From 3dae249990c6647eb4141b2f87f05fa43f6dbd3b Mon Sep 17 00:00:00 2001
From: be-marc <marcbecker@posteo.de>
Date: Mon, 25 Nov 2024 09:38:32 +0100
Subject: [PATCH] pass named list to callback parameter

---
 R/ensemble_fselect.R                   | 11 ++++++-----
 man/ensemble_fselect.Rd                |  4 ++--
 tests/testthat/test_ensemble_fselect.R |  6 +++---
 3 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/R/ensemble_fselect.R b/R/ensemble_fselect.R
index 4f79824e..097078ed 100644
--- a/R/ensemble_fselect.R
+++ b/R/ensemble_fselect.R
@@ -40,9 +40,9 @@
 #'  Whether to store the benchmark result in [EnsembleFSResult] or not.
 #' @param store_models (`logical(1)`)\cr
 #'  Whether to store models in [auto_fselector] or not.
-#' @param callbacks (list of lists of [CallbackBatchFSelect])\cr
+#' @param callbacks (Named list of lists of [CallbackBatchFSelect])\cr
 #'  Callbacks to be used for each learner.
-#'  The lists must have the same length as the number of learners.
+#'  The lists must be named by the learner ids.
 #'
 #' @template param_fselector
 #' @template param_task
@@ -87,12 +87,13 @@ ensemble_fselect = function(
   assert_resampling(inner_resampling)
   assert_measure(inner_measure, task = task)
   assert_measure(measure, task = task)
-  assert_list(callbacks, types = "list", len = length(learners), null.ok = TRUE)
+  callbacks = map(callbacks, function(callbacks) assert_callbacks(as_callbacks(callbacks)))
+  if (length(callbacks)) assert_names(names(callbacks), subset.of = map_chr(learners, "id"))
   assert_flag(store_benchmark_result)
   assert_flag(store_models)
 
   # create auto_fselector for each learner
-  afss = imap(unname(learners), function(learner, i) {
+  afss = map(learners, function(learner) {
     auto_fselector(
       fselector = fselector,
       learner = learner,
@@ -100,7 +101,7 @@ ensemble_fselect = function(
       measure = inner_measure,
       terminator = terminator,
       store_models = store_models,
-      callbacks = callbacks[[i]]
+      callbacks = callbacks[[learner$id]]
     )
   })
 
diff --git a/man/ensemble_fselect.Rd b/man/ensemble_fselect.Rd
index 1e1d01c9..280c2695 100644
--- a/man/ensemble_fselect.Rd
+++ b/man/ensemble_fselect.Rd
@@ -63,9 +63,9 @@ Measure used to score each trained learner on the test sets generated by \code{i
 \item{terminator}{(\link[bbotk:Terminator]{bbotk::Terminator})\cr
 Stop criterion of the feature selection.}
 
-\item{callbacks}{(list of lists of \link{CallbackBatchFSelect})\cr
+\item{callbacks}{(Named list of lists of \link{CallbackBatchFSelect})\cr
 Callbacks to be used for each learner.
-The lists must have the same length as the number of learners.}
+The lists must be named by the learner ids.}
 
 \item{store_benchmark_result}{(\code{logical(1)})\cr
 Whether to store the benchmark result in \link{EnsembleFSResult} or not.}
diff --git a/tests/testthat/test_ensemble_fselect.R b/tests/testthat/test_ensemble_fselect.R
index 0759d4ff..b64bb709 100644
--- a/tests/testthat/test_ensemble_fselect.R
+++ b/tests/testthat/test_ensemble_fselect.R
@@ -197,7 +197,7 @@ test_that("EnsembleFSResult initialization", {
 test_that("different callbacks can be set", {
   callback_test = callback_batch_fselect("mlr3fselect.test",
     on_eval_before_archive = function(callback, context) {
-      context$aggregated_performance[, callback_active := context$instance$objective$learner$id == "classif.rpart.fselector"]
+      context$aggregated_performance[, callback_active := context$instance$objective$learner$id == "classif.rpart"]
     }
   )
 
@@ -210,9 +210,9 @@ test_that("different callbacks can be set", {
     inner_measure = msr("classif.ce"),
     measure = msr("classif.acc"),
     terminator = trm("none"),
-    callbacks = list(list(callback_test), list())
+    callbacks = list("classif.rpart" = callback_test)
   )
 
   expect_true(all(efsr$benchmark_result$score()$learner[[1]]$fselect_instance$archive$data$callback_active))
-  expect_null(efsr$benchmark_result$score()$learner[[2]]$fselect_instance$archive$data$callback_active)
+  expect_null(efsr$benchmark_result$score()$learner[[3]]$fselect_instance$archive$data$callback_active)
 })