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fix(parallel/seeds): switch to future.apply (which still works when knit), use future.seed, plus other little chores #8

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Merged
amyheather merged 4 commits into from
Jan 27, 2025
Merged

fix(parallel/seeds): switch to future.apply (which still works when knit), use future.seed, plus other little chores #8

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4f5514d
refactor(parallel): switched parallel to future.apply, which is consi…
amyheather Jan 27, 2025
8763607
fix(seeds): Use future.seed in trial, and set.seed() optional in mode…
amyheather Jan 27, 2025
23211d5
feat(seeds): re-run .rmd following change to seeds - and so, differen…
amyheather Jan 27, 2025
2323517
chore(lint/round): build docs, lint files, and round cores output so …
amyheather Jan 27, 2025
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4 changes: 3 additions & 1 deletion DESCRIPTION
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Expand Up @@ -24,7 +24,9 @@ Imports:
rlang,
tidyr,
R6,
tidyselect
tidyselect,
future,
future.apply
Suggests:
testthat (>= 3.0.0),
lintr,
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10 changes: 4 additions & 6 deletions NAMESPACE
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Expand Up @@ -13,13 +13,11 @@ importFrom(dplyr,lead)
importFrom(dplyr,mutate)
importFrom(dplyr,n_distinct)
importFrom(dplyr,summarise)
importFrom(future,multisession)
importFrom(future,plan)
importFrom(future,sequential)
importFrom(future.apply,future_lapply)
importFrom(magrittr,"%>%")
importFrom(parallel,clusterEvalQ)
importFrom(parallel,clusterExport)
importFrom(parallel,detectCores)
importFrom(parallel,makeCluster)
importFrom(parallel,parLapply)
importFrom(parallel,stopCluster)
importFrom(purrr,reduce)
importFrom(rlang,.data)
importFrom(simmer,add_generator)
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2 changes: 1 addition & 1 deletion R/defaults.R
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Expand Up @@ -93,7 +93,7 @@ Defaults <- R6Class( # nolint
#' the class without needing to run `Defaults[["new"]]()` every time.
#'
#' @return Instance of the Defaults class.
#'
#'
#' @export
#' @rdname defaults

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9 changes: 7 additions & 2 deletions R/model.R
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Expand Up @@ -2,6 +2,9 @@
#'
#' @param run_number Integer representing index of current simulation run.
#' @param param_class Instance of Defaults containing model parameters.
#' @param set_seed Whether to set seed within the model function (which we
#' may not wish to do if being set elsewhere - such as done in trial()).
#' Default is TRUE.
#'
#' @importFrom simmer trajectory seize timeout release simmer add_resource
#' @importFrom simmer add_generator run wrap get_mon_arrivals
Expand All @@ -12,7 +15,7 @@
#' @return Named list with two tables: monitored arrivals and resources
#' @export

model <- function(run_number, param_class) {
model <- function(run_number, param_class, set_seed = TRUE) {

# Extract parameter list from the parameter class
# It is important to do this within the model function (rather than
Expand All @@ -25,7 +28,9 @@ model <- function(run_number, param_class) {
valid_inputs(run_number, param)

# Set random seed based on run number
set.seed(run_number)
if (set_seed) {
set.seed(run_number)
}

# Define the patient trajectory
patient <- trajectory("appointment") %>%
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46 changes: 19 additions & 27 deletions R/trial.R
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@@ -1,13 +1,9 @@
#' Run simulation for multiple replications, sequentially or in parallel.
#'
#' Note: The parallel processing is implemented using `parLapply` because
#' `mcLapply`` does not work on Windows and `future_lapply`` would often get
#' stuck.
#'
#' @param param_class Instance of Defaults containing model parameters.
#'
#' @importFrom parallel detectCores makeCluster stopCluster clusterEvalQ
#' @importFrom parallel clusterExport parLapply
#' @importFrom future plan multisession sequential
#' @importFrom future.apply future_lapply
#'
#' @return Named list with two tables: monitored arrivals and resources.
#' @export
Expand All @@ -17,35 +13,31 @@ trial <- function(param_class) {
n_cores <- param_class[["get"]]()[["cores"]]
n_runs <- param_class[["get"]]()[["number_of_runs"]]

# Determine the parallel execution plan
if (n_cores == 1L) {

# Sequential execution
results <- lapply(
1L:n_runs,
function(i) simulation::model(run_number = i, param_class = param_class)
)

plan(sequential) # Sequential execution
} else {
# Parallel execution

# Create a cluster with specified number of cores
if (n_cores == -1L) {
cores <- detectCores() - 1L
cores <- future::availableCores() - 1L
} else {
cores <- n_cores
}
cl <- makeCluster(cores)

# Ensure the cluster is stopped upon exit
on.exit(stopCluster(cl))

# Run simulations in parallel
results <- parLapply(
cl, 1L:n_runs,
function(i) simulation::model(run_number = i, param_class = param_class)
)
plan(multisession, workers = cores) # Parallel execution
}

# Run simulations (sequentially or in parallel)
# Mark set_seed as FALSE as we handle this using future.seed(), rather than
# within the function, and we don't want to override future.seed
results <- future_lapply(
1L:n_runs,
function(i) {
simulation::model(run_number = i,
param_class = param_class,
set_seed = FALSE)
},
future.seed = 123456L
)

# Combine the results from multiple replications into just two dataframes
if (n_runs > 1L) {
all_arrivals <- do.call(
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6 changes: 5 additions & 1 deletion man/model.Rd
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4 changes: 1 addition & 3 deletions man/trial.Rd
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200 changes: 100 additions & 100 deletions outputs/base_trial.csv
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Original file line number Diff line number Diff line change
@@ -1,101 +1,101 @@
"","replication","arrivals","mean_waiting_time_nurse","mean_activity_time_nurse","utilisation_nurse"
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"90",90,21,0.019621619152381,9.41218877944077,0.572715294625353
"91",91,24,0,7.43438548758834,0.552238861244287
"92",92,16,2.12286939339375,12.4974164222768,0.655423578887326
"93",93,23,0.56134304333913,10.612379013797,0.783901564319108
"94",94,13,0,4.90019655529476,0.186899046605271
"95",95,14,0,8.44998224756754,0.316635268100141
"96",96,23,0.30675455233913,7.45276488235241,0.506751654132952
"97",97,17,0.144732497258824,9.26537058668207,0.426026115330518
"98",98,20,0.238843066105,9.94408414189002,0.538063695810092
"99",99,17,0.112457353976471,9.82925194721256,0.550634397521811
"100",100,16,0.2714164474625,7.80477320313942,0.422094474520408
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10 changes: 5 additions & 5 deletions outputs/scenario_nurse_util.tex
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Original file line number Diff line number Diff line change
Expand Up @@ -4,11 +4,11 @@
\hline
& Patient inter-arrival time & 5 nurses & 6 nurses & 7 nurses & 8 nurses \\
\hline
1 & 3 & 0.58 (0.55, 0.61) & 0.49 (0.46, 0.51) & 0.42 (0.40, 0.44) & 0.37 (0.35, 0.39) \\
2 & 4 & 0.45 (0.42, 0.48) & 0.38 (0.35, 0.40) & 0.32 (0.30, 0.34) & 0.28 (0.27, 0.30) \\
3 & 5 & 0.38 (0.35, 0.40) & 0.31 (0.29, 0.33) & 0.27 (0.25, 0.28) & 0.23 (0.22, 0.25) \\
4 & 6 & 0.31 (0.29, 0.34) & 0.26 (0.24, 0.28) & 0.22 (0.21, 0.24) & 0.20 (0.18, 0.21) \\
5 & 7 & 0.28 (0.26, 0.30) & 0.23 (0.22, 0.25) & 0.20 (0.18, 0.21) & 0.17 (0.16, 0.19) \\
1 & 3 & 0.60 (0.57, 0.63) & 0.51 (0.49, 0.54) & 0.44 (0.42, 0.46) & 0.39 (0.36, 0.41) \\
2 & 4 & 0.47 (0.44, 0.50) & 0.39 (0.37, 0.42) & 0.34 (0.32, 0.36) & 0.30 (0.28, 0.31) \\
3 & 5 & 0.38 (0.36, 0.41) & 0.32 (0.30, 0.34) & 0.28 (0.26, 0.30) & 0.24 (0.22, 0.26) \\
4 & 6 & 0.32 (0.29, 0.35) & 0.27 (0.24, 0.29) & 0.23 (0.21, 0.25) & 0.20 (0.18, 0.22) \\
5 & 7 & 0.28 (0.26, 0.31) & 0.24 (0.21, 0.26) & 0.20 (0.18, 0.22) & 0.18 (0.16, 0.19) \\
\hline
\end{tabular}
\end{table}
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