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bach_driver_drp18.r
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bach_driver_drp18.r
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# translate runDREAMZS_hmixbatch.m into R
# Simon Woodward, DairyNZ 2017-2018
# now allows keeping previous results or starting from scratch
# save runlist including diagnostics
# bach_driver11.r + bach_script11.stan:
# - apply priors to parameters (requires intial values to chains)
# - unique name for log file
# - increase weighting of llflow
# - reorganise parameter trace plots
# remove all variables - try to avoid memory error after run
rm(list=ls())
# dev.off() # close figures if they exist
# kill_all_Rscript_s() # WARNING --- kills processes in other sessions too!!!
# load libraries
suppressMessages({
library(rstan)
library(tidyverse)
library(cowplot)
library(parallel)
library(installr)
library(truncnorm)
library(ggthemes)
})
# set run directory
out_path <- 'run - whatawhata - DRP NNN/'
print(paste0("Fitting data in /", out_path))
# declare functions
"%notin%" <- function(x,y)!("%in%"(x,y))
# open log_file
log_file_name <- paste(out_path, 'log', format(Sys.time(), "_%Y%m%d_%H%M"), '.txt', sep='')
log_file <- file(log_file_name, open='w') # delete duplicate log file if necessary
close(log_file)
log_file <- file(log_file_name, open='a') # open file for logging
# read data files
source('read_data10.r')
cat(paste(nrow(runlist),'lines in runlist.dat'), file=log_file, sep='\n')
chem1i <- unique(runlist$chem1i)
chem2i <- unique(runlist$chem2i)
# TPmax <- max(pretty(concdata$tp/1000 * 2))
# TNmax <- max(pretty(concdata$tn/1000 * 2))
TPmax <- max(pretty(concdata[[chem1i]]/1000 * 2))
TNmax <- max(pretty(concdata[[chem2i]]/1000 * 2))
# model and parameters
stan_model <- paste(out_path, 'bach_script16.stan', sep='')
stan_pars_raw <- c('medb0raw', 'medd1raw', 'slowb0raw', 'slowd1raw',
'chem1fastraw0', 'chem1medraw0', 'chem1slowraw0',
'chem2fastraw0', 'chem2medraw0', 'chem2slowraw0',
'chem1fastraw1', 'chem1medraw1', 'chem1slowraw1',
'chem2fastraw1', 'chem2medraw1', 'chem2slowraw1',
'chem1fastrawb1', 'chem1medrawb1', 'chem1slowrawb1',
'chem2fastrawb1', 'chem2medrawb1', 'chem2slowrawb1',
'chem1fastrawb2', 'chem1medrawb2', 'chem1slowrawb2',
'chem2fastrawb2', 'chem2medrawb2', 'chem2slowrawb2')
stan_pars_scale <- c(1, ((-0.1*log(1-0.99)) - (-0.1*log(1-0.1))) * 10, # NOTE -ln(1-a) needs special scaling
1, ((-0.1*log(1-0.9999)) - (-0.1*log(1-0.99))) * 10, # NOTE -ln(1-a) needs special scaling
TPmax, TPmax, TPmax, TNmax, TNmax, TNmax,
TPmax, TPmax, TPmax, TNmax, TNmax, TNmax,
TPmax, TPmax, TPmax, TNmax, TNmax, TNmax,
TPmax, TPmax, TPmax, TNmax, TNmax, TNmax)
stan_pars_label <- c('b0,m/(1-a1,m)', '-ln(1-a1,m)', 'b0,s/(1-a1,s)', '-ln(1-a1,s)',
'e0,fTP', 'e0,mTP', 'e0,sTP',
'e0,fTN', 'e0,mTN', 'e0,sTN',
'e1,fTP', 'e1,mTP', 'e1,sTP',
'e1,fTN', 'e1,mTN', 'e1,sTN',
'f1,fTP', 'f1,mTP', 'f1,sTP',
'f1,fTN', 'f1,mTN', 'f1,sTN',
'f2,fTP', 'f2,mTP', 'f2,sTP',
'f2,fTN', 'f2,mTN', 'f2,sTN')
stan_pars <- c('medb0', 'meda1', 'slowb0', 'slowa1',
'medBFImax', 'medrec', 'slowBFImax', 'slowrec',
'chem1fast0', 'chem1med0', 'chem1slow0',
'chem2fast0', 'chem2med0', 'chem2slow0',
'chem1fast1', 'chem1med1', 'chem1slow1',
'chem2fast1', 'chem2med1', 'chem2slow1',
'chem1fastb1', 'chem1medb1', 'chem1slowb1',
'chem2fastb1', 'chem2medb1', 'chem2slowb1',
'chem1fastb2', 'chem1medb2', 'chem1slowb2',
'chem2fastb2', 'chem2medb2', 'chem2slowb2',
'llchem1', 'llchem2')
# priors
priorwide <- 0.3
priornarrow <- 0.1
priortab <- tibble(
parname = stan_pars_raw,
parscale = stan_pars_scale,
parlabel = stan_pars_label,
parmin = c(rep(0, 4), rep(0, 12), rep(-1, 12)),
parmax = 1,
parmean = c(1.0, 0.4, 1.0, 0.7,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0),
parsd = c(priornarrow, priorwide, priornarrow, priorwide,
rep(priorwide, 6), rep(priorwide, 6),
rep(priornarrow, 6), rep(priornarrow, 6))
)
# calculate values for prior table in paper
if (FALSE){
medd1raw <- c(0, 0.4, 1) %>% print # min, mean, max of medd1raw
medd1scale = (-0.1*log(1-0.1)) + ((-0.1*log(1-0.99)) - (-0.1*log(1-0.1))) * medd1raw # = 0.1*ln(1-meda1)
abs(medd1scale)*10 # min, mean, max of ln(1- meda1)
((-0.1*log(1-0.99)) - (-0.1*log(1-0.1))) * priorwide * 10 # sd of ln(1- meda1)
meda1 = (1-exp(-abs(medd1scale)*10)) %>% print # min, mean, max of meda1
medb0raw <- c(0, 1, 1) %>% print # min, mean, max of medb1raw = b0,m/(1-a1.m)
ratio = (1/rev(1-meda1)) %>% print
medb0 = ifelse(ratio>1 , medb0raw/ratio , medb0raw) %>% print # min, mean, max of medb0
medBFImax = (medb0/rev(1-meda1)) %>% print
medrec = ifelse(medb0<1 , meda1/(1-medb0) , 0) %>% print # FIXME wrong formula?
slowd1raw <- c(0, 0.7, 1) %>% print # min, mean, max, sd
slowd1scale = (-0.1*log(1-0.99)) + ((-0.1*log(1-0.9999)) - (-0.1*log(1-0.99))) * slowd1raw # = 0.1*ln(1-slowa1)
abs(slowd1scale)*10 # min, mean, max of ln(1- slowa1)
((-0.1*log(1-0.9999)) - (-0.1*log(1-0.99))) * priorwide * 10 # sd of ln(1- slowa1)
slowa1 = (1-exp(-abs(slowd1scale)*10)) %>% print # min, mean, max of slowa1
slowb0raw <- c(0, 1, 1) %>% print # min, mean, max of slowb1raw = b0,s/(1-a1.s)
ratio = (1/rev(1-slowa1)) %>% print
slowb0 = ifelse(ratio>1 , slowb0raw/ratio , slowb0raw) %>% print # min, mean, max of slowb0
slowBFImax = (slowb0/rev(1-slowa1)) %>% print
slowrec = ifelse(slowb0<1 , slowa1/(1-slowb0) , 0) %>% print # FIXME wrong formula?
}
# record version information
cat(R.version.string, file=log_file, sep='\n')
cat(paste('Stan version', stan_version()), file=log_file, sep='\n')
# init rstan
rstan_options(auto_write = TRUE)
# options(mc.cores = detectCores() - 1)
if (FALSE){
# https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
dotR <- file.path(Sys.getenv("HOME"), ".R")
if (!file.exists(dotR)) dir.create(dotR)
M <- file.path(dotR, ifelse(.Platform$OS.type == "windows", "Makevars.win", "Makevars"))
if (!file.exists(M)) file.create(M)
cat(if( grepl("^darwin", R.version$os)) "\nCXX14FLAGS=-O3 -march=native -mtune=native -arch x86_64 -ftemplate-depth-256" else
if (.Platform$OS.type == "windows") "\nCXX14FLAGS=-O3 -mtune=native -mmmx -msse -msse2 -msse3 -mssse3 -msse4.1 -msse4.2" else
"CXX14FLAGS = -fPIC",
file = M, sep = "\n", append = TRUE)
# Sys.setenv(LOCAL_CPPFLAGS = '-march=native -mtune=native')
}
# prepare output files
nruns <- nrow(runlist)
from_scratch <- all(runlist$control %notin% "keep")
source('prep_output10.r')
# loop through runlist
rows <- 1:nruns # to run all sites
# rows <- c(12) # to run a subset of sites, useful for testing
i <- rows[[1]] # set i useful for line by line testing
for (i in rows) {
# seed
set.seed(i)
# write header
data_file_name <- runlist$catchfile[i]
opt_file_name <- runlist$datafile[i]
header <- paste(i, data_file_name, opt_file_name, format(Sys.time(), "%a %d %b %H:%M:%S %Y"))
print(header)
cat(header, file=log_file, sep='\n')
# get mode
mode <- runlist$control[i]
if (mode=='zero') {
# report
cat(paste('max(Rhat) = zero'), file=log_file, sep='\n')
cat(paste('elapsed(h) = 0'), file=log_file, sep='\n')
# insert into output files
irows <- (i*7-6):(i*7)
bestxs[i, ] <- 0
quartxs[irows, ] <- 0
sbestxs[i, ] <- 0
squartxs[irows, ] <- 0
} else if (mode=='calib') {
# data
arun <- runlist[i, ]
temp1 <- flowdata %>%
filter(catch == data_file_name) %>%
select(date, flow)
temp2 <- concdata %>%
filter(catch == data_file_name) %>%
mutate(date = as.Date(date)) %>%
select(date, all_of(c(arun$chem1i, arun$chem2i)))
adata <- left_join(temp1, temp2, by = "date")
# assemble run options/data into vectors (?)
nyears <- arun$nyears
date1 <- arun$date1
catchname <- arun$catchname
setname <- arun$setname
ii <- arun$startrun:arun$endcalib
jj <- arun$startcalib:arun$endcalib - arun$startrun + 1
date <- as.vector(adata$date[ii])
ndate <- length(date)
flow <- as.vector(adata$flow[ii] / 1000)
TP <- as.vector(adata[[arun$chem1i]][ii] / 1000)
TN <- as.vector(adata[[arun$chem2i]][ii] / 1000)
meanTPcalib <- mean(TP[jj], na.rm=TRUE)
meanTNcalib <- mean(TN[jj], na.rm=TRUE)
TP[is.na(TP)] <- -1 # stan doesn't understand NA
TN[is.na(TN)] <- -1 # stan doesn't understand NA
keeps <- c('startrun', 'startcalib', 'endcalib', 'startvalid', 'endvalid')
# intoptions <- as.vector(t(arun[keeps]))
intoptions <- c(1, arun$startcalib-arun$startrun+1, rep(arun$endcalib-arun$startrun+1, 3)) # ignore valid
nintoptions <- length(intoptions)
keeps <- c('chem1ae', 'chem1re', 'chem2ae', 'chem2re', 'area')
realoptions <- as.vector(t(arun[keeps]))
nrealoptions <- length(realoptions)
# fit the model
nits <- arun$nits
wits <- nits # warmup iterations (minimum recommended = 150)
sits <- 200 # sampling iterations FIXME enough? was 400 for bach
# priors
chain_init <- function(){
x <- rtruncnorm(1, priortab$parmin, priortab$parmax, priortab$parmean, priortab$parsd)
names(x) <- priortab$parname
as.list(x)
}
stan_init <- list(chain_init(), chain_init(), chain_init(), chain_init())
fit <- stan(file=stan_model,
data=c('nintoptions', 'intoptions', 'nrealoptions', 'realoptions',
'ndate', 'date', 'flow', 'TP', 'TN', 'meanTPcalib', 'meanTNcalib'),
# control=list(adapt_delta=0.90, stepsize=0.005, max_treedepth=15),
warmup=wits,
iter=wits+sits,
chains=4,
init=stan_init, # needed if priors
cores = 4, # disable parallel processing
# save_warmup=FALSE, # warmup iterations required for some diagnostics
# algorithm = "Fixed_param",
# verbose = TRUE,
seed=i)
# memory management if necessary
# save.image(file="temp.RData")
# rm(list=ls())
# load(file="temp.RData")
# extract samples
samples <- as.data.frame(rstan::extract(fit)) %>% # very large!
select(-starts_with('bach')) %>% # discard model traces. This is a bit slow.
mutate(setname=setname) # add identifier
write_rds(samples, paste(out_path, setname, '_samples.rds', sep=''))
# extract quartiles
results <- summary(fit)$summary # quartiles
keeps <- grep('bach*', rownames(results), invert=FALSE) # extract model traces 'bach[]'
traces <- as.data.frame(results[keeps,]) # don't use tibble because want row names
keeps <- grep('bach*', rownames(results), invert=TRUE) # discard model traces 'bach[]'
results <- as.data.frame(results[keeps,]) # don't use tibble because want row names
maxRhat <- max(results$Rhat, na.rm=TRUE) # should be < 1.2
warning <- ''
if (maxRhat >= 1.1) { # generate a warning and save fit
warning <- paste(' Warning: max(Rhat) =', maxRhat, '>= 1.l')
print(warning)
# http://discourse.mc-stan.org/t/saving-and-sharing-an-rstan-model-fit/1059
# fit@stanmodel@dso <- new("cxxdso") # have to kill the Dynamic Shared Object inside the stanmodel slot?
# write_rds(fit, paste(out_path, setname, '_fit.rds', sep='')) # big file!
}
runtime <- as_tibble(get_elapsed_time(fit)) %>% mutate(total=warmup+sample)
resultst <- cbind.data.frame(nms=names(results), t(results), stringsAsFactors=FALSE) # transpose, keeping names
# don't use mutate; it kills row names
resultst[, 1] <- 0 # replace first columns 'nms' with 0
resultst[, 'totalgens'] <- wits + sits
resultst[, 'gelmanr'] <- maxRhat
resultst[, 'elapsed'] <- max(runtime$total)/3600 # hours
resultst[, 'setname'] <- setname
write_rds(traces, paste(out_path, setname, '_traces.rds', sep=''))
write_rds(resultst, paste(out_path, setname, '_quartiles.rds', sep=''))
# report
cat(paste('iterations =', wits+sits), file=log_file, sep='\n')
cat(paste('max(Rhat) =', maxRhat, warning), file=log_file, sep='\n')
cat(paste('elapsed(h) =', max(runtime$total)/3600), file=log_file, sep='\n')
runrecord$nits[i] <- wits+sits
runrecord$gelmanr[i] <- maxRhat
runrecord$elapsed[i] <- max(runtime$total)/3600
# insert into output files
irows <- (i*7-6):(i*7)
keeps <- match(parcols, colnames(resultst), nomatch=1) # col numbers we want, use col 1 if missing
bestxs[i, ] <- resultst['50%', keeps]
quartxs[irows, ] <- resultst[c('2.5%','2.5%','25%','50%','75%','97.5%','97.5%'), keeps]
keeps <- match(statcols, colnames(resultst), nomatch=1) # col numbers we want, use col 1 if missing
sbestxs[i, ] <- resultst['50%', keeps]
squartxs[irows, ] <- resultst[c('2.5%','2.5%','25%','50%','75%','97.5%','97.5%'), keeps]
} else { # do nothing
# report
cat(paste('iterations = 0'), file=log_file, sep='\n')
cat(paste('max(Rhat) = unchanged'), file=log_file, sep='\n')
cat(paste('elapsed(h) = 0'), file=log_file, sep='\n')
} # fit the model
# write results so far
write_tsv(bestxs, file.path(out_path, 'bestxs.tsv'), col_names=FALSE, na='#N/A')
write_tsv(quartxs, file.path(out_path, 'quartxs.tsv'), col_names=FALSE, na='#N/A')
write_tsv(sbestxs, file.path(out_path, 'sbestxs.tsv'), col_names=FALSE, na='#N/A')
write_tsv(squartxs, file.path(out_path, 'squartxs.tsv'), col_names=FALSE, na='#N/A')
write_tsv(runrecord, file.path(out_path, 'runrecord.tsv'), col_names=TRUE)
# continue
i <- i + 1
} # end loop
#
cat(format(Sys.time(), "%a %d %b %H:%M:%S %Y"), file=log_file, sep='\n')
cat("\n")
close(log_file)
showConnections(all=FALSE)
#closeAllConnections()
print("Finished.")
# report results of last fit
if (exists("fit")){
print("Stan compile information:")
print(fit@stanmodel@dso) # print compilation information
source("bach_driver_last15.R")
}
# use this if necessary to kill processes that didn't terminate
# kill_all_Rscript_s()
# plot boxplots and traces
source('box_plots_drp16.r')
source('trace_plots_drp15.r')