release: 1.0.1

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3
 Title: Machine Learning in R - Next Generation
-Version: 1.0.0.9000
+Version: 1.0.1
 Authors@R:
   c(
     person("Michel", "Lang", , "[email protected]", role = "aut",
@@ -50,13 +50,13 @@ Imports:
     checkmate (>= 2.0.0),
     cli,
     data.table (>= 1.15.0),
-    evaluate,
+    evaluate (>= 1.0.4),
     future,
     future.apply (>= 1.5.0),
     lgr (>= 0.3.4),
     mlbench,
     mlr3measures (>= 1.0.0),
-    mlr3misc (>= 0.17.0),
+    mlr3misc (>= 0.18.0),
     parallelly,
     palmerpenguins,
     paradox (>= 1.0.1),

NEWS.md

@@ -1,4 +1,8 @@
-# mlr3 (development version)
+# mlr3 1.0.1
+
+* fix: The printer of `Learner` failed when the `validate` field was set.
+* fix: Avoid printing empty line for feature less tasks.
+* perf: Use `data.table::setattr()` for less copying.
 
 # mlr3 1.0.0
 

R/TaskRegr_california_housing.R

@@ -19,7 +19,7 @@
 #' @section Meta Information:
 #' `r rd_info(tsk("california_housing"))`
 #'
-#' @source \url{https://www.kaggle.com/datasets/camnugent/california-housing-prices}
+#' @source https://www.kaggle.com/datasets/camnugent/california-housing-prices
 #'
 #' @template seealso_task
 NULL

README.md

@@ -24,8 +24,7 @@ Status](https://www.r-pkg.org/badges/version-ago/mlr3)](https://cran.r-project.o
   [gallery](https://mlr-org.com/gallery.html) with case studies.
 - [Reference manual](https://mlr3.mlr-org.com/reference/)
 - [FAQ](https://mlr-org.com/faq.html)
-- Ask questions on [Stackoverflow (tag
-  \#mlr3)](https://stackoverflow.com/questions/tagged/mlr3)
+- Ask questions on Stackoverflow (tag mlr3)
 - **Extension Learners**
   - Recommended core regression, classification, and survival learners
     are in [mlr3learners](https://github.com/mlr-org/mlr3learners)
@@ -99,7 +98,7 @@ task_penguins = as_task_classif(species ~ ., data = palmerpenguins::penguins)
 task_penguins
 ```
 
-    ## 
+    ##
     ## ── <TaskClassif> (344x8) ───────────────────────────────────────────────────────
     ## • Target: species
     ## • Target classes: Adelie (44%), Gentoo (36%), Chinstrap (20%)
@@ -141,7 +140,7 @@ measure = msr("classif.acc")
 prediction$score(measure)
 ```
 
-    ## classif.acc 
+    ## classif.acc
     ##   0.9651163
 
 ### Resample
@@ -166,7 +165,7 @@ rr$score(measure)[, .(task_id, learner_id, iteration, classif.acc)]
 rr$aggregate(measure)
 ```
 
-    ## classif.acc 
+    ## classif.acc
     ##   0.9361302
 
 ## Extension Packages

tests/testthat/test_Task.R

@@ -769,7 +769,9 @@ test_that("rbind with weights", {
   new_data$w_lrn = 1001:1010
   new_data$w_msr = 2010:2001
   new_data$Petal.Length = new_data$Petal.Length + 100 # Change a feature to check backend update
-  new_data$Species = factor(sample(c("setosa", "versicolor", "virginica", "new_level"), 10, replace = TRUE)) # new factor level
+  new_species = sample(c("setosa", "versicolor", "virginica", "new_level"), 10, replace = TRUE) # new factor level
+  new_species[1] = "new_level" # include new level at least once
+  new_data$Species = factor(new_species)
 
   task$rbind(new_data)