From 2fe8746f71aaa55e12fff49f7934c96cb0d29ee8 Mon Sep 17 00:00:00 2001
From: Marc Becker <33069354+be-marc@users.noreply.github.com>
Date: Fri, 20 Dec 2024 16:52:13 +0100
Subject: [PATCH 01/10] refactor: predict_type and predict_types (#1233)

* refactor: predict_type and predict_types

* ...

* ...
---
 NEWS.md        |  3 +++
 R/Learner.R    | 21 +++++++++++++--------
 man/Learner.Rd | 13 ++++++++-----
 3 files changed, 24 insertions(+), 13 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 83d6dfbb0..6992b98cd 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,8 @@
 # mlr3 (development version)
 
+* BREAKING CHANGE: `Learner$predict_types` is read-only now.
+* docs: Clear up behavior of `Learner$predict_type` after training.
+
 # mlr3 0.22.1
 
 * fix: Extend `assert_measure()` with checks for trained models in `assert_scorable()`.
diff --git a/R/Learner.R b/R/Learner.R
index d1b7e7863..461dd150f 100644
--- a/R/Learner.R
+++ b/R/Learner.R
@@ -157,11 +157,6 @@ Learner = R6Class("Learner",
     #' @template field_task_type
     task_type = NULL,
 
-    #' @field predict_types (`character()`)\cr
-    #' Stores the possible predict types the learner is capable of.
-    #' A complete list of candidate predict types, grouped by task type, is stored in [`mlr_reflections$learner_predict_types`][mlr_reflections].
-    predict_types = NULL,
-
     #' @field feature_types (`character()`)\cr
     #' Stores the feature types the learner can handle, e.g. `"logical"`, `"numeric"`, or `"factor"`.
     #' A complete list of candidate feature types, grouped by task type, is stored in [`mlr_reflections$task_feature_types`][mlr_reflections].
@@ -214,7 +209,7 @@ Learner = R6Class("Learner",
       self$task_type = assert_choice(task_type, mlr_reflections$task_types$type)
       private$.param_set = assert_param_set(param_set)
       self$feature_types = assert_ordered_set(feature_types, mlr_reflections$task_feature_types, .var.name = "feature_types")
-      self$predict_types = assert_ordered_set(predict_types, names(mlr_reflections$learner_predict_types[[task_type]]),
+      private$.predict_types = assert_ordered_set(predict_types, names(mlr_reflections$learner_predict_types[[task_type]]),
         empty.ok = FALSE, .var.name = "predict_types")
       private$.predict_type = predict_types[1L]
       self$properties = sort(assert_subset(properties, mlr_reflections$learner_properties[[task_type]]))
@@ -627,6 +622,8 @@ Learner = R6Class("Learner",
     #' @field predict_type (`character(1)`)\cr
     #' Stores the currently active predict type, e.g. `"response"`.
     #' Must be an element of `$predict_types`.
+    #' A few learners already use the predict type during training.
+    #' So there is no guarantee that changing the predict type after training will have any effect or does not lead to errors.
     predict_type = function(rhs) {
       if (missing(rhs)) {
         return(private$.predict_type)
@@ -648,8 +645,6 @@ Learner = R6Class("Learner",
       private$.param_set
     },
 
-
-
     #' @field fallback ([Learner])\cr
     #' Returns the fallback learner set with `$encapsulate()`.
     fallback = function(rhs) {
@@ -672,6 +667,15 @@ Learner = R6Class("Learner",
       }
       assert_r6(rhs, "HotstartStack", null.ok = TRUE)
       private$.hotstart_stack = rhs
+    },
+
+    #' @field predict_types (`character()`)\cr
+    #' Stores the possible predict types the learner is capable of.
+    #' A complete list of candidate predict types, grouped by task type, is stored in [`mlr_reflections$learner_predict_types`][mlr_reflections].
+    #' This field is read-only.
+    predict_types = function(rhs) {
+      assert_ro_binding(rhs)
+      return(private$.predict_types)
     }
   ),
 
@@ -679,6 +683,7 @@ Learner = R6Class("Learner",
     .encapsulation = c(train = "none", predict = "none"),
     .fallback = NULL,
     .predict_type = NULL,
+    .predict_types = NULL,
     .param_set = NULL,
     .hotstart_stack = NULL,
 
diff --git a/man/Learner.Rd b/man/Learner.Rd
index f5ede1944..70d7cf8f9 100644
--- a/man/Learner.Rd
+++ b/man/Learner.Rd
@@ -195,10 +195,6 @@ Task type, e.g. \code{"classif"} or \code{"regr"}.
 For a complete list of possible task types (depending on the loaded packages),
 see \code{\link[=mlr_reflections]{mlr_reflections$task_types$type}}.}
 
-\item{\code{predict_types}}{(\code{character()})\cr
-Stores the possible predict types the learner is capable of.
-A complete list of candidate predict types, grouped by task type, is stored in \code{\link[=mlr_reflections]{mlr_reflections$learner_predict_types}}.}
-
 \item{\code{feature_types}}{(\code{character()})\cr
 Stores the feature types the learner can handle, e.g. \code{"logical"}, \code{"numeric"}, or \code{"factor"}.
 A complete list of candidate feature types, grouped by task type, is stored in \code{\link[=mlr_reflections]{mlr_reflections$task_feature_types}}.}
@@ -289,7 +285,9 @@ Hash (unique identifier) for this partial object, excluding some components whic
 
 \item{\code{predict_type}}{(\code{character(1)})\cr
 Stores the currently active predict type, e.g. \code{"response"}.
-Must be an element of \verb{$predict_types}.}
+Must be an element of \verb{$predict_types}.
+A few learners already use the predict type during training.
+So there is no guarantee that changing the predict type after training will have any effect or does not lead to errors.}
 
 \item{\code{param_set}}{(\link[paradox:ParamSet]{paradox::ParamSet})\cr
 Set of hyperparameters.}
@@ -302,6 +300,11 @@ Returns the encapsulation settings set with \verb{$encapsulate()}.}
 
 \item{\code{hotstart_stack}}{(\link{HotstartStack})\cr.
 Stores \code{HotstartStack}.}
+
+\item{\code{predict_types}}{(\code{character()})\cr
+Stores the possible predict types the learner is capable of.
+A complete list of candidate predict types, grouped by task type, is stored in \code{\link[=mlr_reflections]{mlr_reflections$learner_predict_types}}.
+This field is read-only.}
 }
 \if{html}{\out{</div>}}
 }

From 89544ffafa789eba982ef42197c45f1beedb476f Mon Sep 17 00:00:00 2001
From: Marc Becker <33069354+be-marc@users.noreply.github.com>
Date: Fri, 20 Dec 2024 16:53:15 +0100
Subject: [PATCH 02/10] refactor: allow uft8 in column names by default (#1234)

---
 NEWS.md                         |  2 ++
 R/DataBackendRename.R           |  2 +-
 R/Task.R                        | 10 +++-------
 R/helper.R                      |  5 -----
 R/zzz.R                         |  4 ----
 inst/testthat/helper_autotest.R |  2 --
 man/mlr3-package.Rd             |  4 ----
 tests/testthat/test_Task.R      |  9 ---------
 8 files changed, 6 insertions(+), 32 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 6992b98cd..69e7292de 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,7 @@
 # mlr3 (development version)
 
+* Column names with UTF-8 characters are now allowed by default.
+The option `mlr3.allow_utf8_names` is removed.
 * BREAKING CHANGE: `Learner$predict_types` is read-only now.
 * docs: Clear up behavior of `Learner$predict_type` after training.
 
diff --git a/R/DataBackendRename.R b/R/DataBackendRename.R
index 81617a283..d4838d5e1 100644
--- a/R/DataBackendRename.R
+++ b/R/DataBackendRename.R
@@ -9,7 +9,7 @@ DataBackendRename = R6Class("DataBackendRename", inherit = DataBackend, cloneabl
       assert_character(old, any.missing = FALSE, unique = TRUE)
       assert_subset(old, b$colnames)
       assert_character(new, any.missing = FALSE, len = length(old))
-      assert_names(new, if (allow_utf8_names()) "unique" else "strict")
+      assert_names(new, "unique")
 
       ii = old != new
       old = old[ii]
diff --git a/R/Task.R b/R/Task.R
index a081f15ca..0f077c477 100644
--- a/R/Task.R
+++ b/R/Task.R
@@ -128,13 +128,9 @@ Task = R6Class("Task",
       cn = self$backend$colnames
       rn = self$backend$rownames
 
-      if (allow_utf8_names()) {
-        assert_names(cn, "unique", .var.name = "column names")
-        if (any(grepl("%", cn, fixed = TRUE))) {
-          stopf("Column names may not contain special character '%%'")
-        }
-      } else {
-        assert_names(cn, "strict", .var.name = "column names")
+      assert_names(cn, "unique", .var.name = "column names")
+      if (any(grepl("%", cn, fixed = TRUE))) {
+        stopf("Column names may not contain special character '%%'")
       }
 
       self$col_info = col_info(self$backend)
diff --git a/R/helper.R b/R/helper.R
index b27ecb192..39460eb08 100644
--- a/R/helper.R
+++ b/R/helper.R
@@ -4,11 +4,6 @@ translate_types = function(x) {
   factor(map_values(x, r_types, p_types), levels = p_types)
 }
 
-
-allow_utf8_names = function() {
-  isTRUE(getOption("mlr3.allow_utf8_names"))
-}
-
 get_featureless_learner = function(task_type) {
   if (!is.na(task_type)) {
     id = paste0(task_type, ".featureless")
diff --git a/R/zzz.R b/R/zzz.R
index b34c219ba..84f0077b3 100644
--- a/R/zzz.R
+++ b/R/zzz.R
@@ -50,10 +50,6 @@
 #' * `"mlr3.debug"`: If set to `TRUE`, parallelization via \CRANpkg{future} is disabled to simplify
 #'   debugging and provide more concise tracebacks.
 #'   Note that results computed in debug mode use a different seeding mechanism and are **not reproducible**.
-#' * `"mlr3.allow_utf8_names"`: If set to `TRUE`, checks on the feature names are relaxed, allowing
-#'   non-ascii characters in column names. This is an experimental and temporal option to
-#'   pave the way for text analysis, and will likely be removed in a future version of the package.
-#'   analysis.
 #' * `"mlr3.warn_version_mismatch"`: Set to `FALSE` to silence warnings raised during predict if a learner has been
 #'   trained with a different version version of mlr3.
 #'
diff --git a/inst/testthat/helper_autotest.R b/inst/testthat/helper_autotest.R
index 377e7d152..f1f647b34 100644
--- a/inst/testthat/helper_autotest.R
+++ b/inst/testthat/helper_autotest.R
@@ -81,8 +81,6 @@ generate_generic_tasks = function(learner, proto) {
 
   # task with non-ascii feature names
   if (p > 0L) {
-    opts = options(mlr3.allow_utf8_names = TRUE)
-    on.exit(options(opts))
     sel = proto$feature_types[list(learner$feature_types), "id", on = "type", with = FALSE, nomatch = NULL][[1L]]
     tasks$utf8_feature_names = proto$clone(deep = TRUE)$select(sel)
     old = sel[1L]
diff --git a/man/mlr3-package.Rd b/man/mlr3-package.Rd
index fb5185e56..9c4f5f689 100644
--- a/man/mlr3-package.Rd
+++ b/man/mlr3-package.Rd
@@ -63,10 +63,6 @@ parallelization with \CRANpkg{future}. Defaults to 1.
 \item \code{"mlr3.debug"}: If set to \code{TRUE}, parallelization via \CRANpkg{future} is disabled to simplify
 debugging and provide more concise tracebacks.
 Note that results computed in debug mode use a different seeding mechanism and are \strong{not reproducible}.
-\item \code{"mlr3.allow_utf8_names"}: If set to \code{TRUE}, checks on the feature names are relaxed, allowing
-non-ascii characters in column names. This is an experimental and temporal option to
-pave the way for text analysis, and will likely be removed in a future version of the package.
-analysis.
 \item \code{"mlr3.warn_version_mismatch"}: Set to \code{FALSE} to silence warnings raised during predict if a learner has been
 trained with a different version version of mlr3.
 }
diff --git a/tests/testthat/test_Task.R b/tests/testthat/test_Task.R
index b1005c81d..880b2726b 100644
--- a/tests/testthat/test_Task.R
+++ b/tests/testthat/test_Task.R
@@ -552,15 +552,6 @@ test_that("set_levels", {
 })
 
 test_that("special chars in feature names (#697)", {
-  prev = options(mlr3.allow_utf8_names = FALSE)
-  on.exit(options(prev))
-
-  expect_error(
-    TaskRegr$new("test", data.table(`%^` = 1:3, t = 3:1), target = "t"),
-    "comply"
-  )
-  options(mlr3.allow_utf8_names = TRUE)
-
   expect_error(
     TaskRegr$new("test", data.table(`%asd` = 1:3, t = 3:1), target = "t")
     ,

From 54ec9b16379fef0e3bd5fca0c1dc05ae266a6c01 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Maximilian=20M=C3=BCcke?= <muecke.maximilian@gmail.com>
Date: Mon, 23 Dec 2024 09:38:14 +0100
Subject: [PATCH 03/10] perf: use %chin% (#1224)

---
 R/DataBackendRename.R                |  2 +-
 R/HotstartStack.R                    |  4 ++--
 R/LearnerClassifDebug.R              |  8 ++++----
 R/LearnerClassifRpart.R              |  6 +++---
 R/LearnerRegrRpart.R                 |  2 +-
 R/Measure.R                          | 10 +++++-----
 R/PredictionClassif.R                |  2 +-
 R/PredictionDataClassif.R            |  4 ++--
 R/PredictionDataRegr.R               |  8 ++++----
 R/PredictionRegr.R                   |  8 ++++----
 R/Task.R                             | 18 +++++++++---------
 R/assertions.R                       |  6 +++---
 R/benchmark.R                        |  6 +++---
 R/resample.R                         |  6 +++---
 R/set_validate.R                     |  2 +-
 R/worker.R                           |  4 ++--
 tests/testthat/test_HotstartStack.R  | 12 ++++++------
 tests/testthat/test_PredictionRegr.R |  4 ++--
 tests/testthat/test_Task.R           | 16 ++++++++--------
 tests/testthat/test_benchmark.R      | 10 +++++-----
 tests/testthat/test_convert_task.R   |  4 ++--
 tests/testthat/test_resample.R       |  2 +-
 22 files changed, 72 insertions(+), 72 deletions(-)

diff --git a/R/DataBackendRename.R b/R/DataBackendRename.R
index d4838d5e1..5024682a9 100644
--- a/R/DataBackendRename.R
+++ b/R/DataBackendRename.R
@@ -15,7 +15,7 @@ DataBackendRename = R6Class("DataBackendRename", inherit = DataBackend, cloneabl
       old = old[ii]
       new = new[ii]
 
-      if (self$primary_key %in% old) {
+      if (self$primary_key %chin% old) {
         stopf("Renaming the primary key is not supported")
       }
 
diff --git a/R/HotstartStack.R b/R/HotstartStack.R
index 90096abff..208ca3a68 100644
--- a/R/HotstartStack.R
+++ b/R/HotstartStack.R
@@ -202,9 +202,9 @@ calculate_cost = function(start_learner, learner, hotstart_id) {
   cost = learner$param_set$values[[hotstart_id]] - start_learner$param_set$values[[hotstart_id]]
   if (cost == 0) return(-1)
 
-  if ("hotstart_backward" %in% learner$properties && "hotstart_forward" %in% learner$properties) {
+  if ("hotstart_backward" %chin% learner$properties && "hotstart_forward" %chin% learner$properties) {
     if (cost < 0) 0 else cost
-  } else if ("hotstart_backward" %in% learner$properties) {
+  } else if ("hotstart_backward" %chin% learner$properties) {
     if (cost < 0) 0 else NA_real_
   } else {
     if (cost > 0) cost else NA_real_
diff --git a/R/LearnerClassifDebug.R b/R/LearnerClassifDebug.R
index 908bb7965..4f6984f1d 100644
--- a/R/LearnerClassifDebug.R
+++ b/R/LearnerClassifDebug.R
@@ -163,7 +163,7 @@ LearnerClassifDebug = R6Class("LearnerClassifDebug", inherit = LearnerClassif,
       pv = self$param_set$get_values(tags = "train")
       pv$count_marshaling = pv$count_marshaling %??% FALSE
       roll = function(name) {
-        name %in% names(pv) && pv[[name]] > runif(1L)
+        name %chin% names(pv) && pv[[name]] > runif(1L)
       }
 
       if (!is.null(pv$sleep_train)) {
@@ -248,7 +248,7 @@ LearnerClassifDebug = R6Class("LearnerClassifDebug", inherit = LearnerClassif,
       n = task$nrow
       pv = self$param_set$get_values(tags = "predict")
       roll = function(name) {
-        name %in% names(pv) && pv[[name]] > runif(1L)
+        name %chin% names(pv) && pv[[name]] > runif(1L)
       }
 
       if (!is.null(pv$sleep_predict)) {
@@ -281,7 +281,7 @@ LearnerClassifDebug = R6Class("LearnerClassifDebug", inherit = LearnerClassif,
       response = prob = NULL
       missing_type = pv$predict_missing_type %??% "na"
 
-      if ("response" %in% self$predict_type) {
+      if ("response" %chin% self$predict_type) {
         response = rep.int(unclass(model$response), n)
         if (!is.null(pv$predict_missing)) {
           ii = sample.int(n, n * pv$predict_missing)
@@ -292,7 +292,7 @@ LearnerClassifDebug = R6Class("LearnerClassifDebug", inherit = LearnerClassif,
         }
       }
 
-      if ("prob" %in% self$predict_type) {
+      if ("prob" %chin% self$predict_type) {
         cl = task$class_names
         prob = matrix(runif(n * length(cl)), nrow = n)
         prob = prob / rowSums(prob)
diff --git a/R/LearnerClassifRpart.R b/R/LearnerClassifRpart.R
index 02070150f..05342335c 100644
--- a/R/LearnerClassifRpart.R
+++ b/R/LearnerClassifRpart.R
@@ -77,7 +77,7 @@ LearnerClassifRpart = R6Class("LearnerClassifRpart", inherit = LearnerClassif,
     .train = function(task) {
       pv = self$param_set$get_values(tags = "train")
       names(pv) = replace(names(pv), names(pv) == "keep_model", "model")
-      if ("weights" %in% task$properties) {
+      if ("weights" %chin% task$properties) {
         pv = insert_named(pv, list(weights = task$weights$weight))
       }
 
@@ -89,11 +89,11 @@ LearnerClassifRpart = R6Class("LearnerClassifRpart", inherit = LearnerClassif,
       newdata = task$data(cols = task$feature_names)
       response = prob = NULL
 
-      if ("response" %in% self$predict_type) {
+      if ("response" %chin% self$predict_type) {
         response = invoke(predict, self$model, newdata = newdata, type = "class",
           .opts = allow_partial_matching, .args = pv)
         response = unname(response)
-      } else if ("prob" %in% self$predict_type) {
+      } else if ("prob" %chin% self$predict_type) {
         prob = invoke(predict, self$model, newdata = newdata, type = "prob",
           .opts = allow_partial_matching, .args = pv)
         rownames(prob) = NULL
diff --git a/R/LearnerRegrRpart.R b/R/LearnerRegrRpart.R
index 5910fbcd0..f78785799 100644
--- a/R/LearnerRegrRpart.R
+++ b/R/LearnerRegrRpart.R
@@ -77,7 +77,7 @@ LearnerRegrRpart = R6Class("LearnerRegrRpart", inherit = LearnerRegr,
     .train = function(task) {
       pv = self$param_set$get_values(tags = "train")
       names(pv) = replace(names(pv), names(pv) == "keep_model", "model")
-      if ("weights" %in% task$properties) {
+      if ("weights" %chin% task$properties) {
         pv = insert_named(pv, list(weights = task$weights$weight))
       }
 
diff --git a/R/Measure.R b/R/Measure.R
index 5c58b85e8..a4cd9fcf1 100644
--- a/R/Measure.R
+++ b/R/Measure.R
@@ -200,11 +200,11 @@ Measure = R6Class("Measure",
       # 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$properties && is.null(task)) {
+      if ("requires_task" %chin% self$properties && is.null(task)) {
         stopf("Measure '%s' requires a task", self$id)
       }
 
-      if ("requires_learner" %in% self$properties && is.null(learner)) {
+      if ("requires_learner" %chin% self$properties && is.null(learner)) {
         stopf("Measure '%s' requires a learner", self$id)
       }
 
@@ -212,7 +212,7 @@ Measure = R6Class("Measure",
         stopf("Measure '%s' incompatible with task type '%s'", self$id, prediction$task_type)
       }
 
-      if ("requires_train_set" %in% self$properties && is.null(train_set)) {
+      if ("requires_train_set" %chin% self$properties && is.null(train_set)) {
         stopf("Measure '%s' requires the train_set", self$id)
       }
 
@@ -258,7 +258,7 @@ Measure = R6Class("Measure",
     #' @template field_predict_sets
     predict_sets = function(rhs) {
       if (!missing(rhs)) {
-        private$.predict_sets = assert_subset(rhs, mlr_reflections$predict_sets, empty.ok = "requires_no_prediction" %in% self$properties)
+        private$.predict_sets = assert_subset(rhs, mlr_reflections$predict_sets, empty.ok = "requires_no_prediction" %chin% self$properties)
       }
       private$.predict_sets
     },
@@ -385,7 +385,7 @@ score_single_measure = function(measure, task, learner, train_set, prediction) {
 #' @noRd
 score_measures = function(obj, measures, reassemble = TRUE, view = NULL, iters = NULL) {
   reassemble_learners = reassemble ||
-    some(measures, function(m) any(c("requires_learner", "requires_model") %in% m$properties))
+    some(measures, function(m) any(c("requires_learner", "requires_model") %chin% m$properties))
   tab = get_private(obj)$.data$as_data_table(view = view, reassemble_learners = reassemble_learners, convert_predictions = FALSE)
 
   if (!is.null(iters)) {
diff --git a/R/PredictionClassif.R b/R/PredictionClassif.R
index bc8aa9335..2b452a663 100644
--- a/R/PredictionClassif.R
+++ b/R/PredictionClassif.R
@@ -175,7 +175,7 @@ PredictionClassif = R6Class("PredictionClassif", inherit = Prediction,
 as.data.table.PredictionClassif = function(x, ...) { # nolint
   tab = as.data.table(x$data[c("row_ids", "truth", "response")])
 
-  if ("prob" %in% x$predict_types) {
+  if ("prob" %chin% x$predict_types) {
     prob = as.data.table(x$data$prob)
     setnames(prob, names(prob), paste0("prob.", names(prob)))
     tab = rcbind(tab, prob)
diff --git a/R/PredictionDataClassif.R b/R/PredictionDataClassif.R
index 94206354e..06fb0e554 100644
--- a/R/PredictionDataClassif.R
+++ b/R/PredictionDataClassif.R
@@ -132,11 +132,11 @@ create_empty_prediction_data.TaskClassif = function(task, learner) {
     truth = factor(character(), levels = cn)
   )
 
-  if ("response" %in% predict_types) {
+  if ("response" %chin% predict_types) {
     pdata$response = pdata$truth
   }
 
-  if ("prob" %in% predict_types) {
+  if ("prob" %chin% predict_types) {
     pdata$prob = matrix(numeric(), nrow = 0L, ncol = length(cn), dimnames = list(NULL, cn))
   }
 
diff --git a/R/PredictionDataRegr.R b/R/PredictionDataRegr.R
index 080dd549b..bfe49c28a 100644
--- a/R/PredictionDataRegr.R
+++ b/R/PredictionDataRegr.R
@@ -99,7 +99,7 @@ c.PredictionDataRegr = function(..., keep_duplicates = TRUE) { # nolint
   result = as.list(tab)
   result$quantiles = quantiles
 
-  if ("distr" %in% predict_types[[1L]]) {
+  if ("distr" %chin% predict_types[[1L]]) {
     require_namespaces("distr6", msg = "To predict probability distributions, please install %s")
     result$distr = do.call(c, map(dots, "distr"))
   }
@@ -137,15 +137,15 @@ create_empty_prediction_data.TaskRegr = function(task, learner) {
     truth = numeric()
   )
 
-  if ("response" %in% predict_types) {
+  if ("response" %chin% predict_types) {
     pdata$response = pdata$truth
   }
 
-  if ("se" %in% predict_types) {
+  if ("se" %chin% predict_types) {
     pdata$se = pdata$truth
   }
 
-  if ("distr" %in% predict_types) {
+  if ("distr" %chin% predict_types) {
     pdata$distr = list()
   }
 
diff --git a/R/PredictionRegr.R b/R/PredictionRegr.R
index ddf07d774..63c73fa68 100644
--- a/R/PredictionRegr.R
+++ b/R/PredictionRegr.R
@@ -61,7 +61,7 @@ PredictionRegr = R6Class("PredictionRegr", inherit = Prediction,
       self$data = pdata
       predict_types = intersect(names(mlr_reflections$learner_predict_types[["regr"]]), names(pdata))
       # response is in saved in quantiles matrix
-      if ("quantiles" %in% predict_types) predict_types = union(predict_types, "response")
+      if ("quantiles" %chin% predict_types) predict_types = union(predict_types, "response")
       self$predict_types = predict_types
       if (is.null(pdata$response)) private$.quantile_response = attr(quantiles, "response")
     }
@@ -94,7 +94,7 @@ PredictionRegr = R6Class("PredictionRegr", inherit = Prediction,
     #' Access the stored vector distribution.
     #' Requires package `distr6`(in repository \url{https://raphaels1.r-universe.dev}) .
     distr = function() {
-      if ("distr" %in% self$predict_types) {
+      if ("distr" %chin% self$predict_types) {
         require_namespaces("distr6", msg = "To predict probability distributions, please install %s")
       }
       return(self$data$distr)
@@ -111,12 +111,12 @@ PredictionRegr = R6Class("PredictionRegr", inherit = Prediction,
 as.data.table.PredictionRegr = function(x, ...) { # nolint
   tab = as.data.table(x$data[c("row_ids", "truth", "response", "se")])
 
-  if ("quantiles" %in% x$predict_types) {
+  if ("quantiles" %chin% x$predict_types) {
     tab = rcbind(tab, as.data.table(x$data$quantiles))
     set(tab, j = "response", value = x$response)
   }
 
-  if ("distr" %in% x$predict_types) {
+  if ("distr" %chin% x$predict_types) {
     require_namespaces("distr6", msg = "To predict probability distributions, please install %s")
     tab$distr = list(x$distr)
   }
diff --git a/R/Task.R b/R/Task.R
index 0f077c477..2bfbc34f3 100644
--- a/R/Task.R
+++ b/R/Task.R
@@ -222,7 +222,7 @@ Task = R6Class("Task",
 
       # print additional columns as specified in reflections
       before = mlr_reflections$task_print_col_roles$before
-      iwalk(before[before %in% names(roles)], function(role, str) {
+      iwalk(before[before %chin% names(roles)], function(role, str) {
         catn(str_indent(sprintf("* %s:", str), roles[[role]]))
       })
 
@@ -242,7 +242,7 @@ Task = R6Class("Task",
 
       # print additional columns are specified in reflections
       after = mlr_reflections$task_print_col_roles$after
-      iwalk(after[after %in% names(roles)], function(role, str) {
+      iwalk(after[after %chin% names(roles)], function(role, str) {
         catn(str_indent(sprintf("* %s:", str), roles[[role]]))
       })
 
@@ -367,7 +367,7 @@ Task = R6Class("Task",
     levels = function(cols = NULL) {
       if (is.null(cols)) {
         cols = unlist(private$.col_roles[c("target", "feature")], use.names = FALSE)
-        cols = self$col_info[get("id") %in% cols & get("type") %in% c("factor", "ordered"), "id", with = FALSE][[1L]]
+        cols = self$col_info[get("id") %chin% cols & get("type") %chin% c("factor", "ordered"), "id", with = FALSE][[1L]]
       } else {
         assert_subset(cols, self$col_info$id)
       }
@@ -465,7 +465,7 @@ Task = R6Class("Task",
       type_check = TRUE
 
       if (is.data.frame(data)) {
-        pk_in_backend = pk %in% names(data)
+        pk_in_backend = pk %chin% names(data)
         type_check = FALSE # done by auto-converter
 
         keep_cols = intersect(names(data), self$col_info$id)
@@ -517,7 +517,7 @@ Task = R6Class("Task",
       }
 
       # merge factor levels
-      ii = tab[type %in% c("factor", "ordered"), which = TRUE]
+      ii = tab[type %chin% c("factor", "ordered"), which = TRUE]
       for (i in ii) {
         x = tab[["levels"]][[i]]
         y = tab[["levels_y"]][[i]]
@@ -726,7 +726,7 @@ Task = R6Class("Task",
     #' @return Modified `self`.
     droplevels = function(cols = NULL) {
       assert_has_backend(self)
-      tab = self$col_info[get("type") %in% c("factor", "ordered"), c("id", "levels", "fix_factor_levels"), with = FALSE]
+      tab = self$col_info[get("type") %chin% c("factor", "ordered"), c("id", "levels", "fix_factor_levels"), with = FALSE]
       if (!is.null(cols)) {
         tab = tab[list(cols), on = "id", nomatch = NULL]
       }
@@ -926,7 +926,7 @@ Task = R6Class("Task",
 
       assert_has_backend(self)
       assert_list(rhs, .var.name = "row_roles")
-      if ("test" %in% names(rhs) || "holdout" %in% names(rhs)) {
+      if ("test" %chin% names(rhs) || "holdout" %chin% names(rhs)) {
         stopf("Setting row roles 'test'/'holdout' is no longer possible.")
       }
       assert_names(names(rhs), "unique", permutation.of = mlr_reflections$task_row_roles, .var.name = "names of row_roles")
@@ -1333,7 +1333,7 @@ col_info = function(x, ...) {
 #' @export
 col_info.data.table = function(x, primary_key = character(), ...) { # nolint
   types = map_chr(x, function(x) class(x)[1L])
-  discrete = setdiff(names(types)[types %in% c("factor", "ordered")], primary_key)
+  discrete = setdiff(names(types)[types %chin% c("factor", "ordered")], primary_key)
   levels = insert_named(named_list(names(types)), lapply(x[, discrete, with = FALSE], distinct_values, drop = FALSE))
   data.table(id = names(types), type = unname(types), levels = levels, key = "id")
 }
@@ -1342,7 +1342,7 @@ col_info.data.table = function(x, primary_key = character(), ...) { # nolint
 #' @export
 col_info.DataBackend = function(x, ...) { # nolint
   types = map_chr(x$head(1L), function(x) class(x)[1L])
-  discrete = setdiff(names(types)[types %in% c("factor", "ordered")], x$primary_key)
+  discrete = setdiff(names(types)[types %chin% c("factor", "ordered")], x$primary_key)
   levels = insert_named(named_list(names(types)), x$distinct(rows = NULL, cols = discrete))
   data.table(id = names(types), type = unname(types), levels = levels, key = "id")
 }
diff --git a/R/assertions.R b/R/assertions.R
index fa6618dbd..d86b5ce43 100644
--- a/R/assertions.R
+++ b/R/assertions.R
@@ -181,7 +181,7 @@ assert_predictable = function(task, learner) {
       stopf("Learner '%s' has received tasks with different columns in train and predict.", learner$id)
     }
 
-    ids = train_task$col_info[get("id") %in% cols_train, "id"]$id
+    ids = train_task$col_info[get("id") %chin% cols_train, "id"]$id
     ci_predict = task$col_info[list(ids), c("id", "type", "levels"), on = "id"]
     ci_train = train_task$col_info[list(ids), c("id", "type", "levels"), on = "id"]
 
@@ -260,11 +260,11 @@ assert_measure = function(measure, task = NULL, learner = NULL, prediction = NUL
 #' @param prediction ([Prediction]).
 #' @rdname mlr_assertions
 assert_scorable = function(measure, task, learner, prediction = NULL, .var.name = vname(measure)) {
-  if ("requires_model" %in% measure$properties && is.null(learner$model)) {
+  if ("requires_model" %chin% 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)) {
+  if ("requires_model" %chin% measure$properties && is_marshaled_model(learner$model)) {
     stopf("Measure '%s' requires the trained model, but model is in marshaled form", measure$id)
   }
 
diff --git a/R/benchmark.R b/R/benchmark.R
index 5f26c38a6..63b41f180 100644
--- a/R/benchmark.R
+++ b/R/benchmark.R
@@ -110,13 +110,13 @@ benchmark = function(design, store_models = FALSE, store_backends = TRUE, encaps
 
   setDT(design)
   task = learner = resampling = NULL
-  if ("task" %in% clone) {
+  if ("task" %chin% clone) {
     design[, "task" := list(list(task[[1L]]$clone())), by = list(hashes(task))]
   }
-  if ("learner" %in% clone) {
+  if ("learner" %chin% clone) {
     design[, "learner" := list(list(learner[[1L]]$clone())), by = list(hashes(learner))]
   }
-  if ("resampling" %in% clone) {
+  if ("resampling" %chin% clone) {
     design[, "resampling" := list(list(resampling[[1L]]$clone())), by = list(hashes(resampling))]
   }
 
diff --git a/R/resample.R b/R/resample.R
index cc1bb88f2..c82ffe4ea 100644
--- a/R/resample.R
+++ b/R/resample.R
@@ -57,9 +57,9 @@
 #' print(bmr1$combine(bmr2))
 resample = function(task, learner, resampling, store_models = FALSE, store_backends = TRUE, encapsulate = NA_character_, allow_hotstart = FALSE, clone = c("task", "learner", "resampling"), unmarshal = TRUE) {
   assert_subset(clone, c("task", "learner", "resampling"))
-  task = assert_task(as_task(task, clone = "task" %in% clone))
-  learner = assert_learner(as_learner(learner, clone = "learner" %in% clone, discard_state = TRUE))
-  resampling = assert_resampling(as_resampling(resampling, clone = "resampling" %in% clone))
+  task = assert_task(as_task(task, clone = "task" %chin% clone))
+  learner = assert_learner(as_learner(learner, clone = "learner" %chin% clone, discard_state = TRUE))
+  resampling = assert_resampling(as_resampling(resampling, clone = "resampling" %chin% clone))
   assert_flag(store_models)
   assert_flag(store_backends)
   # this does not check the internal validation task as it might not be set yet
diff --git a/R/set_validate.R b/R/set_validate.R
index 5e8825b26..c89a61e00 100644
--- a/R/set_validate.R
+++ b/R/set_validate.R
@@ -26,7 +26,7 @@ set_validate = function(learner, validate, ...) {
 
 #' @export
 set_validate.Learner = function(learner, validate, ...) {
-  if (!"validation" %in% learner$properties) {
+  if (!"validation" %chin% learner$properties) {
     stopf("Learner '%s' does not support validation.", learner$id)
   }
   learner$validate = validate
diff --git a/R/worker.R b/R/worker.R
index e52daa23f..b5a794329 100644
--- a/R/worker.R
+++ b/R/worker.R
@@ -255,7 +255,7 @@ workhorse = function(iteration, task, learner, resampling, param_values = NULL,
   if (!is.null(pb)) {
     pb(sprintf("%s|%s|i:%i", task$id, learner$id, iteration))
   }
-  if ("internal_valid" %in% learner$predict_sets && is.null(task$internal_valid_task) && is.null(get0("validate", learner))) {
+  if ("internal_valid" %chin% learner$predict_sets && is.null(task$internal_valid_task) && is.null(get0("validate", learner))) {
     stopf("Cannot set the predict_type field of learner '%s' to 'internal_valid' if there is no internal validation task configured", learner$id)
   }
 
@@ -351,7 +351,7 @@ prediction_tasks_and_sets = function(task, train_result, validate, sets, predict
     return(list(tasks = tasks[predict_sets], sets = sets[predict_sets]))
   }
 
-  if ("internal_valid" %in% predict_sets) {
+  if ("internal_valid" %chin% predict_sets) {
     if (is.numeric(validate) || identical(validate, "test")) {
       # in this scenario, the internal_valid_task was created during learner_train, which means that it used the
       # primary task. The selected ids are returned via the train result
diff --git a/tests/testthat/test_HotstartStack.R b/tests/testthat/test_HotstartStack.R
index 55148c8c3..a1e1dc3c3 100644
--- a/tests/testthat/test_HotstartStack.R
+++ b/tests/testthat/test_HotstartStack.R
@@ -134,7 +134,7 @@ test_that("HotstartStack works with backward target learner and decreased hotsta
   learner_1$train(task)
 
   learner = lrn("classif.debug", iter = 1)
-  learner$properties[learner$properties %in% "hotstart_forward"] = "hotstart_backward"
+  learner$properties[learner$properties %chin% "hotstart_forward"] = "hotstart_backward"
   hot = HotstartStack$new(list(learner_1))
 
   expect_equal(hot$start_cost(learner, task$hash), 0)
@@ -151,7 +151,7 @@ test_that("HotstartStack works with backward target learner when cost of hotstar
   learner_2$train(task)
 
   learner = lrn("classif.debug", iter = 3)
-  learner$properties[learner$properties %in% "hotstart_forward"] = "hotstart_backward"
+  learner$properties[learner$properties %chin% "hotstart_forward"] = "hotstart_backward"
   hot = HotstartStack$new(list(learner_1, learner_2))
 
   expect_equal(hot$start_cost(learner, task$hash), c(0, 0))
@@ -166,7 +166,7 @@ test_that("HotstartStack works when hotstart values of hotstart learners are low
   learner_2$train(task)
 
   learner = lrn("classif.debug", iter = 2)
-  learner$properties[learner$properties %in% "hotstart_forward"] = "hotstart_backward"
+  learner$properties[learner$properties %chin% "hotstart_forward"] = "hotstart_backward"
   hot = HotstartStack$new(list(learner_1, learner_2))
 
   expect_equal(hot$start_cost(learner, task$hash), c(0, NA_real_))
@@ -181,7 +181,7 @@ test_that("HotstartStack works when backward hotstart and target learner are equ
   learner_1$train(task)
 
   learner = lrn("classif.debug", iter = 1)
-  learner$properties[learner$properties %in% "hotstart_forward"] = "hotstart_backward"
+  learner$properties[learner$properties %chin% "hotstart_forward"] = "hotstart_backward"
   hot = HotstartStack$new(list(learner_1))
 
   expect_equal(hot$start_cost(learner, task$hash), -1)
@@ -197,7 +197,7 @@ test_that("HotstartStack works with backward target learner when hotstart values
   learner_1$train(task)
 
   learner = lrn("classif.debug", iter = 2)
-  learner$properties[learner$properties %in% "hotstart_forward"] = "hotstart_backward"
+  learner$properties[learner$properties %chin% "hotstart_forward"] = "hotstart_backward"
   hot = HotstartStack$new(list(learner_1))
 
   expect_equal(hot$start_cost(learner, task$hash), NA_real_)
@@ -218,7 +218,7 @@ test_that("HotstartStack works with backward target learner when hotstart learne
   learner_4$train(task)
 
   learner = lrn("classif.debug", iter = 2)
-  learner$properties[learner$properties %in% "hotstart_forward"] = "hotstart_backward"
+  learner$properties[learner$properties %chin% "hotstart_forward"] = "hotstart_backward"
   hot = HotstartStack$new(list(learner_1, learner_2, learner_3, learner_4))
 
   expect_equal(hot$start_cost(learner, task$hash), c(NA_real_, -1, 0, NA_real_))
diff --git a/tests/testthat/test_PredictionRegr.R b/tests/testthat/test_PredictionRegr.R
index 0005e3a72..90a47b738 100644
--- a/tests/testthat/test_PredictionRegr.R
+++ b/tests/testthat/test_PredictionRegr.R
@@ -45,9 +45,9 @@ test_that("c drops se (#250)", {
 
   pred = do.call(c, rr$predictions())
   expect_null(pred$data$se)
-  expect_false("se" %in% pred$predict_types)
+  expect_false("se" %chin% pred$predict_types)
   expect_true(allMissing(pred$se))
-  expect_false("se" %in% names(as.data.table(pred)))
+  expect_false("se" %chin% names(as.data.table(pred)))
 })
 
 test_that("distr", {
diff --git a/tests/testthat/test_Task.R b/tests/testthat/test_Task.R
index 880b2726b..fbfba18eb 100644
--- a/tests/testthat/test_Task.R
+++ b/tests/testthat/test_Task.R
@@ -237,11 +237,11 @@ test_that("rename works", {
 
 test_that("stratify works", {
   task = tsk("iris")
-  expect_false("strata" %in% task$properties)
+  expect_false("strata" %chin% task$properties)
   expect_null(task$strata)
 
   task$col_roles$stratum = task$target_names
-  expect_true("strata" %in% task$properties)
+  expect_true("strata" %chin% task$properties)
   tab = task$strata
   expect_data_table(tab, ncols = 2, nrows = 3)
   expect_list(tab$row_id, "integer")
@@ -252,8 +252,8 @@ test_that("groups/weights work", {
   task = TaskRegr$new("test", b, target = "y")
   task$set_row_roles(16:20, character())
 
-  expect_false("groups" %in% task$properties)
-  expect_false("weights" %in% task$properties)
+  expect_false("groups" %chin% task$properties)
+  expect_false("weights" %chin% task$properties)
   expect_null(task$groups)
   expect_null(task$weights)
 
@@ -434,7 +434,7 @@ test_that("col roles getters/setters", {
   })
 
   task$col_roles$feature = setdiff(task$col_roles$feature, "Sepal.Length")
-  expect_false("Sepal.Length" %in% task$feature_names)
+  expect_false("Sepal.Length" %chin% task$feature_names)
 })
 
 test_that("Task$row_names", {
@@ -471,7 +471,7 @@ test_that("Task$set_col_roles", {
 
   task$set_col_roles("mass", add_to = "feature")
   expect_equal(task$n_features, 8L)
-  expect_true("mass" %in% task$feature_names)
+  expect_true("mass" %chin% task$feature_names)
 
   task$set_col_roles("age", roles = "weight")
   expect_equal(task$n_features, 7L)
@@ -480,7 +480,7 @@ test_that("Task$set_col_roles", {
 
   task$set_col_roles("age", add_to = "feature", remove_from = "weight")
   expect_equal(task$n_features, 8L)
-  expect_true("age" %in% task$feature_names)
+  expect_true("age" %chin% task$feature_names)
   expect_null(task$weights)
 })
 
@@ -650,7 +650,7 @@ test_that("cbind supports non-standard primary key (#961)", {
   b = as_data_backend(tbl, primary_key = "myid")
   task = as_task_regr(b, target = "y")
   task$cbind(data.table(x1 = 10:1))
-  expect_true("x1" %in% task$feature_names)
+  expect_true("x1" %chin% task$feature_names)
 })
 
 test_that("$select changes hash", {
diff --git a/tests/testthat/test_benchmark.R b/tests/testthat/test_benchmark.R
index d62a4928d..8a14a6015 100644
--- a/tests/testthat/test_benchmark.R
+++ b/tests/testthat/test_benchmark.R
@@ -85,9 +85,9 @@ test_that("bmr$combine()", {
     expect_data_table(get_private(bmr_new)$.data$data$fact, nrows = 6L)
     expect_data_table(get_private(bmr_combined)$.data$data$fact, nrows = 24L)
 
-    expect_false("pima" %in% bmr$tasks$task_id)
-    expect_true("pima" %in% bmr_new$tasks$task_id)
-    expect_true("pima" %in% bmr_combined$tasks$task_id)
+    expect_false("pima" %chin% bmr$tasks$task_id)
+    expect_true("pima" %chin% bmr_new$tasks$task_id)
+    expect_true("pima" %chin% bmr_combined$tasks$task_id)
   }
 
   rr = resample(tsk("zoo"), lrn("classif.rpart"), rsmp("holdout"))
@@ -212,7 +212,7 @@ test_that("extract params", {
   aggr = bmr$aggregate(params = TRUE)
   expect_list(aggr$params[[1]], names = "unique", len = 0L)
 
-  expect_true(all(c("warnings", "errors") %in% names(bmr$score(conditions = TRUE))))
+  expect_true(all(c("warnings", "errors") %chin% names(bmr$score(conditions = TRUE))))
 })
 
 test_that("benchmark_grid", {
@@ -480,7 +480,7 @@ test_that("param_values in benchmark", {
     x
   }
   trained = bmr$learners$learner
-  ii = which(map_lgl(trained, function(x) "cp" %in% names(x$param_set$values))) # find learner with cp
+  ii = which(map_lgl(trained, function(x) "cp" %chin% names(x$param_set$values))) # find learner with cp
   expect_count(ii)
 
   expect_equal(sortnames(bmr$learners$learner[-ii][[1]]$param_set$values), list(minbucket = 2, minsplit = 12, xval = 0))
diff --git a/tests/testthat/test_convert_task.R b/tests/testthat/test_convert_task.R
index 9758f2dad..da87eebfc 100644
--- a/tests/testthat/test_convert_task.R
+++ b/tests/testthat/test_convert_task.R
@@ -109,7 +109,7 @@ test_that("convert_task reconstructs task", {
   tsk2 = convert_task(task2)
   expect_equal(task2$nrow, tsk2$nrow)
   expect_equal(task2$ncol, tsk2$ncol)
-  expect_true("twoclass" %in% tsk2$properties)
+  expect_true("twoclass" %chin% tsk2$properties)
 
   task3 = task2
   task3$row_roles$use = 1:150
@@ -117,7 +117,7 @@ test_that("convert_task reconstructs task", {
   tsk3$man = "mlr3::mlr_tasks_iris"
   expect_equal(task3$nrow, tsk3$nrow)
   expect_equal(task3$ncol, tsk3$ncol)
-  expect_true("multiclass" %in% tsk3$properties)
+  expect_true("multiclass" %chin% tsk3$properties)
   expect_equal(task, tsk3, ignore_attr = TRUE)
 })
 
diff --git a/tests/testthat/test_resample.R b/tests/testthat/test_resample.R
index 5668734a9..a7cc0102c 100644
--- a/tests/testthat/test_resample.R
+++ b/tests/testthat/test_resample.R
@@ -94,7 +94,7 @@ test_that("empty train/predict sets", {
 })
 
 test_that("conditions are returned", {
-  expect_true(all(c("warnings", "errors") %in% names(rr$score(conditions = TRUE))))
+  expect_true(all(c("warnings", "errors") %chin% names(rr$score(conditions = TRUE))))
 })
 
 test_that("save/load roundtrip", {

From 52deba9d9ab3d5124b4e9a3adec44b0e79608886 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Maximilian=20M=C3=BCcke?= <muecke.maximilian@gmail.com>
Date: Mon, 6 Jan 2025 16:57:09 +0100
Subject: [PATCH 04/10] feat(task): add missing ordered in properties (#1238)

---
 R/Task.R    | 4 +++-
 man/Task.Rd | 1 +
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/R/Task.R b/R/Task.R
index 2bfbc34f3..66b7cafc5 100644
--- a/R/Task.R
+++ b/R/Task.R
@@ -896,6 +896,7 @@ Task = R6Class("Task",
     #' * `"strata"`: The task is resampled using one or more stratification variables (role `"stratum"`).
     #' * `"groups"`: The task comes with grouping/blocking information (role `"group"`).
     #' * `"weights"`: The task comes with observation weights (role `"weight"`).
+    #' * `"ordered"`: The task has columns which define the row order (role `"order"`).
     #'
     #' Note that above listed properties are calculated from the `$col_roles` and may not be set explicitly.
     properties = function(rhs) {
@@ -905,7 +906,8 @@ Task = R6Class("Task",
           private$.properties,
           if (length(col_roles$group)) "groups" else NULL,
           if (length(col_roles$stratum)) "strata" else NULL,
-          if (length(col_roles$weight)) "weights" else NULL
+          if (length(col_roles$weight)) "weights" else NULL,
+          if (length(col_roles$order)) "ordered" else NULL
         )
       } else {
         private$.properties = assert_set(rhs, .var.name = "properties")
diff --git a/man/Task.Rd b/man/Task.Rd
index 4e925c1ad..53a7afe97 100644
--- a/man/Task.Rd
+++ b/man/Task.Rd
@@ -198,6 +198,7 @@ The following properties are currently standardized and understood by tasks in \
 \item \code{"strata"}: The task is resampled using one or more stratification variables (role \code{"stratum"}).
 \item \code{"groups"}: The task comes with grouping/blocking information (role \code{"group"}).
 \item \code{"weights"}: The task comes with observation weights (role \code{"weight"}).
+\item \code{"ordered"}: The task has columns which define the row order (role \code{"order"}).
 }
 
 Note that above listed properties are calculated from the \verb{$col_roles} and may not be set explicitly.}

From 47a3a51d75ab3f92a1bc23b5072b23b20ac17706 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Maximilian=20M=C3=BCcke?= <muecke.maximilian@gmail.com>
Date: Mon, 6 Jan 2025 17:01:01 +0100
Subject: [PATCH 05/10] feat(task): allow date feature (#1237)

---
 R/auto_convert.R                   | 29 +++++++++++++++++++++++++++++
 R/mlr_reflections.R                |  2 +-
 inst/testthat/helper_autotest.R    |  3 ++-
 tests/testthat/test_auto_convert.R |  6 ++++++
 4 files changed, 38 insertions(+), 2 deletions(-)

diff --git a/R/auto_convert.R b/R/auto_convert.R
index 999625e15..5c10a9a90 100644
--- a/R/auto_convert.R
+++ b/R/auto_convert.R
@@ -28,6 +28,10 @@ ee[["logical___POSIXct"]] =
   function(value, type, levels) {
     if (allMissing(value)) .POSIXct(value, tz = "") else value
   }
+ee[["logical___Date"]] =
+  function(value, type, levels) {
+    if (allMissing(value)) as.Date(value) else value
+  }
 
 ## from: integer
 ee[["integer___logical"]] =
@@ -44,6 +48,8 @@ ee[["integer___ordered"]] =
   ee[["logical___ordered"]]
 ee[["integer___POSIXct"]] =
   ee[["logical___POSIXct"]]
+ee[["integer___Date"]] =
+  ee[["logical___Date"]]
 
 ## from: numeric
 ee[["numeric___logical"]] =
@@ -60,6 +66,8 @@ ee[["numeric___ordered"]] =
   ee[["logical___ordered"]]
 ee[["numeric___POSIXct"]] =
   ee[["logical___POSIXct"]]
+ee[["numeric___Date"]] =
+  ee[["logical___Date"]]
 
 ## from: character
 ee[["character___logical"]] =
@@ -83,6 +91,11 @@ ee[["character___POSIXct"]] =
     x = try(as.POSIXct(value, ""), silent = TRUE)
     if (inherits(x, "try-error")) value else x
   }
+ee[["character___Date"]] =
+  function(value, type, levels) {
+    x = try(as.Date(value), silent = TRUE)
+    if (inherits(x, "try-error")) value else x
+  }
 
 ## from: factor
 ee[["factor___logical"]] =
@@ -99,6 +112,8 @@ ee[["factor___ordered"]] =
   }
 ee[["factor___POSIXct"]] =
   ee[["character___POSIXct"]]
+ee[["factor___Date"]] =
+  ee[["character___Date"]]
 
 ## from: ordered
 ee[["ordered___character"]] =
@@ -109,6 +124,20 @@ ee[["ordered___ordered"]] =
   ee[["ordered___ordered"]]
 ee[["ordered___POSIXct"]] =
   ee[["character___POSIXct"]]
+ee[["ordered___Date"]] =
+  ee[["character___Date"]]
+
+## from: POSIXct
+ee[["POSIXct___Date"]] =
+  function(value, type, levels) {
+    as.Date(value)
+  }
+
+## from: Date
+ee[["Date___POSIXct"]] =
+  function(value, type, levels) {
+    as.POSIXct(value)
+  }
 
 rm(ee)
 # nolint end
diff --git a/R/mlr_reflections.R b/R/mlr_reflections.R
index 283562658..4ac532741 100644
--- a/R/mlr_reflections.R
+++ b/R/mlr_reflections.R
@@ -87,7 +87,7 @@ local({
   )
 
   mlr_reflections$task_feature_types = c(
-    lgl = "logical", int = "integer", dbl = "numeric", chr = "character", fct = "factor", ord = "ordered", pxc = "POSIXct"
+    lgl = "logical", int = "integer", dbl = "numeric", chr = "character", fct = "factor", ord = "ordered", pxc = "POSIXct", dte = "Date"
   )
 
   mlr_reflections$task_row_roles = c(
diff --git a/inst/testthat/helper_autotest.R b/inst/testthat/helper_autotest.R
index f1f647b34..fc3fcc41b 100644
--- a/inst/testthat/helper_autotest.R
+++ b/inst/testthat/helper_autotest.R
@@ -118,7 +118,8 @@ generate_data = function(learner, N) {
       character = sample(rep_len(letters[1:2], N)),
       factor = sample(factor(rep_len(c("f1", "f2"), N), levels = c("f1", "f2"))),
       ordered = sample(ordered(rep_len(c("o1", "o2"), N), levels = c("o1", "o2"))),
-      POSIXct = Sys.time() - runif(N, min = 0, max = 10 * 365 * 24 * 60 * 60)
+      POSIXct = Sys.time() - runif(N, min = 0, max = 10 * 365 * 24 * 60 * 60),
+      Date = Sys.Date() - runif(N, min = 0, max = 10 * 365)
     )
   }
   types = unique(learner$feature_types)
diff --git a/tests/testthat/test_auto_convert.R b/tests/testthat/test_auto_convert.R
index d17a9ac24..c7129793a 100644
--- a/tests/testthat/test_auto_convert.R
+++ b/tests/testthat/test_auto_convert.R
@@ -243,4 +243,10 @@ test_that("POSIXct", {
     auto_convert("2020-01-20 10:00:00", "x", "POSIXct", character()),
     as.POSIXct("2020-01-20 10:00:00", "")
   )
+
+  expect_date(auto_convert(Sys.time(), "x", "Date", character()))
+})
+
+test_that("Date", {
+  expect_date(auto_convert("2021-04-21", "x", "Date", character()))
 })

From 9c953178059216032e8d4868e06296614a54063b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Maximilian=20M=C3=BCcke?= <muecke.maximilian@gmail.com>
Date: Mon, 6 Jan 2025 17:01:29 +0100
Subject: [PATCH 06/10] refactor: use more set_values() (#1239)

* refactor: use more set_values()

* docs: adjust text to reference set_values for changing param set
---
 R/Learner.R                       | 4 ++--
 R/LearnerClassifDebug.R           | 2 +-
 R/LearnerClassifFeatureless.R     | 2 +-
 R/LearnerClassifRpart.R           | 2 +-
 R/LearnerRegrFeatureless.R        | 2 +-
 R/LearnerRegrRpart.R              | 2 +-
 R/MeasureClassifCosts.R           | 2 +-
 R/MeasureDebug.R                  | 2 +-
 R/MeasureSelectedFeatures.R       | 2 +-
 R/Resampling.R                    | 2 +-
 R/ResamplingBootstrap.R           | 2 +-
 R/ResamplingCV.R                  | 2 +-
 R/ResamplingHoldout.R             | 2 +-
 R/ResamplingRepeatedCV.R          | 2 +-
 R/ResamplingSubsampling.R         | 2 +-
 R/TaskGeneratorMoons.R            | 2 +-
 man/Learner.Rd                    | 4 ++--
 man/Resampling.Rd                 | 2 +-
 man/mlr_learners_classif.debug.Rd | 2 +-
 19 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/R/Learner.R b/R/Learner.R
index 461dd150f..84fd7a075 100644
--- a/R/Learner.R
+++ b/R/Learner.R
@@ -72,10 +72,10 @@
 #' All information about hyperparameters is stored in the slot `param_set` which is a [paradox::ParamSet].
 #' The printer gives an overview about the ids of available hyperparameters, their storage type, lower and upper bounds,
 #' possible levels (for factors), default values and assigned values.
-#' To set hyperparameters, assign a named list to the subslot `values`:
+#' To set hyperparameters, call the `set_values()` method on the `param_set`:
 #' ```
 #' lrn = lrn("classif.rpart")
-#' lrn$param_set$values = list(minsplit = 3, cp = 0.01)
+#' lrn$param_set$set_values(minsplit = 3, cp = 0.01)
 #' ```
 #' Note that this operation replaces all previously set hyperparameter values.
 #' If you only intend to change one specific hyperparameter value and leave the others as-is, you can use the helper function [mlr3misc::insert_named()]:
diff --git a/R/LearnerClassifDebug.R b/R/LearnerClassifDebug.R
index 4f6984f1d..ed7ebfc92 100644
--- a/R/LearnerClassifDebug.R
+++ b/R/LearnerClassifDebug.R
@@ -37,7 +37,7 @@
 #' @export
 #' @examples
 #' learner = lrn("classif.debug")
-#' learner$param_set$values = list(message_train = 1, save_tasks = TRUE)
+#' learner$param_set$set_values(message_train = 1, save_tasks = TRUE)
 #'
 #' # this should signal a message
 #' task = tsk("penguins")
diff --git a/R/LearnerClassifFeatureless.R b/R/LearnerClassifFeatureless.R
index 35c3aeb17..995839fb0 100644
--- a/R/LearnerClassifFeatureless.R
+++ b/R/LearnerClassifFeatureless.R
@@ -34,7 +34,7 @@ LearnerClassifFeatureless = R6Class("LearnerClassifFeatureless", inherit = Learn
       ps = ps(
         method = p_fct(c("mode", "sample", "weighted.sample"), default = "mode", tags = "predict")
       )
-      ps$values = list(method = "mode")
+      ps$set_values(method = "mode")
       super$initialize(
         id = "classif.featureless",
         feature_types = mlr_reflections$task_feature_types,
diff --git a/R/LearnerClassifRpart.R b/R/LearnerClassifRpart.R
index 05342335c..c3fc6896d 100644
--- a/R/LearnerClassifRpart.R
+++ b/R/LearnerClassifRpart.R
@@ -37,7 +37,7 @@ LearnerClassifRpart = R6Class("LearnerClassifRpart", inherit = LearnerClassif,
         usesurrogate   = p_int(0L, 2L, default = 2L, tags = "train"),
         xval           = p_int(0L, default = 10L, tags = "train")
       )
-      ps$values = list(xval = 0L)
+      ps$set_values(xval = 0L)
 
       super$initialize(
         id = "classif.rpart",
diff --git a/R/LearnerRegrFeatureless.R b/R/LearnerRegrFeatureless.R
index 501c48535..ec349d295 100644
--- a/R/LearnerRegrFeatureless.R
+++ b/R/LearnerRegrFeatureless.R
@@ -23,7 +23,7 @@ LearnerRegrFeatureless = R6Class("LearnerRegrFeatureless", inherit = LearnerRegr
       ps = ps(
         robust = p_lgl(default = TRUE, tags = "train")
       )
-      ps$values = list(robust = FALSE)
+      ps$set_values(robust = FALSE)
 
       super$initialize(
         id = "regr.featureless",
diff --git a/R/LearnerRegrRpart.R b/R/LearnerRegrRpart.R
index f78785799..35e2f2f0e 100644
--- a/R/LearnerRegrRpart.R
+++ b/R/LearnerRegrRpart.R
@@ -37,7 +37,7 @@ LearnerRegrRpart = R6Class("LearnerRegrRpart", inherit = LearnerRegr,
         usesurrogate   = p_int(0L, 2L, default = 2L, tags = "train"),
         xval           = p_int(0L, default = 10L, tags = "train")
       )
-      ps$values = list(xval = 0L)
+      ps$set_values(xval = 0L)
 
       super$initialize(
         id = "regr.rpart",
diff --git a/R/MeasureClassifCosts.R b/R/MeasureClassifCosts.R
index 63456d213..063780a85 100644
--- a/R/MeasureClassifCosts.R
+++ b/R/MeasureClassifCosts.R
@@ -45,7 +45,7 @@ MeasureClassifCosts = R6Class("MeasureClassifCosts",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = ps(normalize = p_lgl(tags = "required"))
-      param_set$values = list(normalize = TRUE)
+      param_set$set_values(normalize = TRUE)
 
       super$initialize(
         id = "classif.costs",
diff --git a/R/MeasureDebug.R b/R/MeasureDebug.R
index 1efb64329..8e02c0a72 100644
--- a/R/MeasureDebug.R
+++ b/R/MeasureDebug.R
@@ -27,7 +27,7 @@ MeasureDebugClassif = R6Class("MeasureDebugClassif",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = ps(na_ratio = p_dbl(0, 1, tags = "required"))
-      param_set$values = list(na_ratio = 0)
+      param_set$set_values(na_ratio = 0)
       super$initialize(
         id = "debug_classif",
         param_set = param_set,
diff --git a/R/MeasureSelectedFeatures.R b/R/MeasureSelectedFeatures.R
index db4748608..d53ead467 100644
--- a/R/MeasureSelectedFeatures.R
+++ b/R/MeasureSelectedFeatures.R
@@ -31,7 +31,7 @@ MeasureSelectedFeatures = R6Class("MeasureSelectedFeatures",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = ps(normalize = p_lgl(tags = "required"))
-      param_set$values = list(normalize = FALSE)
+      param_set$set_values(normalize = FALSE)
 
       super$initialize(
         id = "selected_features",
diff --git a/R/Resampling.R b/R/Resampling.R
index 89aa3e89b..a33544d54 100644
--- a/R/Resampling.R
+++ b/R/Resampling.R
@@ -56,7 +56,7 @@
 #' r$param_set$values
 #'
 #' # Do only 3 repeats on 10% of the data
-#' r$param_set$values = list(ratio = 0.1, repeats = 3)
+#' r$param_set$set_values(ratio = 0.1, repeats = 3)
 #' r$param_set$values
 #'
 #' # Instantiate on penguins task
diff --git a/R/ResamplingBootstrap.R b/R/ResamplingBootstrap.R
index b4e75e942..54667059d 100644
--- a/R/ResamplingBootstrap.R
+++ b/R/ResamplingBootstrap.R
@@ -49,7 +49,7 @@ ResamplingBootstrap = R6Class("ResamplingBootstrap", inherit = Resampling,
         ratio   = p_dbl(0, upper = 1, tags = "required"),
         repeats = p_int(1L, tags = "required")
       )
-      ps$values = list(ratio = 1, repeats = 30L)
+      ps$set_values(ratio = 1, repeats = 30L)
 
       super$initialize(id = "bootstrap", param_set = ps, duplicated_ids = TRUE,
         label = "Bootstrap", man = "mlr3::mlr_resamplings_bootstrap")
diff --git a/R/ResamplingCV.R b/R/ResamplingCV.R
index 4b36a71b0..34a0e60fc 100644
--- a/R/ResamplingCV.R
+++ b/R/ResamplingCV.R
@@ -44,7 +44,7 @@ ResamplingCV = R6Class("ResamplingCV", inherit = Resampling,
       ps = ps(
         folds = p_int(2L, tags = "required")
       )
-      ps$values = list(folds = 10L)
+      ps$set_values(folds = 10L)
 
       super$initialize(id = "cv", param_set = ps,
         label = "Cross-Validation", man = "mlr3::mlr_resamplings_cv")
diff --git a/R/ResamplingHoldout.R b/R/ResamplingHoldout.R
index 0a5d7ad8d..0c47a12e9 100644
--- a/R/ResamplingHoldout.R
+++ b/R/ResamplingHoldout.R
@@ -45,7 +45,7 @@ ResamplingHoldout = R6Class("ResamplingHoldout", inherit = Resampling,
       ps = ps(
         ratio = p_dbl(0, 1, tags = "required")
       )
-      ps$values = list(ratio = 2 / 3)
+      ps$set_values(ratio = 2 / 3)
 
       super$initialize(id = "holdout", param_set = ps,
         label = "Holdout", man = "mlr3::mlr_resamplings_holdout")
diff --git a/R/ResamplingRepeatedCV.R b/R/ResamplingRepeatedCV.R
index 212106eb8..bbf3b492c 100644
--- a/R/ResamplingRepeatedCV.R
+++ b/R/ResamplingRepeatedCV.R
@@ -56,7 +56,7 @@ ResamplingRepeatedCV = R6Class("ResamplingRepeatedCV", inherit = Resampling,
         folds   = p_int(2L, tags = "required"),
         repeats = p_int(1L)
       )
-      ps$values = list(repeats = 10L, folds = 10L)
+      ps$set_values(repeats = 10L, folds = 10L)
       super$initialize(id = "repeated_cv", param_set = ps,
         label = "Repeated Cross-Validation", man = "mlr3::mlr_resamplings_repeated_cv")
     },
diff --git a/R/ResamplingSubsampling.R b/R/ResamplingSubsampling.R
index 99a447f3c..beeae7f1b 100644
--- a/R/ResamplingSubsampling.R
+++ b/R/ResamplingSubsampling.R
@@ -48,7 +48,7 @@ ResamplingSubsampling = R6Class("ResamplingSubsampling", inherit = Resampling,
         ratio   = p_dbl(0, 1, tags = "required"),
         repeats = p_int(1, tags = "required")
       )
-      ps$values = list(repeats = 30L, ratio = 2 / 3)
+      ps$set_values(repeats = 30L, ratio = 2 / 3)
 
       super$initialize(id = "subsampling", param_set = ps,
         label = "Subsampling", man = "mlr3::mlr_resamplings_subsampling")
diff --git a/R/TaskGeneratorMoons.R b/R/TaskGeneratorMoons.R
index e2036d2b6..7a1b7be7a 100644
--- a/R/TaskGeneratorMoons.R
+++ b/R/TaskGeneratorMoons.R
@@ -27,7 +27,7 @@ TaskGeneratorMoons = R6Class("TaskGeneratorMoons",
       ps = ps(
         sigma = p_dbl(0, tags = "required")
       )
-      ps$values = list(sigma = 1)
+      ps$set_values(sigma = 1)
 
       super$initialize(id = "moons", task_type = "classif", param_set = ps,
         label = "Moons Classification", man = "mlr3::mlr_task_generators_moons")
diff --git a/man/Learner.Rd b/man/Learner.Rd
index 70d7cf8f9..817222e0f 100644
--- a/man/Learner.Rd
+++ b/man/Learner.Rd
@@ -61,10 +61,10 @@ If the learner is not trained yet, this returns \code{NULL}.
 All information about hyperparameters is stored in the slot \code{param_set} which is a \link[paradox:ParamSet]{paradox::ParamSet}.
 The printer gives an overview about the ids of available hyperparameters, their storage type, lower and upper bounds,
 possible levels (for factors), default values and assigned values.
-To set hyperparameters, assign a named list to the subslot \code{values}:
+To set hyperparameters, call the \code{set_values()} method on the \code{param_set}:
 
 \if{html}{\out{<div class="sourceCode">}}\preformatted{lrn = lrn("classif.rpart")
-lrn$param_set$values = list(minsplit = 3, cp = 0.01)
+lrn$param_set$set_values(minsplit = 3, cp = 0.01)
 }\if{html}{\out{</div>}}
 
 Note that this operation replaces all previously set hyperparameter values.
diff --git a/man/Resampling.Rd b/man/Resampling.Rd
index 26692dd46..c2e62bb05 100644
--- a/man/Resampling.Rd
+++ b/man/Resampling.Rd
@@ -53,7 +53,7 @@ r = rsmp("subsampling")
 r$param_set$values
 
 # Do only 3 repeats on 10\% of the data
-r$param_set$values = list(ratio = 0.1, repeats = 3)
+r$param_set$set_values(ratio = 0.1, repeats = 3)
 r$param_set$values
 
 # Instantiate on penguins task
diff --git a/man/mlr_learners_classif.debug.Rd b/man/mlr_learners_classif.debug.Rd
index a6e5ca749..e8c89c46a 100644
--- a/man/mlr_learners_classif.debug.Rd
+++ b/man/mlr_learners_classif.debug.Rd
@@ -77,7 +77,7 @@ lrn("classif.debug")
 
 \examples{
 learner = lrn("classif.debug")
-learner$param_set$values = list(message_train = 1, save_tasks = TRUE)
+learner$param_set$set_values(message_train = 1, save_tasks = TRUE)
 
 # this should signal a message
 task = tsk("penguins")

From 5c24ba8ac1990b7e336cc6a0c378fc54001700d9 Mon Sep 17 00:00:00 2001
From: Sebastian Fischer <sebf.fischer@gmail.com>
Date: Mon, 6 Jan 2025 17:04:59 +0100
Subject: [PATCH 07/10] Fix/predict newdata (#1240)

* ci: fail on note

* fix(predict): type conversion when predicting on new data

* ...

---------

Co-authored-by: Marc Becker <33069354+be-marc@users.noreply.github.com>
---
 NEWS.md                       |  4 +++-
 R/Learner.R                   | 11 +++++++++++
 R/assertions.R                |  2 +-
 man/Learner.Rd                |  4 +++-
 tests/testthat/test_Learner.R | 27 +++++++++++++++++++++------
 5 files changed, 39 insertions(+), 9 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 69e7292de..441f1976e 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,7 +1,9 @@
 # mlr3 (development version)
 
+* fix: the `$predict_newdata()` method of `Learner` now automatically conducts type conversions if the input is a `data.frame` (#685)
+* BREAKING_CHANGE: Predicting on a `task` with the wrong column information is now an error and not a warning. 
 * Column names with UTF-8 characters are now allowed by default.
-The option `mlr3.allow_utf8_names` is removed.
+  The option `mlr3.allow_utf8_names` is removed.
 * BREAKING CHANGE: `Learner$predict_types` is read-only now.
 * docs: Clear up behavior of `Learner$predict_type` after training.
 
diff --git a/R/Learner.R b/R/Learner.R
index 84fd7a075..de23f51e2 100644
--- a/R/Learner.R
+++ b/R/Learner.R
@@ -377,6 +377,8 @@ Learner = R6Class("Learner",
     #'   `data.frame()` or [DataBackend].
     #'   If a [DataBackend] is provided as `newdata`, the row ids are preserved,
     #'   otherwise they are set to to the sequence `1:nrow(newdata)`.
+    #'  If the input is a `data.frame`, [`auto_convert`] is used for type-conversions to ensure compatability
+    #'  of features between `$train()` and `$predict()`.
     #'
     #' @param task ([Task]).
     #'
@@ -393,6 +395,14 @@ Learner = R6Class("Learner",
         task = task_rm_backend(task)
       }
 
+      if (is.data.frame(newdata)) {
+        keep_cols = intersect(names(newdata), task$col_info$id)
+        ci = task$col_info[list(keep_cols), on = "id"]
+        newdata = do.call(data.table, Map(auto_convert,
+          value = as.list(newdata)[ci$id],
+          id = ci$id, type = ci$type, levels = ci$levels))
+      }
+
       newdata = as_data_backend(newdata)
       assert_names(newdata$colnames, must.include = task$feature_names)
 
@@ -409,6 +419,7 @@ Learner = R6Class("Learner",
 
       # do some type conversions if necessary
       task$backend = newdata
+      task$col_info = col_info(task$backend)
       task$row_roles$use = task$backend$rownames
       self$predict(task)
     },
diff --git a/R/assertions.R b/R/assertions.R
index d86b5ce43..e14494bf4 100644
--- a/R/assertions.R
+++ b/R/assertions.R
@@ -189,7 +189,7 @@ assert_predictable = function(task, learner) {
       all(pmap_lgl(list(x = ci_train$levels, y = ci_predict$levels), identical))
 
     if (!ok) {
-      lg$warn("Learner '%s' received task with different column info (feature type or level ordering) during train and predict.", learner$id)
+      stopf("Learner '%s' received task with different column info (feature type or factor level ordering) during train and predict.", learner$id)
     }
   }
 
diff --git a/man/Learner.Rd b/man/Learner.Rd
index 817222e0f..c57dcbf14 100644
--- a/man/Learner.Rd
+++ b/man/Learner.Rd
@@ -518,7 +518,9 @@ New data to predict on.
 All data formats convertible by \code{\link[=as_data_backend]{as_data_backend()}} are supported, e.g.
 \code{data.frame()} or \link{DataBackend}.
 If a \link{DataBackend} is provided as \code{newdata}, the row ids are preserved,
-otherwise they are set to to the sequence \code{1:nrow(newdata)}.}
+otherwise they are set to to the sequence \code{1:nrow(newdata)}.
+If the input is a \code{data.frame}, \code{\link{auto_convert}} is used for type-conversions to ensure compatability
+of features between \verb{$train()} and \verb{$predict()}.}
 
 \item{\code{task}}{(\link{Task}).}
 }
diff --git a/tests/testthat/test_Learner.R b/tests/testthat/test_Learner.R
index 8917f4452..901c6c271 100644
--- a/tests/testthat/test_Learner.R
+++ b/tests/testthat/test_Learner.R
@@ -255,13 +255,15 @@ test_that("learner cannot be trained with TuneToken present", {
 
 test_that("integer<->numeric conversion in newdata (#533)", {
   data = data.table(y = runif(10), x = 1:10)
-  newdata = data.table(y = runif(10), x = 1:10 + 0.1)
+  newdata1 = data.table(y = runif(10), x = as.double(1:10))
+  newdata2 = data.table(y = runif(10), x = 1:10 + 0.1)
 
   task = TaskRegr$new("test", data, "y")
   learner = lrn("regr.featureless")
   learner$train(task)
   expect_prediction(learner$predict_newdata(data))
-  expect_prediction(learner$predict_newdata(newdata))
+  expect_prediction(learner$predict_newdata(newdata1))
+  expect_error(learner$predict_newdata(newdata2), "failed to convert from class 'numeric'")
 })
 
 test_that("weights", {
@@ -575,10 +577,7 @@ test_that("column info is compared during predict", {
   task_other = as_task_classif(dother, target = "y")
   l = lrn("classif.rpart")
   l$train(task)
-  old_threshold = lg$threshold
-  lg$set_threshold("warn")
-  expect_output(l$predict(task_flip), "task with different column info")
-  lg$set_threshold(old_threshold)
+  expect_error(l$predict(task_flip), "task with different column info")
   expect_error(l$predict(task_other), "with different columns")
 })
 
@@ -663,3 +662,19 @@ test_that("configure method works", {
   expect_equal(learner$param_set$values$xval, 10)
   expect_equal(learner$predict_sets, "train")
 })
+
+test_that("predict_newdata auto conversion (#685)", {
+  l = lrn("classif.debug", save_tasks = TRUE)$train(tsk("iris")$select(c("Sepal.Length", "Sepal.Width")))
+  expect_error(l$predict_newdata(data.table(Sepal.Length = 1, Sepal.Width = "abc")),
+    "Incompatible types during auto-converting column 'Sepal.Width'", fixed = TRUE)
+  expect_error(l$predict_newdata(data.table(Sepal.Length = 1L)),
+    "but is missing elements")
+
+  # New test for integerish value conversion to double
+  p1 = l$predict_newdata(data.table(Sepal.Length = 1, Sepal.Width = 2))
+  p2 = l$predict_newdata(data.table(Sepal.Length = 1L, Sepal.Width = 2))
+  expect_equal(l$model$task_predict$col_info[list("Sepal.Length")]$type, "numeric")
+  expect_double(l$model$task_predict$data(cols = "Sepal.Length")[[1]])
+
+  expect_equal(p1, p2)
+})

From e21782a288f2dfbe38eb7034bdcf7321c133ef2d Mon Sep 17 00:00:00 2001
From: Sebastian Fischer <sebf.fischer@gmail.com>
Date: Mon, 6 Jan 2025 17:12:03 +0100
Subject: [PATCH 08/10] feat: improve docs for converters and better checks
 (#1231)

* feat: improve docs for converters and better checks

* ...

* fix failing tests
---
 NAMESPACE                           |  2 ++
 R/BenchmarkResult.R                 | 18 +++++++++++++++---
 R/Prediction.R                      | 12 ++++++++++--
 R/ResampleResult.R                  | 18 +++++++++++++++---
 R/as_learner.R                      |  1 +
 R/as_measure.R                      |  2 ++
 R/as_resampling.R                   |  3 ++-
 R/as_task.R                         |  8 ++++++++
 R/as_task_classif.R                 |  2 +-
 R/as_task_regr.R                    |  2 +-
 R/assertions.R                      | 26 ++++++++++++++++++++++++++
 man/as_resampling.Rd                |  1 +
 man/as_task.Rd                      |  2 ++
 man/as_task_classif.Rd              |  2 +-
 man/as_task_regr.Rd                 |  2 +-
 man/assert_empty_ellipsis.Rd        | 20 ++++++++++++++++++++
 tests/testthat/test_Learner.R       |  3 +--
 tests/testthat/test_as_learner.R    |  4 ++++
 tests/testthat/test_as_measure.R    |  4 ++++
 tests/testthat/test_as_resampling.R |  4 ++++
 tests/testthat/test_as_task.R       |  4 ++++
 tests/testthat/test_assertions.R    |  8 ++++++++
 22 files changed, 133 insertions(+), 15 deletions(-)
 create mode 100644 man/assert_empty_ellipsis.Rd
 create mode 100644 tests/testthat/test_assertions.R

diff --git a/NAMESPACE b/NAMESPACE
index 8946cdd5e..d6625ea4e 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -42,6 +42,7 @@ S3method(as_resampling,Resampling)
 S3method(as_resamplings,default)
 S3method(as_resamplings,list)
 S3method(as_task,Task)
+S3method(as_task,default)
 S3method(as_task_classif,DataBackend)
 S3method(as_task_classif,Matrix)
 S3method(as_task_classif,TaskClassif)
@@ -200,6 +201,7 @@ export(as_tasks)
 export(as_tasks_unsupervised)
 export(assert_backend)
 export(assert_benchmark_result)
+export(assert_empty_ellipsis)
 export(assert_learnable)
 export(assert_learner)
 export(assert_learners)
diff --git a/R/BenchmarkResult.R b/R/BenchmarkResult.R
index 9ee35c3ce..d3abea9bc 100644
--- a/R/BenchmarkResult.R
+++ b/R/BenchmarkResult.R
@@ -175,7 +175,11 @@ BenchmarkResult = R6Class("BenchmarkResult",
     #'
     #' @return [data.table::data.table()].
     score = function(measures = NULL, ids = TRUE, conditions = FALSE, predictions = TRUE) {
-      measures = as_measures(measures, task_type = self$task_type)
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       assert_flag(ids)
       assert_flag(conditions)
       assert_flag(predictions)
@@ -230,7 +234,11 @@ BenchmarkResult = R6Class("BenchmarkResult",
     #' @param predict_sets (`character()`)\cr
     #'   The predict sets.
     obs_loss = function(measures = NULL, predict_sets = "test") {
-      measures = as_measures(measures, task_type = private$.data$task_type)
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       map_dtr(self$resample_results$resample_result,
         function(rr) {
           rr$obs_loss(measures, predict_sets)
@@ -276,7 +284,11 @@ BenchmarkResult = R6Class("BenchmarkResult",
     #'
     #' @return [data.table::data.table()].
     aggregate = function(measures = NULL, ids = TRUE, uhashes = FALSE, params = FALSE, conditions = FALSE) {
-      measures = assert_measures(as_measures(measures, task_type = self$task_type))
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       assert_flag(ids)
       assert_flag(uhashes)
       assert_flag(params)
diff --git a/R/Prediction.R b/R/Prediction.R
index 243397ea6..ad7a0c8ce 100644
--- a/R/Prediction.R
+++ b/R/Prediction.R
@@ -90,7 +90,11 @@ Prediction = R6Class("Prediction",
     #'
     #' @return [Prediction].
     score = function(measures = NULL, task = NULL, learner = NULL, train_set = NULL) {
-      measures = as_measures(measures, task_type = self$task_type)
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       scores = map_dbl(measures, function(m) m$score(prediction = self, task = task, learner = learner, train_set = train_set))
       set_names(scores, ids(measures))
     },
@@ -105,7 +109,11 @@ Prediction = R6Class("Prediction",
     #' Note that some measures such as RMSE, do have an `$obs_loss`, but they require an
     #' additional transformation after aggregation, in this example taking the square-root.
     obs_loss = function(measures = NULL) {
-      measures = as_measures(measures, task_type = self$task_type)
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       get_obs_loss(as.data.table(self), measures)
     },
 
diff --git a/R/ResampleResult.R b/R/ResampleResult.R
index 83b79ada3..895696d54 100644
--- a/R/ResampleResult.R
+++ b/R/ResampleResult.R
@@ -143,7 +143,11 @@ ResampleResult = R6Class("ResampleResult",
     #'
     #' @return [data.table::data.table()].
     score = function(measures = NULL, ids = TRUE, conditions = FALSE, predictions = TRUE) {
-      measures = as_measures(measures, task_type = private$.data$task_type)
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       assert_flag(ids)
       assert_flag(conditions)
       assert_flag(predictions)
@@ -196,7 +200,11 @@ ResampleResult = R6Class("ResampleResult",
     #' @param predict_sets (`character()`)\cr
     #'   The predict sets.
     obs_loss = function(measures = NULL, predict_sets = "test") {
-      measures = as_measures(measures, task_type = self$task_type)
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       tab = map_dtr(self$predictions(predict_sets), as.data.table, .idcol = "iteration")
       get_obs_loss(tab, measures)
     },
@@ -208,7 +216,11 @@ ResampleResult = R6Class("ResampleResult",
     #'
     #' @return Named `numeric()`.
     aggregate = function(measures = NULL) {
-      measures = as_measures(measures, task_type = private$.data$task_type)
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       resample_result_aggregate(self, measures)
     },
 
diff --git a/R/as_learner.R b/R/as_learner.R
index 4c303f511..3ec806845 100644
--- a/R/as_learner.R
+++ b/R/as_learner.R
@@ -16,6 +16,7 @@ as_learner = function(x, ...) { # nolint
 #'   Whether to discard the state.
 #' @rdname as_learner
 as_learner.Learner = function(x, clone = FALSE, discard_state = FALSE, ...) { # nolint
+  assert_empty_ellipsis(...)
   if (isTRUE(clone) && isTRUE(discard_state)) {
     clone_without(x, "state")
   } else if (isTRUE(clone)) {
diff --git a/R/as_measure.R b/R/as_measure.R
index d266d8f27..97f2798ef 100644
--- a/R/as_measure.R
+++ b/R/as_measure.R
@@ -17,12 +17,14 @@ as_measure = function(x, ...) { # nolint
 #' @export
 #' @rdname as_measure
 as_measure.NULL = function(x, task_type = NULL, ...) { # nolint
+  assert_empty_ellipsis(...)
   default_measures(task_type)[[1L]]
 }
 
 #' @export
 #' @rdname as_measure
 as_measure.Measure = function(x, clone = FALSE, ...) { # nolint
+  assert_empty_ellipsis(...)
   if (isTRUE(clone)) x$clone() else x
 }
 
diff --git a/R/as_resampling.R b/R/as_resampling.R
index 03c4e6cba..60e2453e6 100644
--- a/R/as_resampling.R
+++ b/R/as_resampling.R
@@ -2,7 +2,7 @@
 #'
 #' @description
 #' Convert object to a [Resampling] or a list of [Resampling].
-#'
+#' This method e.g. allows to convert an [`mlr3oml::OMLTask`] to a [`Resampling`].
 #' @inheritParams as_task
 #' @export
 as_resampling = function(x, ...) { # nolint
@@ -12,6 +12,7 @@ as_resampling = function(x, ...) { # nolint
 #' @export
 #' @rdname as_resampling
 as_resampling.Resampling = function(x, clone = FALSE, ...) { # nolint
+  assert_empty_ellipsis(...)
   if (isTRUE(clone)) x$clone() else x
 }
 
diff --git a/R/as_task.R b/R/as_task.R
index a6841dc39..1f7017f70 100644
--- a/R/as_task.R
+++ b/R/as_task.R
@@ -2,6 +2,8 @@
 #'
 #' @description
 #' Convert object to a [Task] or a list of [Task].
+#' This method e.g. allows to convert an [`mlr3oml::OMLTask`] to a [`Task`] and additionally supports cloning.
+#' In order to construct a [Task] from a `data.frame`, use task-specific converters such as [`as_task_classif()`] or [`as_task_regr()`].
 #'
 #' @param x (any)\cr
 #'   Object to convert.
@@ -12,11 +14,17 @@ as_task = function(x, ...) {
   UseMethod("as_task")
 }
 
+#' @export
+as_task.default = function(x, ...) {
+  stopf("No method for class '%s'. To create a task from a `data.frame`, use dedicated converters such as `as_task_classif()` or `as_task_regr()`.", class(x)[1L])
+}
+
 #' @rdname as_task
 #' @param clone (`logical(1)`)\cr
 #'   If `TRUE`, ensures that the returned object is not the same as the input `x`.
 #' @export
 as_task.Task = function(x, clone = FALSE, ...) { # nolint
+  assert_empty_ellipsis(...)
   if (isTRUE(clone)) x$clone(deep = TRUE) else x
 }
 
diff --git a/R/as_task_classif.R b/R/as_task_classif.R
index cf75a0396..4f1c39bbd 100644
--- a/R/as_task_classif.R
+++ b/R/as_task_classif.R
@@ -4,7 +4,7 @@
 #' Convert object to a [TaskClassif].
 #' This is a S3 generic. mlr3 ships with methods for the following objects:
 #'
-#' 1. [TaskClassif]: ensure the identity
+#' 1. [TaskClassif]: returns the object as-is, possibly cloned.
 #' 2. [`formula`], [data.frame()], [matrix()], [Matrix::Matrix()] and [DataBackend]: provides an alternative to the constructor of [TaskClassif].
 #' 3. [TaskRegr]: Calls [convert_task()].
 #'
diff --git a/R/as_task_regr.R b/R/as_task_regr.R
index ce4f90d1a..ad1e682e9 100644
--- a/R/as_task_regr.R
+++ b/R/as_task_regr.R
@@ -4,7 +4,7 @@
 #' Convert object to a [TaskRegr].
 #' This is a S3 generic. mlr3 ships with methods for the following objects:
 #'
-#' 1. [TaskRegr]: ensure the identity
+#' 1. [TaskRegr]: returns the object as-is, possibly cloned.
 #' 2. [`formula`], [data.frame()], [matrix()], [Matrix::Matrix()] and [DataBackend]: provides an alternative to the constructor of [TaskRegr].
 #' 3. [TaskClassif]: Calls [convert_task()].
 #'
diff --git a/R/assertions.R b/R/assertions.R
index e14494bf4..bd1a529ba 100644
--- a/R/assertions.R
+++ b/R/assertions.R
@@ -405,3 +405,29 @@ assert_param_values = function(x, n_learners = NULL, .var.name = vname(x)) {
   }
   invisible(x)
 }
+
+#' @title Assert Empty Ellipsis
+#' @description
+#' Assert that `...` arguments are empty.
+#' Use this function in S3-methods to ensure that misspelling of arguments does not go unnoticed.
+#' @param ... (any)\cr
+#'    Ellipsis arguments to check.
+#' @keywords internal
+#' @return `NULL`
+#' @export
+assert_empty_ellipsis = function(...) {
+  if (...length()) {
+    names = ...names()
+    if (is.null(names)) {
+      stopf("Received %i unnamed argument that was not used.", ...length())
+    } else {
+      names2 = names[names != ""]
+      if (length(names2) == length(names)) {
+        stopf("Received the following named arguments that were unused: %s.", paste0(names2, collapse = ", "))
+      } else {
+        stopf("Received unused arguments: %i unnamed, as well as named arguments %s.", length(names) - length(names2), paste0(names2, collapse = ", "))
+      }
+    }
+  }
+  NULL
+}
diff --git a/man/as_resampling.Rd b/man/as_resampling.Rd
index 846ea9ac3..02784a7ec 100644
--- a/man/as_resampling.Rd
+++ b/man/as_resampling.Rd
@@ -30,4 +30,5 @@ If \code{TRUE}, ensures that the returned object is not the same as the input \c
 }
 \description{
 Convert object to a \link{Resampling} or a list of \link{Resampling}.
+This method e.g. allows to convert an \code{\link[mlr3oml:oml_task]{mlr3oml::OMLTask}} to a \code{\link{Resampling}}.
 }
diff --git a/man/as_task.Rd b/man/as_task.Rd
index eba52ac8a..8aff6071e 100644
--- a/man/as_task.Rd
+++ b/man/as_task.Rd
@@ -30,4 +30,6 @@ If \code{TRUE}, ensures that the returned object is not the same as the input \c
 }
 \description{
 Convert object to a \link{Task} or a list of \link{Task}.
+This method e.g. allows to convert an \code{\link[mlr3oml:oml_task]{mlr3oml::OMLTask}} to a \code{\link{Task}} and additionally supports cloning.
+In order to construct a \link{Task} from a \code{data.frame}, use task-specific converters such as \code{\link[=as_task_classif]{as_task_classif()}} or \code{\link[=as_task_regr]{as_task_regr()}}.
 }
diff --git a/man/as_task_classif.Rd b/man/as_task_classif.Rd
index 823cffaa8..4d633963c 100644
--- a/man/as_task_classif.Rd
+++ b/man/as_task_classif.Rd
@@ -106,7 +106,7 @@ Data frame containing all columns referenced in formula \code{x}.}
 Convert object to a \link{TaskClassif}.
 This is a S3 generic. mlr3 ships with methods for the following objects:
 \enumerate{
-\item \link{TaskClassif}: ensure the identity
+\item \link{TaskClassif}: returns the object as-is, possibly cloned.
 \item \code{\link{formula}}, \code{\link[=data.frame]{data.frame()}}, \code{\link[=matrix]{matrix()}}, \code{\link[Matrix:Matrix]{Matrix::Matrix()}} and \link{DataBackend}: provides an alternative to the constructor of \link{TaskClassif}.
 \item \link{TaskRegr}: Calls \code{\link[=convert_task]{convert_task()}}.
 }
diff --git a/man/as_task_regr.Rd b/man/as_task_regr.Rd
index 35e57e84d..f2b77330c 100644
--- a/man/as_task_regr.Rd
+++ b/man/as_task_regr.Rd
@@ -100,7 +100,7 @@ Data frame containing all columns referenced in formula \code{x}.}
 Convert object to a \link{TaskRegr}.
 This is a S3 generic. mlr3 ships with methods for the following objects:
 \enumerate{
-\item \link{TaskRegr}: ensure the identity
+\item \link{TaskRegr}: returns the object as-is, possibly cloned.
 \item \code{\link{formula}}, \code{\link[=data.frame]{data.frame()}}, \code{\link[=matrix]{matrix()}}, \code{\link[Matrix:Matrix]{Matrix::Matrix()}} and \link{DataBackend}: provides an alternative to the constructor of \link{TaskRegr}.
 \item \link{TaskClassif}: Calls \code{\link[=convert_task]{convert_task()}}.
 }
diff --git a/man/assert_empty_ellipsis.Rd b/man/assert_empty_ellipsis.Rd
new file mode 100644
index 000000000..21568d8b8
--- /dev/null
+++ b/man/assert_empty_ellipsis.Rd
@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/assertions.R
+\name{assert_empty_ellipsis}
+\alias{assert_empty_ellipsis}
+\title{Assert Empty Ellipsis}
+\usage{
+assert_empty_ellipsis(...)
+}
+\arguments{
+\item{...}{(any)\cr
+Ellipsis arguments to check.}
+}
+\value{
+\code{NULL}
+}
+\description{
+Assert that \code{...} arguments are empty.
+Use this function in S3-methods to ensure that misspelling of arguments does not go unnoticed.
+}
+\keyword{internal}
diff --git a/tests/testthat/test_Learner.R b/tests/testthat/test_Learner.R
index 901c6c271..5ed9d803a 100644
--- a/tests/testthat/test_Learner.R
+++ b/tests/testthat/test_Learner.R
@@ -551,8 +551,7 @@ test_that("learner state contains internal valid task information", {
 test_that("validation task with 0 observations", {
   learner = lrn("classif.debug", validate = "predefined")
   task = tsk("iris")
-  task$internal_valid_task = integer(0)
-  expect_error({learner$train(task)}, "has 0 observations")
+  expect_warning({task$internal_valid_task = integer(0)})
 })
 
 test_that("column info is compared during predict", {
diff --git a/tests/testthat/test_as_learner.R b/tests/testthat/test_as_learner.R
index 3d34685a1..59cdc865a 100644
--- a/tests/testthat/test_as_learner.R
+++ b/tests/testthat/test_as_learner.R
@@ -21,3 +21,7 @@ test_that("discard_state", {
   as_learner(learner3, clone = FALSE, discard_state = TRUE)
   expect_null(learner3$state)
 })
+
+test_that("error when arguments are misspelled", {
+  expect_error(as_learner(lrn("classif.rpart"), clone2 = TRUE), "Received the following")
+})
diff --git a/tests/testthat/test_as_measure.R b/tests/testthat/test_as_measure.R
index 647774fed..584962d66 100644
--- a/tests/testthat/test_as_measure.R
+++ b/tests/testthat/test_as_measure.R
@@ -14,3 +14,7 @@ test_that("as_measure conversion", {
   default = as_measures(NULL, task_type = "classif")
   expect_list(default, types = "Measure")
 })
+
+test_that("error when arguments are misspelled", {
+  expect_error(as_measure(msr("classif.acc"), clone2 = TRUE), "Received the following")
+})
diff --git a/tests/testthat/test_as_resampling.R b/tests/testthat/test_as_resampling.R
index 40143a0c4..0718a7ff0 100644
--- a/tests/testthat/test_as_resampling.R
+++ b/tests/testthat/test_as_resampling.R
@@ -10,3 +10,7 @@ test_that("as_resampling conversion", {
   expect_list(as_resamplings(resampling), types = "Resampling")
   expect_list(as_resamplings(list(resampling)), types = "Resampling")
 })
+
+test_that("error when arguments are misspelled", {
+  expect_error(as_resampling(rsmp("holdout"), clone2 = TRUE), "Received the following")
+})
diff --git a/tests/testthat/test_as_task.R b/tests/testthat/test_as_task.R
index 13b00ed82..9d763f221 100644
--- a/tests/testthat/test_as_task.R
+++ b/tests/testthat/test_as_task.R
@@ -22,3 +22,7 @@ test_that("as_task_xx error messages (#944)", {
     "subset of"
   )
 })
+
+test_that("error when arguments are misspelled", {
+  expect_error(as_task(tsk("iris"), clone2 = TRUE), "Received the following")
+})
diff --git a/tests/testthat/test_assertions.R b/tests/testthat/test_assertions.R
new file mode 100644
index 000000000..1c77d92b9
--- /dev/null
+++ b/tests/testthat/test_assertions.R
@@ -0,0 +1,8 @@
+test_that("assert_empty_ellipsis works", {
+  expect_error(assert_empty_ellipsis(1), "Received 1 unnamed argument")
+  expect_error(assert_empty_ellipsis(1, 2), "Received 2 unnamed argument")
+  expect_error(assert_empty_ellipsis(a = 1), "that were unused: a")
+  expect_error(assert_empty_ellipsis(a = 1, b = 2), "that were unused: a, b")
+  expect_error(assert_empty_ellipsis(a = 1, b = 1, 2), "1 unnamed, as well as named arguments a, b")
+  expect_null(assert_empty_ellipsis())
+})

From 694e21c4cf97c22e2f2ddc7ae16441917ba32f78 Mon Sep 17 00:00:00 2001
From: Sebastian Fischer <sebf.fischer@gmail.com>
Date: Thu, 9 Jan 2025 09:51:29 +0100
Subject: [PATCH 09/10] fix: as_measures (#1242)

---
 R/BenchmarkResult.R                     | 12 ++----------
 R/Prediction.R                          | 12 ++----------
 R/ResampleResult.R                      | 18 +++---------------
 R/as_measure.R                          | 14 +++++++-------
 man/as_measure.Rd                       | 16 ++++++++--------
 man/mlr_learners_classif.featureless.Rd |  2 +-
 man/mlr_learners_regr.featureless.Rd    |  2 +-
 7 files changed, 24 insertions(+), 52 deletions(-)

diff --git a/R/BenchmarkResult.R b/R/BenchmarkResult.R
index d3abea9bc..1ebd980a0 100644
--- a/R/BenchmarkResult.R
+++ b/R/BenchmarkResult.R
@@ -175,11 +175,7 @@ BenchmarkResult = R6Class("BenchmarkResult",
     #'
     #' @return [data.table::data.table()].
     score = function(measures = NULL, ids = TRUE, conditions = FALSE, predictions = TRUE) {
-      measures = if (is.null(measures)) {
-        default_measures(self$task_type)
-      } else {
-        assert_measures(as_measures(measures))
-      }
+      measures = assert_measures(as_measures(measures, task_type = self$task_type))
       assert_flag(ids)
       assert_flag(conditions)
       assert_flag(predictions)
@@ -234,11 +230,7 @@ BenchmarkResult = R6Class("BenchmarkResult",
     #' @param predict_sets (`character()`)\cr
     #'   The predict sets.
     obs_loss = function(measures = NULL, predict_sets = "test") {
-      measures = if (is.null(measures)) {
-        default_measures(self$task_type)
-      } else {
-        assert_measures(as_measures(measures))
-      }
+      measures = assert_measures(as_measures(measures, task_type = self$task_type))
       map_dtr(self$resample_results$resample_result,
         function(rr) {
           rr$obs_loss(measures, predict_sets)
diff --git a/R/Prediction.R b/R/Prediction.R
index ad7a0c8ce..c2000e5ec 100644
--- a/R/Prediction.R
+++ b/R/Prediction.R
@@ -90,11 +90,7 @@ Prediction = R6Class("Prediction",
     #'
     #' @return [Prediction].
     score = function(measures = NULL, task = NULL, learner = NULL, train_set = NULL) {
-      measures = if (is.null(measures)) {
-        default_measures(self$task_type)
-      } else {
-        assert_measures(as_measures(measures))
-      }
+      measures = assert_measures(as_measures(measures, task_type = self$task_type))
       scores = map_dbl(measures, function(m) m$score(prediction = self, task = task, learner = learner, train_set = train_set))
       set_names(scores, ids(measures))
     },
@@ -109,11 +105,7 @@ Prediction = R6Class("Prediction",
     #' Note that some measures such as RMSE, do have an `$obs_loss`, but they require an
     #' additional transformation after aggregation, in this example taking the square-root.
     obs_loss = function(measures = NULL) {
-      measures = if (is.null(measures)) {
-        default_measures(self$task_type)
-      } else {
-        assert_measures(as_measures(measures))
-      }
+      measures = assert_measures(as_measures(measures, task_type = self$task_type))
       get_obs_loss(as.data.table(self), measures)
     },
 
diff --git a/R/ResampleResult.R b/R/ResampleResult.R
index 895696d54..53da71cdc 100644
--- a/R/ResampleResult.R
+++ b/R/ResampleResult.R
@@ -143,11 +143,7 @@ ResampleResult = R6Class("ResampleResult",
     #'
     #' @return [data.table::data.table()].
     score = function(measures = NULL, ids = TRUE, conditions = FALSE, predictions = TRUE) {
-      measures = if (is.null(measures)) {
-        default_measures(self$task_type)
-      } else {
-        assert_measures(as_measures(measures))
-      }
+      measures = assert_measures(as_measures(measures, task_type = self$task_type))
       assert_flag(ids)
       assert_flag(conditions)
       assert_flag(predictions)
@@ -200,11 +196,7 @@ ResampleResult = R6Class("ResampleResult",
     #' @param predict_sets (`character()`)\cr
     #'   The predict sets.
     obs_loss = function(measures = NULL, predict_sets = "test") {
-      measures = if (is.null(measures)) {
-        default_measures(self$task_type)
-      } else {
-        assert_measures(as_measures(measures))
-      }
+      measures = assert_measures(as_measures(measures, task_type = self$task_type))
       tab = map_dtr(self$predictions(predict_sets), as.data.table, .idcol = "iteration")
       get_obs_loss(tab, measures)
     },
@@ -216,11 +208,7 @@ ResampleResult = R6Class("ResampleResult",
     #'
     #' @return Named `numeric()`.
     aggregate = function(measures = NULL) {
-      measures = if (is.null(measures)) {
-        default_measures(self$task_type)
-      } else {
-        assert_measures(as_measures(measures))
-      }
+      measures = assert_measures(as_measures(measures, task_type = self$task_type))
       resample_result_aggregate(self, measures)
     },
 
diff --git a/R/as_measure.R b/R/as_measure.R
index 97f2798ef..bc63c32ab 100644
--- a/R/as_measure.R
+++ b/R/as_measure.R
@@ -10,7 +10,7 @@
 #'
 #' @return [Measure].
 #' @export
-as_measure = function(x, ...) { # nolint
+as_measure = function(x, task_type = NULL, ...) { # nolint
   UseMethod("as_measure")
 }
 
@@ -23,21 +23,21 @@ as_measure.NULL = function(x, task_type = NULL, ...) { # nolint
 
 #' @export
 #' @rdname as_measure
-as_measure.Measure = function(x, clone = FALSE, ...) { # nolint
+as_measure.Measure = function(x, task_type = NULL, clone = FALSE, ...) { # nolint
   assert_empty_ellipsis(...)
   if (isTRUE(clone)) x$clone() else x
 }
 
 #' @export
 #' @rdname as_measure
-as_measures = function(x, ...) { # nolint
+as_measures = function(x, task_type = NULL, ...) { # nolint
   UseMethod("as_measures")
 }
 
 #' @export
 #' @rdname as_measure
-as_measures.default = function(x, ...) { # nolint
-  list(as_measure(x, ...))
+as_measures.default = function(x, task_type = NULL, ...) { # nolint
+  list(as_measure(x, task_type = task_type, ...))
 }
 
 #' @export
@@ -48,6 +48,6 @@ as_measures.NULL = function(x, task_type = NULL, ...) { # nolint
 
 #' @export
 #' @rdname as_measure
-as_measures.list = function(x, ...) { # nolint
-  lapply(x, as_measure, ...)
+as_measures.list = function(x, task_type = NULL, ...) { # nolint
+  lapply(x, as_measure, task_type = NULL, ...)
 }
diff --git a/man/as_measure.Rd b/man/as_measure.Rd
index 79d52a565..ad17bc4a8 100644
--- a/man/as_measure.Rd
+++ b/man/as_measure.Rd
@@ -10,31 +10,31 @@
 \alias{as_measures.list}
 \title{Convert to a Measure}
 \usage{
-as_measure(x, ...)
+as_measure(x, task_type = NULL, ...)
 
 \method{as_measure}{`NULL`}(x, task_type = NULL, ...)
 
-\method{as_measure}{Measure}(x, clone = FALSE, ...)
+\method{as_measure}{Measure}(x, task_type = NULL, clone = FALSE, ...)
 
-as_measures(x, ...)
+as_measures(x, task_type = NULL, ...)
 
-\method{as_measures}{default}(x, ...)
+\method{as_measures}{default}(x, task_type = NULL, ...)
 
 \method{as_measures}{`NULL`}(x, task_type = NULL, ...)
 
-\method{as_measures}{list}(x, ...)
+\method{as_measures}{list}(x, task_type = NULL, ...)
 }
 \arguments{
 \item{x}{(any)\cr
 Object to convert.}
 
-\item{...}{(any)\cr
-Additional arguments.}
-
 \item{task_type}{(\code{character(1)})\cr
 Used if \code{x} is \code{NULL} to construct a default measure for the respective task type.
 The default measures are stored in \code{\link[=mlr_reflections]{mlr_reflections$default_measures}}.}
 
+\item{...}{(any)\cr
+Additional arguments.}
+
 \item{clone}{(\code{logical(1)})\cr
 If \code{TRUE}, ensures that the returned object is not the same as the input \code{x}.}
 }
diff --git a/man/mlr_learners_classif.featureless.Rd b/man/mlr_learners_classif.featureless.Rd
index cf9724c9a..e701c7369 100644
--- a/man/mlr_learners_classif.featureless.Rd
+++ b/man/mlr_learners_classif.featureless.Rd
@@ -36,7 +36,7 @@ lrn("classif.featureless")
 \itemize{
 \item Task type: \dQuote{classif}
 \item Predict Types: \dQuote{response}, \dQuote{prob}
-\item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{character}, \dQuote{factor}, \dQuote{ordered}, \dQuote{POSIXct}
+\item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{character}, \dQuote{factor}, \dQuote{ordered}, \dQuote{POSIXct}, \dQuote{Date}
 \item Required Packages: \CRANpkg{mlr3}
 }
 }
diff --git a/man/mlr_learners_regr.featureless.Rd b/man/mlr_learners_regr.featureless.Rd
index 4cd8de85e..64a69f4cb 100644
--- a/man/mlr_learners_regr.featureless.Rd
+++ b/man/mlr_learners_regr.featureless.Rd
@@ -25,7 +25,7 @@ lrn("regr.featureless")
 \itemize{
 \item Task type: \dQuote{regr}
 \item Predict Types: \dQuote{response}, \dQuote{se}, \dQuote{quantiles}
-\item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{character}, \dQuote{factor}, \dQuote{ordered}, \dQuote{POSIXct}
+\item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{character}, \dQuote{factor}, \dQuote{ordered}, \dQuote{POSIXct}, \dQuote{Date}
 \item Required Packages: \CRANpkg{mlr3}, 'stats'
 }
 }

From a54679e30ce533c5f5bf6af23c3333d59eca6196 Mon Sep 17 00:00:00 2001
From: Sebastian Fischer <sebf.fischer@gmail.com>
Date: Fri, 10 Jan 2025 10:14:27 +0100
Subject: [PATCH 10/10] fix(learner): column info and type conversion in
 predict_newdata (#1243)

---
 NEWS.md                       |  2 +-
 R/Learner.R                   | 33 +++++++++++++++++------------
 man/Learner.Rd                |  6 +++---
 tests/testthat/test_Learner.R | 39 +++++++++++++++++++++++++++++++++++
 4 files changed, 63 insertions(+), 17 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 441f1976e..89892f152 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,6 +1,6 @@
 # mlr3 (development version)
 
-* fix: the `$predict_newdata()` method of `Learner` now automatically conducts type conversions if the input is a `data.frame` (#685)
+* fix: the `$predict_newdata()` method of `Learner` now automatically conducts type conversions (#685)
 * BREAKING_CHANGE: Predicting on a `task` with the wrong column information is now an error and not a warning. 
 * Column names with UTF-8 characters are now allowed by default.
   The option `mlr3.allow_utf8_names` is removed.
diff --git a/R/Learner.R b/R/Learner.R
index de23f51e2..7e9657085 100644
--- a/R/Learner.R
+++ b/R/Learner.R
@@ -370,6 +370,8 @@ Learner = R6Class("Learner",
     #' of the training task stored in the learner.
     #' If the learner has been fitted via [resample()] or [benchmark()], you need to pass the corresponding task stored
     #' in the [ResampleResult] or [BenchmarkResult], respectively.
+    #' Further, [`auto_convert`] is used for type-conversions to ensure compatability
+    #' of features between `$train()` and `$predict()`.
     #'
     #' @param newdata (any object supported by [as_data_backend()])\cr
     #'   New data to predict on.
@@ -377,8 +379,6 @@ Learner = R6Class("Learner",
     #'   `data.frame()` or [DataBackend].
     #'   If a [DataBackend] is provided as `newdata`, the row ids are preserved,
     #'   otherwise they are set to to the sequence `1:nrow(newdata)`.
-    #'  If the input is a `data.frame`, [`auto_convert`] is used for type-conversions to ensure compatability
-    #'  of features between `$train()` and `$predict()`.
     #'
     #' @param task ([Task]).
     #'
@@ -395,31 +395,38 @@ Learner = R6Class("Learner",
         task = task_rm_backend(task)
       }
 
-      if (is.data.frame(newdata)) {
-        keep_cols = intersect(names(newdata), task$col_info$id)
-        ci = task$col_info[list(keep_cols), on = "id"]
-        newdata = do.call(data.table, Map(auto_convert,
-          value = as.list(newdata)[ci$id],
-          id = ci$id, type = ci$type, levels = ci$levels))
-      }
-
       newdata = as_data_backend(newdata)
       assert_names(newdata$colnames, must.include = task$feature_names)
 
       # the following columns are automatically set to NA if missing
       impute = unlist(task$col_roles[c("target", "name", "order", "stratum", "group", "weight")], use.names = FALSE)
       impute = setdiff(impute, newdata$colnames)
-      if (length(impute)) {
+      tab1 = if (length(impute)) {
         # create list with correct NA types and cbind it to the backend
         ci = insert_named(task$col_info[list(impute), c("id", "type", "levels"), on = "id", with = FALSE], list(value = NA))
         na_cols = set_names(pmap(ci, function(..., nrow) rep(auto_convert(...), nrow), nrow = newdata$nrow), ci$id)
-        tab = invoke(data.table, .args = insert_named(na_cols, set_names(list(newdata$rownames), newdata$primary_key)))
+        invoke(data.table, .args = insert_named(na_cols, set_names(list(newdata$rownames), newdata$primary_key)))
+      }
+
+      # Perform type conversion where necessary
+      keep_cols = intersect(newdata$colnames, task$col_info$id)
+      ci = task$col_info[list(keep_cols), ][
+        get("type") != col_info(newdata)[list(keep_cols), on = "id"]$type]
+      tab2 = do.call(data.table, Map(auto_convert,
+        value = as.list(newdata$data(rows = newdata$rownames, cols = ci$id)),
+        id = ci$id, type = ci$type, levels = ci$levels))
+
+      tab = cbind(tab1, tab2)
+      if (ncol(tab)) {
+        tab[[newdata$primary_key]] = newdata$rownames
         newdata = DataBackendCbind$new(newdata, DataBackendDataTable$new(tab, primary_key = newdata$primary_key))
       }
 
-      # do some type conversions if necessary
+      prevci = task$col_info
       task$backend = newdata
       task$col_info = col_info(task$backend)
+      task$col_info[, c("label", "fix_factor_levels")] = prevci[list(task$col_info$id), on = "id", c("label", "fix_factor_levels")]
+      task$col_info$fix_factor_levels[is.na(task$col_info$fix_factor_levels)] = FALSE
       task$row_roles$use = task$backend$rownames
       self$predict(task)
     },
diff --git a/man/Learner.Rd b/man/Learner.Rd
index c57dcbf14..ca54c1a11 100644
--- a/man/Learner.Rd
+++ b/man/Learner.Rd
@@ -506,6 +506,8 @@ If the learner's \verb{$train()} method has been called, there is a (size reduce
 of the training task stored in the learner.
 If the learner has been fitted via \code{\link[=resample]{resample()}} or \code{\link[=benchmark]{benchmark()}}, you need to pass the corresponding task stored
 in the \link{ResampleResult} or \link{BenchmarkResult}, respectively.
+Further, \code{\link{auto_convert}} is used for type-conversions to ensure compatability
+of features between \verb{$train()} and \verb{$predict()}.
 \subsection{Usage}{
 \if{html}{\out{<div class="r">}}\preformatted{Learner$predict_newdata(newdata, task = NULL)}\if{html}{\out{</div>}}
 }
@@ -518,9 +520,7 @@ New data to predict on.
 All data formats convertible by \code{\link[=as_data_backend]{as_data_backend()}} are supported, e.g.
 \code{data.frame()} or \link{DataBackend}.
 If a \link{DataBackend} is provided as \code{newdata}, the row ids are preserved,
-otherwise they are set to to the sequence \code{1:nrow(newdata)}.
-If the input is a \code{data.frame}, \code{\link{auto_convert}} is used for type-conversions to ensure compatability
-of features between \verb{$train()} and \verb{$predict()}.}
+otherwise they are set to to the sequence \code{1:nrow(newdata)}.}
 
 \item{\code{task}}{(\link{Task}).}
 }
diff --git a/tests/testthat/test_Learner.R b/tests/testthat/test_Learner.R
index 5ed9d803a..086a06298 100644
--- a/tests/testthat/test_Learner.R
+++ b/tests/testthat/test_Learner.R
@@ -677,3 +677,42 @@ test_that("predict_newdata auto conversion (#685)", {
 
   expect_equal(p1, p2)
 })
+
+test_that("predict_newdata creates column info correctly", {
+
+  learner = lrn("classif.debug", save_tasks = TRUE)
+  task = tsk("iris")
+  task$col_info$label = letters[1:6]
+  task$col_info$fix_factor_levels = c(TRUE, TRUE, FALSE, TRUE, FALSE, TRUE)
+  learner$train(task)
+
+  ## data.frame is passed without task
+  p1 = learner$predict_newdata(iris[10:11, ])
+  expect_equal(learner$model$task_predict$col_info, task$col_info)
+  expect_equal(p1$row_ids, 1:2)
+
+  ## backend is passed without task
+  d = iris[10:11, ]
+  d$..row_id = 10:11
+  b = as_data_backend(d, primary_key = "..row_id")
+  p2 = learner$predict_newdata(b)
+  expect_equal(p2$row_ids, 10:11)
+  expect_equal(learner$model$task_predict$col_info, task$col_info)
+
+  ## data.frame is passed with task
+  task2 = tsk("iris")
+  learner$predict_newdata(iris[10:11, ], task2)
+  expect_equal(learner$model$task_predict$col_info, task2$col_info)
+
+  ## backend is passed with task
+  learner$predict_newdata(b, task2)
+  expect_equal(learner$model$task_predict$col_info, task2$col_info)
+
+  ## backend with different name for primary key
+  d2 = iris[10:11, ]
+  d2$row_id = 10:11
+  b2 = as_data_backend(d2, primary_key = "row_id")
+  p3 = learner$predict_newdata(b2, task2)
+  expect_equal(p3$row_ids, 10:11)
+  expect_true("row_id" %in% learner$model$task_predict$col_info$id)
+})