at once
#but four times for the model that needs to process runs and reward/punishment separately.
if(m %in% c("double_update_rpo_repeated_runs_notrialpost", "double_update_rpo_repeated_runs_ntp_otmod",
"double_update_nov_rev2","double_update_nov_rev2-a-a","double_update_nov_rev2-b",
"double_update_nov_rev2-c","double_update_nov_rev2-d",
"double_update_rev3b",
"double_update_rev4",
"double_update_rev5")){
runlist<-list(c(1,2))
rp_list<-list(c(1,2))
if(m %in% c("double_update_nov_rev2","double_update_nov_rev2-a-a","double_update_nov_rev2-b",
"double_update_nov_rev2-c","double_update_nov_rev2-d",
"double_update_rev3b",
"double_update_rev4",
"double_update_rev5")){
rl_unique_runids=TRUE
if(m %in% c("double_update_nov_rev2-a-a","double_update_rev3b", "double_update_rev4","double_update_rev5")){
variable_run_lengths=TRUE
}
}
}else if (m %in% c("double_update_notrialpost")){
#only run reward and punishment when we can
runlist<-list(c(1),c(2))
rp_list<-list(c(1),c(2))
rl_unique_runids=NA
}
#for the model that takes both simultaneously, this will pass all the parameters at once
#but for the model that needs them separately, this will run through four times.
for (rp in rp_list){
for(runs in runlist){
print(paste0("rp:",paste(rp),"; runs:",paste(runs)))
generatePosteriorTrialPredictions=NA
#run the model
fitpackage<-lookupOrRunFit(
run=runs,groups_to_fit=g, model_to_use=m,includeSubjGroup = FALSE,
rp=rp,
model_rp_separately=FALSE,model_runs_separately = TRUE, include_pain=FALSE,
fastDebug=FALSE,
fileSuffix=paste0("rev_20180101",as.character(t)),
estimation_method = em,
bseed=t+1187913262,#set.seed(as.numeric(Sys.time())); sample.int(.Machine$integer.max-1000, 1)
collateTrialData=FALSE,
chainNum = 12,
iterations = iterations,
warmup_iter = warmup_iter,
rl_unique_runids=TRUE,
variable_run_lengths=variable_run_lengths,
sample_from_prior=FALSE,
subj_level_params=FALSE,
include_run_ot=TRUE
)
cat("...model loaded. Extracting...")
#save just the output we want.
first_empty_list_pos<-min(which(sapply(model.summaries,is.null)))
print(paste("first_empty_list_pos is", as.character(first_empty_list_pos)))
#get the non-extracted fit, and the stanfit
first_empty_list_pos.stanfits<-min(which(sapply(model.stanfits,is.null)))
model.stanfits[[first_empty_list_pos]]<-fitpackage$fit
extractedfit<-rstan::extract(fitpackage$fit)
# first_empty_list_pos.extractedfit<-min(which(sapply(model.extractedfits,is.null)))
# model.extractedfits[[first_empty_list_pos]]<-extractedfit
if(m %in% c("double_update_rpo_repeated_runs", "double_update_rpo_repeated_runs_notrialpost")){
model.summaries[[first_empty_list_pos]]<-
list("summaryObj"=data_summarize_double_update_rpo_repeated_runs(extractedfit,comprehensive=FALSE),
"g"=g,"m"=m,"t"=t,"EstimationMethod"=em,"elapsedTime"=fitpackage$general_info$estimation_duration)
}else if(m=="double_update" || m=="double_update_notrialpost"){
model.summaries[[first_empty_list_pos]]<-
list("summaryObj"=data_summarize_double_update(extractedfit,comprehensive=FALSE,
outcome.type = rp,
run = runs),
"g"=g,"m"=m,"t"=t,"EstimationMethod"=em,"elapsedTime"=fitpackage$general_info$estimation_duration)
}else if(m %in% c("double_update_nov_rev2","double_update_nov_rev2-a-a","double_update_nov_rev2-c","double_update_nov_rev2-d")){
model.summaries[[first_empty_list_pos]]<-
list("summaryObj"=data_summarize_double_update_rev2_repeated_runs(extractedfit,comprehensive=FALSE,
outcome.type = rp,
run = NA),
"g"=g,"m"=m,"t"=t,"EstimationMethod"=em,"elapsedTime"=fitpackage$general_info$estimation_duration)
}else if(m %in% c("double_update_rev3a")){
model.summaries[[first_empty_list_pos]]<-
list("summaryObj"=data_summarize_double_update_rev3(extractedfit,comprehensive=FALSE,
outcome.type = rp),
"g"=g,"m"=m,"t"=t,"EstimationMethod"=em,"elapsedTime"=fitpackage$general_info$estimation_duration)
}else if(m %in% c("double_update_rev4")){
model.summaries[[first_empty_list_pos]]<-
list("summaryObj"=data_summarize_double_update_rev4(extractedfit,comprehensive=FALSE,
outcome.type = rp),
"g"=g,"m"=m,"t"=t,"EstimationMethod"=em,"elapsedTime"=fitpackage$general_info$estimation_duration)
}else if(m %in% c("double_update_rev5")){
model.summaries[[first_empty_list_pos]]<-
list("summaryObj"=data_summarize_double_update_rev5(extractedfit,comprehensive=FALSE,
outcome.type = rp),
"g"=g,"m"=m,"t"=t,"EstimationMethod"=em,"elapsedTime"=fitpackage$general_info$estimation_duration)
}else{
print(m)
stop("f^<%! I don't recognize that model")
}
#remove the fit object from memory, because it is pretty large!
rm(fitpackage)
print("...summary data extracted.")
# first_empty_list_pos<-min(which(sapply(model.summaries,is.null)))
}
}
}
}
}
}
save(model.summaries,model.stanfits,file=summaryfilepath)
}