/
helpfunctions_JAGS.R
286 lines (227 loc) · 7.88 KB
/
helpfunctions_JAGS.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
get_rng <- function(seed, n_chains) {
# get starting values for the random number generator
# - seed: an optional seed value
# - n_chains: the number of MCMC chains for which starting values need to be
# generated
oldseed <- .Random.seed
on.exit({
.Random.seed <<- oldseed
})
if (!is.null(seed)) {
set_seed(seed)
}
seeds <- sample.int(1.0e5L, size = n_chains)
# available random number generators
rng <- c("base::Mersenne-Twister",
"base::Super-Duper",
"base::Wichmann-Hill",
"base::Marsaglia-Multicarry")
rngs <- sample(rng, size = n_chains, replace = TRUE)
lapply(seq_along(rngs), function(k) {
list(.RNG.name = rngs[k],
.RNG.seed = seeds[k]
)
})
}
# functions for parallel computation -------------------------------------------
run_jags <- function(inits, data_list, modelfile, n_chains, n_adapt, n_iter,
var_names, thin, quiet, warn, mess, progress_bar,
add_samples = FALSE, adapt = NULL) {
t0 <- Sys.time()
if (isTRUE(add_samples)) {
sink(tempfile())
adapt$recompile()
sink()
} else {
adapt <- if (isFALSE(warn)) {
suppressWarnings({
try(rjags::jags.model(file = modelfile, data = data_list,
inits = inits, quiet = quiet,
n.chains = n_chains, n.adapt = n_adapt))
})
} else {
try(rjags::jags.model(file = modelfile, data = data_list,
inits = inits, quiet = quiet,
n.chains = n_chains, n.adapt = n_adapt))
}
}
t1 <- Sys.time()
mcmc <- if (n_iter > 0L & !inherits(adapt, "try-error")) {
if (isFALSE(mess)) {
sink(tempfile())
on.exit(sink())
force(suppressMessages(
try(rjags::coda.samples(adapt, n.iter = n_iter, thin = thin,
variable.names = var_names,
progress.bar = progress_bar))
))
} else {
try(rjags::coda.samples(adapt, n.iter = n_iter, thin = thin,
variable.names = var_names,
progress.bar = progress_bar))
}
}
t2 <- Sys.time()
list(adapt = adapt, mcmc = mcmc,
time_adapt = t1 - t0,
time_sample = t2 - t1)
}
#
# run_samples <- function(adapt, n_iter, var_names, thin) {
# sink(tempfile())
# adapt$recompile()
# sink()
#
# mcmc <- rjags::coda.samples(adapt,
# n.iter = n_iter,
# variable.names = var_names,
# progress.bar = "none", thin = thin
# )
#
# list(adapt = adapt, mcmc = mcmc)
# }
run_parallel <- function(n_adapt, n_iter, n_chains, inits, thin = 1L,
data_list, var_names, modelfile, progress_bar,
quiet = TRUE, mess = TRUE, warn = TRUE,
add_samples = FALSE, models = NULL, ...) {
if (any(n_adapt > 0L, n_iter > 0L)) {
f <- future::future({})
parallel <- f$asynchronous
fit <- if (isTRUE(parallel) |
(isTRUE(add_samples) & inherits(models, "list"))) {
if (isTRUE(mess) & isTRUE(parallel))
msg("Parallel sampling with %s workers started (%s).",
length(f$workers), Sys.time())
if (isTRUE(mess) & !isTRUE(parallel))
msg("Note: the original model was run in parallel.")
if (isTRUE(parallel) & isTRUE(add_samples) & inherits(models, "jags"))
errormsg("It is not possible to run %s in parallel when the input
%s object was run squentially.", dQuote("add_samples()"),
dQuote("JointAI"))
out <- lapply(seq_len(n_chains), function(i) {
future::future({
run_jags(inits = inits[[i]], data_list = data_list,
modelfile = modelfile,
n_chains = 1L,
n_adapt = n_adapt, n_iter = n_iter,
thin = thin,
var_names = var_names, quiet = quiet, warn = warn,
mess = mess, progress_bar = progress_bar,
add_samples = add_samples, adapt = models[[i]])
})
})
res <- lapply(out, future::value)
mcmc <- try(coda::as.mcmc.list(lapply(res, function(x) x$mcmc[[1L]])))
time_adapt <- do.call(c, lapply(res, "[[", "time_adapt"))
time_sample <- do.call(c, lapply(res, "[[", "time_sample"))
list(adapt = lapply(res, "[[", "adapt"),
mcmc = mcmc,
time_adapt = difftime_df(reformat_difftime(time_adapt)),
time_sample = difftime_df(reformat_difftime(time_sample))
)
} else {
run_jags(inits = inits, data_list = data_list,
modelfile = modelfile,
n_chains = n_chains,
n_adapt = n_adapt, n_iter = n_iter,
thin = thin,
var_names = var_names, quiet = quiet, warn = warn,
mess = mess, progress_bar = progress_bar,
add_samples = add_samples, adapt = models)
}
fit$parallel <- parallel
fit$workers <- length(f$workers)
if (!isTRUE(parallel)) {
fit$time_adapt <- difftime_df(fit$time_adapt)
fit$time_sample <- difftime_df(fit$time_sample)
}
fit
}
}
#' Set all elements of a `difftime` object to the same, largest meaningful unit
#' @param dt a `difftime` object (potentially a vector of `difftime`s)
#' @keywords internal
reformat_difftime <- function(dt) {
units(dt) <- "secs"
w <- which(min(dt)/c(secs = 1, mins = 60, hours = 3600, days = 86400) > 1L)
if (any(w))
units(dt) <- names(w)[length(w)]
dt
}
#' Converts a `difftime` object to a `data.frame`
#' @param dt `difftime` object (vector of `difftime` objects)
#' @keywords internal
difftime_df <- function(dt) {
if (length(dt) > 1L) {
dt <- setNames(dt, paste0("chain", seq_along(dt)))
} else {
dt <- setNames(dt, "total")
}
as.data.frame(as.list(dt))
}
rbind_duration <- function(dur, dur_new) {
Map(function(dur_old, dur_new) {
rownames(dur_new) <- paste0("run ", nrow(dur_old) + 1)
rbind(dur_old, dur_new)
}, dur_old = dur, dur_new = dur_new)
}
#' Create a duration object
#'
#' Add row names to the object
#'
#' @param dur list of `difftime` objects
#' @keywords internal
duration_obj <- function(dur) {
lapply(dur, function(x) {
rownames(x) <- paste0("run ", 1:nrow(x))
x
})
}
#' Calculate the sum of the computational duration of a JointAI object
#'
#' @param object object of class `JointAI`
#' @param by optional grouping information; options are `NULL` (default) to
#' calculate the sum over all chains and runs and both the adaptive
#' and sampling phase, `"run"` to get the duration per run,
#' `"phase"` to get the sum over all chains and runs per phase,
#' `"chain"` to get the sum per chain over both phases and all runs,
#' `"phase and run"` to get the sum over all chains, separately per
#' phase and run.
#'
#' @export
#'
sum_duration <- function(object, by = NULL) {
obj <- object$comp_info$duration
if (is.null(by) || by %in% c("phase", "phase and run", "run and phase")) {
s <- lapply(obj, function(p) {
r <- Map(function(vec, parallel) {
if (parallel) {
max(do.call(c, vec))
} else {
sum(do.call(c, vec))
}
}, vec = split(p, rownames(p)), parallel = object$comp_info$parallel)
do.call(c, r)
})
if (is.null(by)) {
sum(do.call(c, s))
} else if (by %in% c("phase and run", "run and phase")) {
s
} else if (by == "phase") {
lapply(s, sum)
}
} else if (by == "run") {
r <- do.call(cbind, obj)
s <- Map(function(vec, parallel) {
if (parallel) {
max(do.call(c, vec))
} else {
sum(do.call(c, vec))
}
}, vec = split(r, rownames(r)),
parallel = object$comp_info$parallel)
do.call(c, s)
} else if (by == "chain") {
do.call(c, lapply(c(do.call(rbind, obj)), sum))
}
}