From 26376c3f63f06253349c95437f0be07d57317c98 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 13 Nov 2024 16:50:04 +0100
Subject: [PATCH 1/7] chore(deps): bump JamesIves/github-pages-deploy-action
 (#1205)

Bumps [JamesIves/github-pages-deploy-action](https://github.com/jamesives/github-pages-deploy-action) from 4.6.8 to 4.6.9.
- [Release notes](https://github.com/jamesives/github-pages-deploy-action/releases)
- [Commits](https://github.com/jamesives/github-pages-deploy-action/compare/v4.6.8...v4.6.9)

---
updated-dependencies:
- dependency-name: JamesIves/github-pages-deploy-action
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/pkgdown.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/pkgdown.yml b/.github/workflows/pkgdown.yml
index 7aec5da57..0e935d7f2 100644
--- a/.github/workflows/pkgdown.yml
+++ b/.github/workflows/pkgdown.yml
@@ -44,7 +44,7 @@ jobs:
 
       - name: Deploy
         if: github.event_name != 'pull_request'
-        uses: JamesIves/github-pages-deploy-action@v4.6.8
+        uses: JamesIves/github-pages-deploy-action@v4.6.9
         with:
           clean: false
           branch: gh-pages

From 7c3c74b614c4c943f61e2e03992ee26b1e177605 Mon Sep 17 00:00:00 2001
From: Marc Becker <33069354+be-marc@users.noreply.github.com>
Date: Wed, 13 Nov 2024 16:59:36 +0100
Subject: [PATCH 2/7] BREAKING CHANGE: remove $loglik method from all learners
 (#1207)

---
 NEWS.md             |  1 +
 R/Learner.R         |  3 ---
 R/MeasureAIC.R      | 12 +++++++-----
 R/MeasureBIC.R      | 10 ++++++----
 R/mlr_reflections.R |  2 +-
 man/Learner.Rd      |  2 --
 6 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 7b3909adb..8c9054232 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -2,6 +2,7 @@
 
 * fix: Quantiles must not ascend with probabilities.
 * refactor: Replace `tsk("boston_housing")` with `tsk("california_housing")`.
+* BREAKING CHANGE: Remove ``$loglik()`` method from all learners.
 
 # mlr3 0.21.1
 
diff --git a/R/Learner.R b/R/Learner.R
index 213c7855a..72014c8f5 100644
--- a/R/Learner.R
+++ b/R/Learner.R
@@ -59,9 +59,6 @@
 #' * `oob_error(...)`: Returns the out-of-bag error of the model as `numeric(1)`.
 #'   The learner must be tagged with property `"oob_error"`.
 #'
-#' * `loglik(...)`: Extracts the log-likelihood (c.f. [stats::logLik()]).
-#'   This can be used in measures like [mlr_measures_aic] or [mlr_measures_bic].
-#'
 #' * `internal_valid_scores`: Returns the internal validation score(s) of the model as a named `list()`.
 #'   Only available for [`Learner`]s with the `"validation"` property.
 #'   If the learner is not trained yet, this returns `NULL`.
diff --git a/R/MeasureAIC.R b/R/MeasureAIC.R
index 5a0203d1d..e0a988ebc 100644
--- a/R/MeasureAIC.R
+++ b/R/MeasureAIC.R
@@ -40,12 +40,14 @@ MeasureAIC = R6Class("MeasureAIC",
   private = list(
     .score = function(prediction, learner, ...) {
       learner = learner$base_learner()
-      if ("loglik" %nin% learner$properties) {
-        return(NA_real_)
-      }
-
       k = self$param_set$values$k %??% 2
-      return(stats::AIC(learner$loglik(), k = k))
+
+      tryCatch({
+        return(stats::AIC(stats::logLik(learner$model), k = k))
+      }, error = function(e) {
+        warningf("Learner '%s' does not support AIC calculation", learner$id)
+        return(NA_real_)
+      })
     }
   )
 )
diff --git a/R/MeasureBIC.R b/R/MeasureBIC.R
index 94d67d515..b3d48f205 100644
--- a/R/MeasureBIC.R
+++ b/R/MeasureBIC.R
@@ -38,11 +38,13 @@ MeasureBIC = R6Class("MeasureBIC",
   private = list(
     .score = function(prediction, learner, ...) {
       learner = learner$base_learner()
-      if ("loglik" %nin% learner$properties) {
-        return(NA_real_)
-      }
 
-      return(stats::BIC(learner$loglik()))
+      tryCatch({
+        return(stats::BIC(stats::logLik(learner$model)))
+      }, error = function(e) {
+        warningf("Learner '%s' does not support BIC calculation", learner$id)
+        return(NA_real_)
+      })
     }
   )
 )
diff --git a/R/mlr_reflections.R b/R/mlr_reflections.R
index e1111dc16..283562658 100644
--- a/R/mlr_reflections.R
+++ b/R/mlr_reflections.R
@@ -118,7 +118,7 @@ local({
   )
 
   ### Learner
-  tmp = c("featureless", "missings", "weights", "importance", "selected_features", "oob_error", "loglik", "hotstart_forward", "hotstart_backward", "validation", "internal_tuning", "marshal")
+  tmp = c("featureless", "missings", "weights", "importance", "selected_features", "oob_error", "hotstart_forward", "hotstart_backward", "validation", "internal_tuning", "marshal")
   mlr_reflections$learner_properties = list(
     classif = c(tmp, "twoclass", "multiclass"),
     regr = tmp
diff --git a/man/Learner.Rd b/man/Learner.Rd
index 8f3aacaeb..fee67c15a 100644
--- a/man/Learner.Rd
+++ b/man/Learner.Rd
@@ -46,8 +46,6 @@ To filter variables using the importance scores, see package \CRANpkg{mlr3filter
 The learner must be tagged with property \code{"selected_features"}.
 \item \code{oob_error(...)}: Returns the out-of-bag error of the model as \code{numeric(1)}.
 The learner must be tagged with property \code{"oob_error"}.
-\item \code{loglik(...)}: Extracts the log-likelihood (c.f. \code{\link[stats:logLik]{stats::logLik()}}).
-This can be used in measures like \link{mlr_measures_aic} or \link{mlr_measures_bic}.
 \item \code{internal_valid_scores}: Returns the internal validation score(s) of the model as a named \code{list()}.
 Only available for \code{\link{Learner}}s with the \code{"validation"} property.
 If the learner is not trained yet, this returns \code{NULL}.

From f89dff8457d3a6cd6800c4ce937d76de11e5aab6 Mon Sep 17 00:00:00 2001
From: Marc Becker <33069354+be-marc@users.noreply.github.com>
Date: Thu, 14 Nov 2024 17:36:54 +0100
Subject: [PATCH 3/7] fix: ignore size of globals when sequential future plan
 is used (#1209)

* fix: ignore size of globals when sequential future plan is used

* ...
---
 NEWS.md         | 1 +
 R/helper_exec.R | 7 +++++++
 2 files changed, 8 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index 8c9054232..953d87a62 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -3,6 +3,7 @@
 * fix: Quantiles must not ascend with probabilities.
 * refactor: Replace `tsk("boston_housing")` with `tsk("california_housing")`.
 * BREAKING CHANGE: Remove ``$loglik()`` method from all learners.
+* fix: Ignore `future.globals.maxSize` when `future::plan("sequential")` is used.
 
 # mlr3 0.21.1
 
diff --git a/R/helper_exec.R b/R/helper_exec.R
index 2ca51bf58..c59c0d1eb 100644
--- a/R/helper_exec.R
+++ b/R/helper_exec.R
@@ -30,6 +30,13 @@ future_map = function(n, FUN, ..., MoreArgs = list()) {
     }
     stdout = if (is_sequential) NA else TRUE
 
+    # workaround for sequential plan checking the size of the globals
+    # see https://github.com/futureverse/future/issues/197
+    if (is_sequential) {
+      old_opts = options(future.globals.maxSize = Inf)
+      on.exit(options(old_opts), add = TRUE)
+    }
+
     MoreArgs = c(MoreArgs, list(is_sequential = is_sequential))
 
     lg$debug("Running resample() via future with %i iterations", n)

From b95228a913fb39864eaa9912577b9df28573aeab Mon Sep 17 00:00:00 2001
From: Marc Becker <33069354+be-marc@users.noreply.github.com>
Date: Wed, 20 Nov 2024 08:21:44 +0100
Subject: [PATCH 4/7] feat: add characteristics field to task (#1211)

* feat: add characteristics field to task

* ...
---
 NEWS.md                    |  1 +
 R/BenchmarkResult.R        | 14 +++++++--
 R/Task.R                   | 16 ++++++++++
 R/helper_hashes.R          |  3 +-
 man/BenchmarkResult.Rd     |  2 +-
 man/Task.Rd                |  3 ++
 tests/testthat/test_Task.R | 61 ++++++++++++++++++++++++++++++++++++++
 7 files changed, 95 insertions(+), 5 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 953d87a62..95136851c 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -4,6 +4,7 @@
 * refactor: Replace `tsk("boston_housing")` with `tsk("california_housing")`.
 * BREAKING CHANGE: Remove ``$loglik()`` method from all learners.
 * fix: Ignore `future.globals.maxSize` when `future::plan("sequential")` is used.
+* feat: Add `$characteristics` field to `Task` to store additional information.
 
 # mlr3 0.21.1
 
diff --git a/R/BenchmarkResult.R b/R/BenchmarkResult.R
index 72c7d8c3f..97cc4c94d 100644
--- a/R/BenchmarkResult.R
+++ b/R/BenchmarkResult.R
@@ -19,7 +19,7 @@
 #' @template param_measures
 #'
 #' @section S3 Methods:
-#' * `as.data.table(rr, ..., reassemble_learners = TRUE, convert_predictions = TRUE, predict_sets = "test")`\cr
+#' * `as.data.table(rr, ..., reassemble_learners = TRUE, convert_predictions = TRUE, predict_sets = "test", task_characteristics = FALSE)`\cr
 #'   [BenchmarkResult] -> [data.table::data.table()]\cr
 #'   Returns a tabular view of the internal data.
 #' * `c(...)`\cr
@@ -545,9 +545,17 @@ BenchmarkResult = R6Class("BenchmarkResult",
 )
 
 #' @export
-as.data.table.BenchmarkResult = function(x, ..., hashes = FALSE, predict_sets = "test") { # nolint
+as.data.table.BenchmarkResult = function(x, ..., hashes = FALSE, predict_sets = "test", task_characteristics = FALSE) { # nolint
+  assert_flag(task_characteristics)
   tab = get_private(x)$.data$as_data_table(view = NULL, predict_sets = predict_sets)
-  tab[, c("uhash", "task", "learner", "resampling", "iteration", "prediction"), with = FALSE]
+  tab = tab[, c("uhash", "task", "learner", "resampling", "iteration", "prediction"), with = FALSE]
+
+  if (task_characteristics) {
+    tab[, characteristics := map(task, "characteristics")]
+    tab = unnest(tab, "characteristics")
+  }
+
+  tab[]
 }
 
 #' @export
diff --git a/R/Task.R b/R/Task.R
index 383ab7df8..12fa909ee 100644
--- a/R/Task.R
+++ b/R/Task.R
@@ -253,6 +253,10 @@ Task = R6Class("Task",
       if (!is.null(private$.internal_valid_task)) {
         catf(str_indent("* Validation Task:", sprintf("(%ix%i)", private$.internal_valid_task$nrow, private$.internal_valid_task$ncol)))
       }
+
+      if (!is.null(self$characteristics)) {
+        catf(str_indent("* Characteristics: ", as_short_string(self$characteristics)))
+      }
     },
 
     #' @description
@@ -1117,6 +1121,17 @@ Task = R6Class("Task",
         private$.col_hashes = self$backend$col_hashes[setdiff(unlist(private$.col_roles, use.names = FALSE), self$backend$primary_key)]
       }
       private$.col_hashes
+    },
+
+    #' @field characteristics (`list()`)\cr
+    #' List of characteristics of the task, e.g. `list(n = 5, p = 7)`.
+    characteristics = function(rhs) {
+      if (missing(rhs)) {
+        return(private$.characteristics)
+      }
+
+      private$.characteristics = assert_list(rhs, null.ok = TRUE)
+      private$.hash = NULL
     }
   ),
 
@@ -1128,6 +1143,7 @@ Task = R6Class("Task",
     .row_roles = NULL,
     .hash = NULL,
     .col_hashes = NULL,
+    .characteristics = NULL,
 
     deep_clone = function(name, value) {
       # NB: DataBackends are never copied!
diff --git a/R/helper_hashes.R b/R/helper_hashes.R
index 6fff3e4fe..211264f28 100644
--- a/R/helper_hashes.R
+++ b/R/helper_hashes.R
@@ -48,5 +48,6 @@ task_hash = function(task, use_ids, test_ids = NULL, ignore_internal_valid_task
     use_ids,
     task$col_roles,
     get_private(task)$.properties,
-    internal_valid_task_hash)
+    internal_valid_task_hash,
+    task$characteristics)
 }
diff --git a/man/BenchmarkResult.Rd b/man/BenchmarkResult.Rd
index 630cb02d7..0e1f18d9a 100644
--- a/man/BenchmarkResult.Rd
+++ b/man/BenchmarkResult.Rd
@@ -20,7 +20,7 @@ Do not modify any extracted object without cloning it first.
 \section{S3 Methods}{
 
 \itemize{
-\item \code{as.data.table(rr, ..., reassemble_learners = TRUE, convert_predictions = TRUE, predict_sets = "test")}\cr
+\item \code{as.data.table(rr, ..., reassemble_learners = TRUE, convert_predictions = TRUE, predict_sets = "test", task_characteristics = FALSE)}\cr
 \link{BenchmarkResult} -> \code{\link[data.table:data.table]{data.table::data.table()}}\cr
 Returns a tabular view of the internal data.
 \item \code{c(...)}\cr
diff --git a/man/Task.Rd b/man/Task.Rd
index 8e327a90a..f45a41ddc 100644
--- a/man/Task.Rd
+++ b/man/Task.Rd
@@ -147,6 +147,9 @@ Required for \code{\link[=convert_task]{convert_task()}}.}
 
 \item{\code{mlr3_version}}{(\code{package_version})\cr
 Package version of \code{mlr3} used to create the task.}
+
+\item{\code{characteristics}}{(\code{list()})\cr
+List of characteristics of the task, e.g. \code{list(n = 5, p = 7)}.}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/tests/testthat/test_Task.R b/tests/testthat/test_Task.R
index 35ffadfc5..442caeefa 100644
--- a/tests/testthat/test_Task.R
+++ b/tests/testthat/test_Task.R
@@ -669,3 +669,64 @@ test_that("$select changes hash", {
   h2 = task$hash
   expect_false(h1 == h2)
 })
+
+test_that("$characteristics works", {
+  task = tsk("spam")
+  characteristics = list(foo = 1, bar = "a")
+  task$characteristics = characteristics
+
+  expect_snapshot(task)
+  expect_equal(task$characteristics, characteristics)
+
+  tsk_1 = tsk("spam")
+  tsk_1$characteristics = list(n = 300)
+  tsk_2 = tsk("spam")
+  tsk_2$characteristics = list(n = 200)
+
+  expect_true(tsk_1$hash != tsk_2$hash)
+
+  learner = lrn("classif.rpart")
+  resampling = rsmp("cv", folds = 3)
+
+  design = benchmark_grid(
+    task = list(tsk_1, tsk_2),
+    learner = learner,
+    resampling = resampling
+  )
+
+  bmr = benchmark(design)
+  tab = as.data.table(bmr, task_characteristics = TRUE)
+  expect_names(names(tab), must.include = "n")
+  expect_subset(tab$n, c(300, 200))
+
+  tsk_1$characteristics = list(n = 300, f = 3)
+  tsk_2$characteristics = list(n = 200, f = 2)
+
+  design = benchmark_grid(
+    task = list(tsk_1, tsk_2),
+    learner = learner,
+    resampling = resampling
+  )
+
+  bmr = benchmark(design)
+  tab = as.data.table(bmr, task_characteristics = TRUE)
+  expect_names(names(tab), must.include = c("n", "f"))
+  expect_subset(tab$n, c(300, 200))
+  expect_subset(tab$f, c(2, 3))
+
+  tsk_1$characteristics = list(n = 300, f = 2)
+  tsk_2$characteristics = list(n = 200)
+
+  design = benchmark_grid(
+    task = list(tsk_1, tsk_2),
+    learner = learner,
+    resampling = resampling
+  )
+
+  bmr = benchmark(design)
+  tab = as.data.table(bmr, task_characteristics = TRUE)
+
+  expect_names(names(tab), must.include = c("n", "f"))
+  expect_subset(tab$n, c(300, 200))
+  expect_subset(tab$f, c(2, NA_real_))
+})

From 4b1f072682db4230c5d8ef25f6c1c7790ac4919e Mon Sep 17 00:00:00 2001
From: Marc Becker <33069354+be-marc@users.noreply.github.com>
Date: Wed, 20 Nov 2024 11:27:54 +0100
Subject: [PATCH 5/7] fix: measure checks (#1212)

---
 R/Measure.R                    | 17 +++++------------
 R/MeasureInternalValidScore.R  |  2 +-
 R/assertions.R                 | 10 ++++++++++
 tests/testthat/_snaps/Task.md  | 19 +++++++++++++++++++
 tests/testthat/test_resample.R |  5 +++++
 5 files changed, 40 insertions(+), 13 deletions(-)
 create mode 100644 tests/testthat/_snaps/Task.md

diff --git a/R/Measure.R b/R/Measure.R
index 994afdc2a..ff45b64d8 100644
--- a/R/Measure.R
+++ b/R/Measure.R
@@ -197,6 +197,9 @@ Measure = R6Class("Measure",
       assert_measure(self, task = task, learner = learner, prediction = prediction)
       assert_prediction(prediction, null.ok = "requires_no_prediction" %nin% self$properties)
 
+      # check should be added to assert_measure()
+      # except when the checks are superfluous for rr$score() and bmr$score()
+      # these checks should be added bellow
       if ("requires_task" %in% self$properties && is.null(task)) {
         stopf("Measure '%s' requires a task", self$id)
       }
@@ -205,21 +208,14 @@ Measure = R6Class("Measure",
         stopf("Measure '%s' requires a learner", self$id)
       }
 
-      if ("requires_model" %in% self$properties && (is.null(learner) || is.null(learner$model))) {
-        stopf("Measure '%s' requires the trained model", self$id)
-      }
-      if ("requires_model" %in% self$properties && is_marshaled_model(learner$model)) {
-        stopf("Measure '%s' requires the trained model, but model is in marshaled form", self$id)
+      if (!is_scalar_na(self$task_type) && self$task_type != prediction$task_type) {
+        stopf("Measure '%s' incompatible with task type '%s'", self$id, prediction$task_type)
       }
 
       if ("requires_train_set" %in% self$properties && is.null(train_set)) {
         stopf("Measure '%s' requires the train_set", self$id)
       }
 
-      if (!is_scalar_na(self$task_type) && self$task_type != prediction$task_type) {
-        stopf("Measure '%s' incompatible with task type '%s'", self$id, prediction$task_type)
-      }
-
       score_single_measure(self, task, learner, train_set, prediction)
     },
 
@@ -358,8 +354,6 @@ score_single_measure = function(measure, task, learner, train_set, prediction) {
     return(NaN)
   }
 
-
-
   if (!is_scalar_na(measure$predict_type) && measure$predict_type %nin% prediction$predict_types) {
     # TODO lgr$debug()
     return(NaN)
@@ -370,7 +364,6 @@ score_single_measure = function(measure, task, learner, train_set, prediction) {
     return(NaN)
   }
 
-
   get_private(measure)$.score(prediction = prediction, task = task, learner = learner, train_set = train_set)
 }
 
diff --git a/R/MeasureInternalValidScore.R b/R/MeasureInternalValidScore.R
index 2d60bf3e0..94770c1a1 100644
--- a/R/MeasureInternalValidScore.R
+++ b/R/MeasureInternalValidScore.R
@@ -33,7 +33,7 @@ MeasureInternalValidScore = R6Class("MeasureInternalValidScore",
       super$initialize(
         id = select %??% "internal_valid_score",
         task_type = NA_character_,
-        properties = c("na_score", "requires_model", "requires_learner", "requires_no_prediction"),
+        properties = c("na_score", "requires_learner", "requires_no_prediction"),
         predict_sets = NULL,
         predict_type = NA_character_,
         range = c(-Inf, Inf),
diff --git a/R/assertions.R b/R/assertions.R
index ebe772792..f4c76fa41 100644
--- a/R/assertions.R
+++ b/R/assertions.R
@@ -206,6 +206,7 @@ assert_measure = function(measure, task = NULL, learner = NULL, prediction = NUL
   assert_class(measure, "Measure", .var.name = .var.name)
 
   if (!is.null(task)) {
+
     if (!is_scalar_na(measure$task_type) && !test_matching_task_type(task$task_type, measure, "measure")) {
       stopf("Measure '%s' is not compatible with type '%s' of task '%s'",
         measure$id, task$task_type, task$id)
@@ -221,6 +222,15 @@ assert_measure = function(measure, task = NULL, learner = NULL, prediction = NUL
   }
 
   if (!is.null(learner)) {
+
+    if ("requires_model" %in% measure$properties && is.null(learner$model)) {
+      stopf("Measure '%s' requires the trained model", measure$id)
+    }
+
+    if ("requires_model" %in% measure$properties && is_marshaled_model(learner$model)) {
+      stopf("Measure '%s' requires the trained model, but model is in marshaled form", measure$id)
+    }
+
     if (!is_scalar_na(measure$task_type) && measure$task_type != learner$task_type) {
       stopf("Measure '%s' is not compatible with type '%s' of learner '%s'",
         measure$id, learner$task_type, learner$id)
diff --git a/tests/testthat/_snaps/Task.md b/tests/testthat/_snaps/Task.md
new file mode 100644
index 000000000..732eafbfb
--- /dev/null
+++ b/tests/testthat/_snaps/Task.md
@@ -0,0 +1,19 @@
+# $characteristics works
+
+    Code
+      task
+    Output
+      <TaskClassif:spam> (4601 x 58): HP Spam Detection
+      * Target: type
+      * Properties: twoclass
+      * Features (57):
+        - dbl (57): address, addresses, all, business, capitalAve,
+          capitalLong, capitalTotal, charDollar, charExclamation, charHash,
+          charRoundbracket, charSemicolon, charSquarebracket, conference,
+          credit, cs, data, direct, edu, email, font, free, george, hp, hpl,
+          internet, lab, labs, mail, make, meeting, money, num000, num1999,
+          num3d, num415, num650, num85, num857, order, original, our, over,
+          parts, people, pm, project, re, receive, remove, report, table,
+          technology, telnet, will, you, your
+      * Characteristics:  foo=1, bar=a
+
diff --git a/tests/testthat/test_resample.R b/tests/testthat/test_resample.R
index 27bbd7b27..7dee66823 100644
--- a/tests/testthat/test_resample.R
+++ b/tests/testthat/test_resample.R
@@ -520,3 +520,8 @@ test_that("resampling instantiated on a different task throws an error", {
   expect_error(resample(tsk("pima"), lrn("classif.rpart"), resampling), "The resampling was probably instantiated on a different task")
 
 })
+
+test_that("$score() checks for models", {
+  rr = resample(tsk("mtcars"), lrn("regr.debug"), rsmp("holdout"))
+  expect_error(rr$score(msr("aic")), "requires the trained model")
+})

From 4f1762fd8b21886d339725f98e60574bf19280ea Mon Sep 17 00:00:00 2001
From: Marc Becker <33069354+be-marc@users.noreply.github.com>
Date: Mon, 25 Nov 2024 08:43:22 +0100
Subject: [PATCH 6/7] release: 0.22.0 (#1204)

* release: 0.22.0

* ...

* ...

* ...
---
 DESCRIPTION                |  2 +-
 NEWS.md                    |  3 ++-
 R/BenchmarkResult.R        |  2 +-
 man/Task.Rd                |  6 +++---
 tests/testthat/test_Task.R | 18 +++++++++---------
 5 files changed, 16 insertions(+), 15 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 28236d721..32db05e66 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: mlr3
 Title: Machine Learning in R - Next Generation
-Version: 0.21.1.9000
+Version: 0.22.0
 Authors@R:
   c(
     person("Michel", "Lang", , "michellang@gmail.com", role = "aut",
diff --git a/NEWS.md b/NEWS.md
index 95136851c..4fbe00895 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,7 +1,8 @@
-# mlr3 (development version)
+# mlr3 0.22.0
 
 * fix: Quantiles must not ascend with probabilities.
 * refactor: Replace `tsk("boston_housing")` with `tsk("california_housing")`.
+* feat: Require unique learner ids in `benchmark_grid()`.
 * BREAKING CHANGE: Remove ``$loglik()`` method from all learners.
 * fix: Ignore `future.globals.maxSize` when `future::plan("sequential")` is used.
 * feat: Add `$characteristics` field to `Task` to store additional information.
diff --git a/R/BenchmarkResult.R b/R/BenchmarkResult.R
index 97cc4c94d..9ee35c3ce 100644
--- a/R/BenchmarkResult.R
+++ b/R/BenchmarkResult.R
@@ -551,7 +551,7 @@ as.data.table.BenchmarkResult = function(x, ..., hashes = FALSE, predict_sets =
   tab = tab[, c("uhash", "task", "learner", "resampling", "iteration", "prediction"), with = FALSE]
 
   if (task_characteristics) {
-    tab[, characteristics := map(task, "characteristics")]
+    set(tab, j = "characteristics", value = map(tab$task, "characteristics"))
     tab = unnest(tab, "characteristics")
   }
 
diff --git a/man/Task.Rd b/man/Task.Rd
index f45a41ddc..4e925c1ad 100644
--- a/man/Task.Rd
+++ b/man/Task.Rd
@@ -147,9 +147,6 @@ Required for \code{\link[=convert_task]{convert_task()}}.}
 
 \item{\code{mlr3_version}}{(\code{package_version})\cr
 Package version of \code{mlr3} used to create the task.}
-
-\item{\code{characteristics}}{(\code{list()})\cr
-List of characteristics of the task, e.g. \code{list(n = 5, p = 7)}.}
 }
 \if{html}{\out{</div>}}
 }
@@ -303,6 +300,9 @@ Alternatively, you can provide a \code{\link[=data.frame]{data.frame()}} with th
 Hash (unique identifier) for all columns except the \code{primary_key}: A \code{character} vector, named by the columns that each element refers to.\cr
 Columns of different \code{\link{Task}}s or \code{\link{DataBackend}}s that have agreeing \code{col_hashes} always represent the same data, given that the same \code{row}s are selected.
 The reverse is not necessarily true: There can be columns with the same content that have different \code{col_hashes}.}
+
+\item{\code{characteristics}}{(\code{list()})\cr
+List of characteristics of the task, e.g. \code{list(n = 5, p = 7)}.}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/tests/testthat/test_Task.R b/tests/testthat/test_Task.R
index 442caeefa..e97e03360 100644
--- a/tests/testthat/test_Task.R
+++ b/tests/testthat/test_Task.R
@@ -689,9 +689,9 @@ test_that("$characteristics works", {
   resampling = rsmp("cv", folds = 3)
 
   design = benchmark_grid(
-    task = list(tsk_1, tsk_2),
-    learner = learner,
-    resampling = resampling
+    tasks = list(tsk_1, tsk_2),
+    learners = learner,
+    resamplings = resampling
   )
 
   bmr = benchmark(design)
@@ -703,9 +703,9 @@ test_that("$characteristics works", {
   tsk_2$characteristics = list(n = 200, f = 2)
 
   design = benchmark_grid(
-    task = list(tsk_1, tsk_2),
-    learner = learner,
-    resampling = resampling
+    tasks = list(tsk_1, tsk_2),
+    learners = learner,
+    resamplings = resampling
   )
 
   bmr = benchmark(design)
@@ -718,9 +718,9 @@ test_that("$characteristics works", {
   tsk_2$characteristics = list(n = 200)
 
   design = benchmark_grid(
-    task = list(tsk_1, tsk_2),
-    learner = learner,
-    resampling = resampling
+    tasks = list(tsk_1, tsk_2),
+    learners = learner,
+    resamplings = resampling
   )
 
   bmr = benchmark(design)

From 282b53a334e89a9e07164130c8f36bb3f390049c Mon Sep 17 00:00:00 2001
From: be-marc <marcbecker@posteo.de>
Date: Mon, 25 Nov 2024 08:55:42 +0100
Subject: [PATCH 7/7] release: 0.22.0.9000

---
 DESCRIPTION | 2 +-
 NEWS.md     | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 32db05e66..3ec978986 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: mlr3
 Title: Machine Learning in R - Next Generation
-Version: 0.22.0
+Version: 0.22.0.9000
 Authors@R:
   c(
     person("Michel", "Lang", , "michellang@gmail.com", role = "aut",
diff --git a/NEWS.md b/NEWS.md
index 4fbe00895..d13823242 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,5 @@
+# mlr3 (development version)
+
 # mlr3 0.22.0
 
 * fix: Quantiles must not ascend with probabilities.