release: 0.22.0 (#1204)

* release: 0.22.0

* ...

* ...

* ...

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3
 Title: Machine Learning in R - Next Generation
-Version: 0.21.1.9000
+Version: 0.22.0
 Authors@R:
   c(
     person("Michel", "Lang", , "[email protected]", role = "aut",

NEWS.md

@@ -1,7 +1,8 @@
-# mlr3 (development version)
+# mlr3 0.22.0
 
 * fix: Quantiles must not ascend with probabilities.
 * refactor: Replace `tsk("boston_housing")` with `tsk("california_housing")`.
+* feat: Require unique learner ids in `benchmark_grid()`.
 * BREAKING CHANGE: Remove ``$loglik()`` method from all learners.
 * fix: Ignore `future.globals.maxSize` when `future::plan("sequential")` is used.
 * feat: Add `$characteristics` field to `Task` to store additional information.

R/BenchmarkResult.R

@@ -551,7 +551,7 @@ as.data.table.BenchmarkResult = function(x, ..., hashes = FALSE, predict_sets =
   tab = tab[, c("uhash", "task", "learner", "resampling", "iteration", "prediction"), with = FALSE]
 
   if (task_characteristics) {
-    tab[, characteristics := map(task, "characteristics")]
+    set(tab, j = "characteristics", value = map(tab$task, "characteristics"))
     tab = unnest(tab, "characteristics")
   }
 

tests/testthat/test_Task.R

@@ -689,9 +689,9 @@ test_that("$characteristics works", {
   resampling = rsmp("cv", folds = 3)
 
   design = benchmark_grid(
-    task = list(tsk_1, tsk_2),
-    learner = learner,
-    resampling = resampling
+    tasks = list(tsk_1, tsk_2),
+    learners = learner,
+    resamplings = resampling
   )
 
   bmr = benchmark(design)
@@ -703,9 +703,9 @@ test_that("$characteristics works", {
   tsk_2$characteristics = list(n = 200, f = 2)
 
   design = benchmark_grid(
-    task = list(tsk_1, tsk_2),
-    learner = learner,
-    resampling = resampling
+    tasks = list(tsk_1, tsk_2),
+    learners = learner,
+    resamplings = resampling
   )
 
   bmr = benchmark(design)
@@ -718,9 +718,9 @@ test_that("$characteristics works", {
   tsk_2$characteristics = list(n = 200)
 
   design = benchmark_grid(
-    task = list(tsk_1, tsk_2),
-    learner = learner,
-    resampling = resampling
+    tasks = list(tsk_1, tsk_2),
+    learners = learner,
+    resamplings = resampling
   )
 
   bmr = benchmark(design)