Merge pull request #8 from pythonhealthdatascience/parallel

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

Author: amyheather

DESCRIPTION

@@ -24,7 +24,9 @@ Imports:
     rlang,
     tidyr,
     R6,
-    tidyselect
+    tidyselect,
+    future,
+    future.apply
 Suggests:
     testthat (>= 3.0.0),
     lintr,

NAMESPACE

@@ -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)

R/defaults.R

@@ -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
 

R/model.R

@@ -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
@@ -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
@@ -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") %>%

R/trial.R

@@ -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
@@ -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(

man/model.Rd

@@ -1,101 +1,101 @@
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