feat: add renv_project option

.Rbuildignore

@@ -1,3 +1,5 @@
+^renv$
+^renv\.lock$
 ^LICENSE$
 ^.ignore$
 ^.editorconfig$

DESCRIPTION

@@ -27,6 +27,7 @@ Imports:
     mlr3misc,
     uuid
 Suggests:
+    renv,
     rpart,
     testthat
 Encoding: UTF-8

NEWS.md

@@ -4,8 +4,9 @@
 * feat: `reduceResultsBatchmark` gains argument `fun` which is passed on to `batchtools::reduceResultsList`.
 Useful for deleting model data to avoid running out of memory.
 Thanks to Toby Dylan Hocking @tdhock for the PR (https://github.com/mlr-org/mlr3batchmark/issues/18).
-* docs: A warning is now given when the loaded mlr3 version differs from the mlr3 version stored in the trained learners
-* feat: support marshaling
+* docs: A warning is now given when the loaded mlr3 version differs from the mlr3 version stored in the trained learners.
+* feat: Support marshaling.
+* feat: A `renv` project can be passed to `batchmark()` that is loaded in the job environment.
 
 # mlr3batchmark 0.1.1
 

R/batchmark.R

@@ -14,6 +14,9 @@
 #'
 #' @inheritParams mlr3::benchmark
 #' @param reg [batchtools::ExperimentRegistry].
+#' @param renv_project `character(1)`\cr
+#' Path to a renv project.
+#' If not `NULL`, the renv project is activated in the job environment.
 #'
 #' @return [data.table()] with ids of created jobs (invisibly).
 #' @export
@@ -33,12 +36,15 @@
 #' batchtools::submitJobs(reg = reg)
 #'
 #' reduceResultsBatchmark(reg = reg)
-batchmark = function(design, store_models = FALSE, reg = batchtools::getDefaultRegistry()) {
+batchmark = function(design, store_models = FALSE, reg = batchtools::getDefaultRegistry(), renv_project = NULL) {
   design = as.data.table(assert_data_frame(design, min.rows = 1L))
   assert_names(names(design), must.include = c("task", "learner", "resampling"))
   assert_flag(store_models)
-  batchtools::assertRegistry(reg, class = "ExperimentRegistry", writeable = TRUE, sync = TRUE,
-    running.ok = FALSE)
+  batchtools::assertRegistry(reg, class = "ExperimentRegistry", writeable = TRUE, sync = TRUE, running.ok = FALSE)
+  if (!is.null(renv_project)) {
+    require_namespaces("renv")
+    assert_directory_exists(renv_project)
+  }
 
   assert_list(design$task, "Task")
   assert_list(design$learner, "Learner")
@@ -114,7 +120,8 @@ batchmark = function(design, store_models = FALSE, reg = batchtools::getDefaultR
       learner_hash = learner_hashes,
       learner_id = map_chr(tab$learner, "id"),
       param_values_hash = param_values_hashes,
-      store_models = store_models
+      store_models = store_models,
+      renv_project = renv_project
     )
 
     ids[[g]] = batchtools::addExperiments(

R/worker.R

@@ -1,4 +1,7 @@
-run_learner = function(job, data, learner_hash, param_values_hash, store_models, ...) {
+run_learner = function(job, data, learner_hash, param_values_hash, store_models, renv_project = NULL, ...) {
+  if (!is.null(renv_project)) {
+    renv::load(renv_project)
+  }
   workhorse = utils::getFromNamespace("workhorse", ns = asNamespace("mlr3"))
   resampling = get(job$prob.pars$resampling_hash, envir = .GlobalEnv)
   learner = get(learner_hash, envir = .GlobalEnv)

tests/testthat/test_batchmark.R

@@ -129,3 +129,19 @@ test_that("adding parameter values works", {
     c(0.5, 1)
   )
 })
+
+# test_that("renv_project works", {
+#   tasks = list(mlr3::tsk("iris"), mlr3::tsk("sonar"))
+#   learners = list(mlr3::lrn("classif.featureless"), mlr3::lrn("classif.rpart"))
+#   resamplings = list(mlr3::rsmp("cv", folds = 3), mlr3::rsmp("holdout"))
+
+#   design = mlr3::benchmark_grid(
+#     tasks = tasks,
+#     learners = learners,
+#     resamplings = resamplings
+#   )
+
+#   reg = batchtools::makeExperimentRegistry(NA, make.default = FALSE)
+#   batchmark(design, reg = reg, renv_project = ".")
+#   batchtools::submitJobs(reg = reg)
+# })