fix: no suggest examples

.Rbuildignore

@@ -18,3 +18,4 @@
 ^revdep$
 ^cran-comments\.md$
 ^CRAN-SUBMISSION$
+^.claude$

.github/workflows/no-suggest-cmd-check.yml

@@ -1,4 +1,4 @@
-# r cmd check workflow without suggests of the mlr3 ecosystem v0.2.0
+# r cmd check workflow without suggests of the mlr3 ecosystem v0.3.1
 # https://github.com/mlr-org/actions
 on:
   workflow_dispatch:
@@ -33,7 +33,7 @@ jobs:
           - {os: ubuntu-latest,   r: 'release'}
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v5
 
       - uses: r-lib/actions/setup-pandoc@v2
 
@@ -43,9 +43,14 @@ jobs:
 
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::rcmdcheck
+          extra-packages: |
+            any::rcmdcheck
+            any::testthat
+            any::knitr
+            any::rmarkdown
           needs: check
           dependencies: '"hard"'
+          cache: false
 
       - uses: mxschmitt/action-tmate@v3
         if: ${{ github.event_name == 'workflow_dispatch' && inputs.debug_enabled }}
@@ -54,5 +59,4 @@ jobs:
 
       - uses: r-lib/actions/check-r-package@v2
         with:
-          error-on: '"note"'
           args: 'c("--no-manual", "--as-cran")'

DESCRIPTION

@@ -26,7 +26,8 @@ License: LGPL-3
 URL: https://mlr3viz.mlr-org.com, https://github.com/mlr-org/mlr3viz
 BugReports: https://github.com/mlr-org/mlr3viz/issues
 Depends:
-    R (>= 3.3.0)
+    R (>= 3.3.0),
+    mlr3 (>= 1.3.0)
 Imports:
     checkmate,
     data.table,
@@ -37,6 +38,7 @@ Imports:
     viridis
 Suggests:
     bbotk (>= 1.0.0),
+    clue,
     cluster,
     GGally,
     ggdendro,
@@ -45,7 +47,6 @@ Suggests:
     glmnet,
     knitr,
     lgr,
-    mlr3 (>= 0.6.0),
     mlr3cluster,
     mlr3filters,
     mlr3fselect (>= 1.3.0),

NAMESPACE

@@ -54,6 +54,7 @@ export(plot_learner_prediction)
 import(checkmate)
 import(data.table)
 import(ggplot2)
+import(mlr3)
 import(mlr3misc)
 importFrom(ggplot2,autoplot)
 importFrom(ggplot2,fortify)

R/BenchmarkResult.R

@@ -28,19 +28,15 @@
 #'
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3viz)
+#' if (mlr3misc::require_namespaces("precrec", quietly = TRUE)) {
+#' tasks = tsks(c("pima", "sonar"))
+#' learner = lrns(c("classif.featureless", "classif.rpart"), predict_type = "prob")
+#' resampling = rsmps("cv")
+#' object = benchmark(benchmark_grid(tasks, learner, resampling))
 #'
-#'   tasks = tsks(c("pima", "sonar"))
-#'   learner = lrns(c("classif.featureless", "classif.rpart"),
-#'     predict_type = "prob")
-#'   resampling = rsmps("cv")
-#'   object = benchmark(benchmark_grid(tasks, learner, resampling))
-#'
-#'   head(fortify(object))
-#'   autoplot(object)
-#'   autoplot(object$clone(deep = TRUE)$filter(task_ids = "pima"), type = "roc")
+#' head(fortify(object))
+#' autoplot(object)
+#' autoplot(object$clone(deep = TRUE)$filter(task_ids = "pima"), type = "roc")
 #' }
 autoplot.BenchmarkResult = function(object, type = "boxplot", measure = NULL, theme = theme_minimal(), ...) {
   assert_choice(type, choices = c("boxplot", "roc", "prc", "ci"), null.ok = FALSE)

R/EnsembleFSResult.R

@@ -35,12 +35,11 @@
 #'
 #' @return [ggplot2::ggplot()].
 #' @examples
+#' if (mlr3misc::require_namespaces("mlr3fselect", quietly = TRUE)) {
 #' \donttest{
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
+#'   set.seed (42)
 #'   library(mlr3fselect)
 #'
-#'   set.seed (42)
 #'   efsr = ensemble_fselect(
 #'     fselector = fs("random_search"),
 #'     task = tsk("sonar"),

R/Filter.R

@@ -18,17 +18,15 @@
 #' @return [ggplot2::ggplot()].
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3viz)
-#'   library(mlr3filters)
+#' if (mlr3misc::require_namespaces("mlr3filters", quietly = TRUE)) {
+#' library(mlr3filters)
 #'
-#'   task = tsk("mtcars")
-#'   f = flt("correlation")
-#'   f$calculate(task)
+#' task = tsk("mtcars")
+#' f = flt("correlation")
+#' f$calculate(task)
 #'
-#'   head(fortify(f))
-#'   autoplot(f, n = 5)
+#' head(fortify(f))
+#' autoplot(f, n = 5)
 #' }
 autoplot.Filter = function(object, type = "boxplot", n = Inf, theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("boxplot"), null.ok = FALSE)

R/LearnerClassif.R

@@ -21,17 +21,12 @@
 #' @export
 #' @examples
 #' \donttest{
-#' if (requireNamespace("mlr3")) {
-#' library(mlr3)
-#' library(mlr3viz)
-#'
 #' task = tsk("pima")$select(c("age", "pedigree"))
 #' learner = lrn("classif.rpart", predict_type = "prob")
 #' learner$train(task)
 #'
 #' autoplot(learner, type = "prediction", task)
 #' }
-#' }
 autoplot.LearnerClassif = function(object, type = "prediction", task, grid_points = 100L, expand_range = 0, theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("prediction"), null.ok = FALSE)
 

R/LearnerClassifGlmnet.R

@@ -24,9 +24,8 @@
 #'
 #' @export
 #' @examples
+#' if (mlr3misc::require_namespaces("mlr3learners", quietly = TRUE)) {
 #' \dontrun{
-#' library(mlr3)
-#' library(mlr3viz)
 #' library(mlr3learners)
 #'
 #' # classification
@@ -41,6 +40,7 @@
 #' learner$train(task)
 #' autoplot(learner, type = "ggfortify")
 #' }
+#' }
 autoplot.LearnerClassifGlmnet = function(object, type = "prediction", task = NULL, grid_points = 100L, expand_range = 0, theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("prediction", "ggfortify"), null.ok = FALSE)
   assert_has_model(object)

R/LearnerClassifRpart.R

@@ -21,21 +21,18 @@
 #'
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3viz)
+#' if (mlr3misc::require_namespaces(c("partykit", "ggparty"), quietly = TRUE)) {
+#' # classification
+#' task = tsk("iris")
+#' learner = lrn("classif.rpart", keep_model = TRUE)
+#' learner$train(task)
+#' autoplot(learner, type = "ggparty")
 #'
-#'   # classification
-#'   task = tsk("iris")
-#'   learner = lrn("classif.rpart", keep_model = TRUE)
-#'   learner$train(task)
-#'   autoplot(learner, type = "ggparty")
-#'
-#'   # regression
-#'   task = tsk("mtcars")
-#'   learner = lrn("regr.rpart", keep_model = TRUE)
-#'   learner$train(task)
-#'   autoplot(learner, type = "ggparty")
+#' # regression
+#' task = tsk("mtcars")
+#' learner = lrn("regr.rpart", keep_model = TRUE)
+#' learner$train(task)
+#' autoplot(learner, type = "ggparty")
 #' }
 autoplot.LearnerClassifRpart = function(object, type = "prediction", task = NULL, grid_points = 100L, expand_range = 0, theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("prediction", "ggparty"), null.ok = FALSE)

R/LearnerClustHierarchical.R

@@ -23,27 +23,25 @@
 #'
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3cluster)
-#'   library(mlr3viz)
+#' if (mlr3misc::require_namespaces("mlr3cluster", quietly = TRUE)) {
+#' library(mlr3cluster)
 #'
-#'   task = tsk("usarrests")
+#' task = tsk("usarrests")
 #'
-#'   # agnes clustering
-#'   learner = lrn("clust.agnes")
-#'   learner$train(task)
-#'   autoplot(learner)
+#' # agnes clustering
+#' learner = lrn("clust.agnes")
+#' learner$train(task)
+#' autoplot(learner)
 #'
-#'   # diana clustering
-#'   learner = lrn("clust.diana")
-#'   learner$train(task)
-#'   autoplot(learner)
+#' # diana clustering
+#' learner = lrn("clust.diana")
+#' learner$train(task)
+#' autoplot(learner)
 #'
-#'   # hclust clustering
-#'   learner = lrn("clust.hclust")
-#'   learner$train(task)
-#'   autoplot(learner, type = "scree")
+#' # hclust clustering
+#' learner = lrn("clust.hclust")
+#' learner$train(task)
+#' autoplot(learner, type = "scree")
 #' }
 autoplot.LearnerClustHierarchical = function(object, type = "dend", task = NULL, theme = theme_minimal(), theme_dendro = TRUE, ...) { # nolint
   assert_choice(type, choices = c("dend", "scree"), null.ok = FALSE)

R/LearnerRegr.R

@@ -21,17 +21,12 @@
 #' @export
 #' @examples
 #' \donttest{
-#' if (requireNamespace("mlr3")) {
-#' library(mlr3)
-#' library(mlr3viz)
-#'
 #' task = tsk("mtcars")$select(c("am", "carb"))
 #' learner = lrn("regr.rpart")
 #' learner$train(task)
 #'
 #' autoplot(learner, type = "prediction", task)
 #' }
-#' }
 autoplot.LearnerRegr = function(object, type = "prediction", task, grid_points = 100L, expand_range = 0, theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("prediction"), null.ok = FALSE)
 

R/OptimInstanceBatchSingleCrit.R

@@ -44,45 +44,44 @@
 #'
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3") && requireNamespace("bbotk") && requireNamespace("patchwork")) {
-#'   library(bbotk)
-#'   library(paradox)
+#' if (mlr3misc::require_namespaces(c("paradox", "bbotk", "patchwork"), quietly = TRUE)) {
+#' library(bbotk)
 #'
-#'   fun = function(xs) {
-#'     c(y = -(xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
-#'   }
-#'   domain = ps(
-#'     x1 = p_dbl(-10, 10),
-#'     x2 = p_dbl(-5, 5)
-#'   )
-#'   codomain = ps(
-#'     y = p_dbl(tags = "maximize")
-#'   )
-#'   obfun = ObjectiveRFun$new(
-#'     fun = fun,
-#'     domain = domain,
-#'     codomain = codomain
-#'   )
+#' fun = function(xs) {
+#'   c(y = -(xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
+#' }
+#' domain = ps(
+#'   x1 = p_dbl(-10, 10),
+#'   x2 = p_dbl(-5, 5)
+#' )
+#' codomain = ps(
+#'   y = p_dbl(tags = "maximize")
+#' )
+#' obfun = ObjectiveRFun$new(
+#'   fun = fun,
+#'   domain = domain,
+#'   codomain = codomain
+#' )
 #'
-#'   instance = oi(objective = obfun, terminator = trm("evals", n_evals = 20))
+#' instance = oi(objective = obfun, terminator = trm("evals", n_evals = 20))
 #'
-#'   optimizer = opt("random_search", batch_size = 2)
-#'   optimizer$optimize(instance)
+#' optimizer = opt("random_search", batch_size = 2)
+#' optimizer$optimize(instance)
 #'
-#'   # plot y versus batch number
-#'   print(autoplot(instance, type = "performance"))
+#' # plot y versus batch number
+#' print(autoplot(instance, type = "performance"))
 #'
-#'   # plot x1 values versus performance
-#'   print(autoplot(instance, type = "marginal", cols_x = "x1"))
+#' # plot x1 values versus performance
+#' print(autoplot(instance, type = "marginal", cols_x = "x1"))
 #'
-#'   # plot parallel coordinates plot
-#'   print(autoplot(instance, type = "parallel"))
+#' # plot parallel coordinates plot
+#' print(autoplot(instance, type = "parallel"))
 #'
-#'   # plot pairs
-#'   print(autoplot(instance, type = "pairs"))
+#' # plot pairs
+#' print(autoplot(instance, type = "pairs"))
 #'
-#'   # plot incumbent
-#'   print(autoplot(instance, type = "incumbent"))
+#' # plot incumbent
+#' print(autoplot(instance, type = "incumbent"))
 #' }
 autoplot.OptimInstanceBatchSingleCrit = function(object, type = "marginal", cols_x = NULL, trafo = FALSE, learner = mlr3::lrn("regr.ranger"), grid_resolution = 100, batch = NULL, theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("marginal", "performance", "parameter", "parallel",

R/PredictionClassif.R

@@ -26,18 +26,15 @@
 #' @export
 #' @examples
 #' \donttest{
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3viz)
+#' if (mlr3misc::require_namespaces("precrec", quietly = TRUE)) {
+#' task = tsk("spam")
+#' learner = lrn("classif.rpart", predict_type = "prob")
+#' object = learner$train(task)$predict(task)
 #'
-#'   task = tsk("spam")
-#'   learner = lrn("classif.rpart", predict_type = "prob")
-#'   object = learner$train(task)$predict(task)
-#'
-#'   head(fortify(object))
-#'   autoplot(object)
-#'   autoplot(object, type = "roc")
-#'   autoplot(object, type = "prc")
+#' head(fortify(object))
+#' autoplot(object)
+#' autoplot(object, type = "roc")
+#' autoplot(object, type = "prc")
 #' }
 #' }
 autoplot.PredictionClassif = function(object, type = "stacked", measure = NULL, theme = theme_minimal(), ...) { # nolint