Merge branch 'main' into release

DESCRIPTION

@@ -49,7 +49,6 @@ Suggests:
     mlr3cluster,
     mlr3filters,
     mlr3fselect (>= 1.0.0),
-	mlr3inferr,
     mlr3learners,
     mlr3tuning (>= 1.0.0),
     paradox,
@@ -65,7 +64,7 @@ Suggests:
     survminer,
     mlr3proba (>= 0.6.3)
 Remotes:
-    mlr-org/mlr3inferr
+    mlr-org/mlr3proba
 Additional_repositories:
   https://mlr-org.r-universe.dev
 Config/testthat/edition: 3

NEWS.md

@@ -1,7 +1,6 @@
 # mlr3viz (development version)
 
 - Add plot for `LearnerSurvCoxPH`.
-- Add plot for confidence intervals (`mlr3inferr`)
 
 # mlr3viz 0.9.0
 

R/BenchmarkResult.R

@@ -12,7 +12,6 @@
 #'   Requires package \CRANpkg{precrec}.
 #' * `"prc"`: Precision recall curve.
 #'    See `"roc"`.
-#' * `"ci"`: Plot confidence intervals. Pass a `msr("ci", ...)` from the `mlr3inferr` package as argument `measure`.
 #'
 #' @param object ([mlr3::BenchmarkResult]).
 #' @template param_type
@@ -47,42 +46,6 @@ autoplot.BenchmarkResult = function(object, type = "boxplot", measure = NULL, th
   task = object$tasks$task[[1L]]
   measure = mlr3::assert_measure(mlr3::as_measure(measure, task_type = task$task_type), task = task)
 
-  if (identical(type, "ci")) {
-    mlr3misc::require_namespaces("mlr3inferr")
-
-    assert_class(measure, "MeasureAbstractCi")
-    mid = measure$id
-
-    tbl = object$aggregate(measure)
-
-    tmp = map(object$resamplings$resampling, function(x) {
-      list(class(x), x$param_set$values)
-    })
-
-    if (length(unique(tmp)) != 1) {
-      stopf("Plot of type 'ci' requires exactly one resampling method")
-    }
-
-    # static checker
-    .data = NULL
-    task_id = NULL
-    p = ggplot(tbl, aes(x = .data[["learner_id"]], y = .data[[mid]])) +
-      geom_point() +
-      geom_errorbar(aes(ymin = .data[[paste0(mid, ".lower")]], ymax = .data[[paste0(mid, ".upper")]]), width = 0.2) +
-      facet_wrap(vars(task_id), scales = "free_y") +
-      labs(
-      title = sprintf("Confidence Intervals for alpha = %s", measure$param_set$values$alpha),
-      x = "Learner",
-      y = paste0(measure$measure$id)
-      ) +
-      theme +
-      theme(
-        axis.text.x = element_text(angle = 45, hjust = 1),
-        axis.title.x = element_blank()
-      )
-    return(p)
-  }
-
   measure_id = measure$id
   tab = fortify(object, measure = measure)
   tab$nr = sprintf("%09d", tab$nr)

tests/testthat/test_BenchmarkResult.R

@@ -49,18 +49,3 @@ test_that("holdout roc plot (#54)", {
   expect_doppelganger("bmr_holdout_roc", p)
 })
 
-skip_if_not_installed("mlr3inferr")
-skip_if_not_installed("rpart")
-
-test_that("CI plot", {
-  bmr = benchmark(benchmark_grid(tsks(c("mtcars", "mtcars")),
-    lrns(c("regr.featureless", "regr.rpart")), rsmp("holdout")))
-
-  p = autoplot(bmr, "ci", msr("ci", "regr.mse"))
-  expect_true(is.ggplot(p))
-  expect_doppelganger("bmr_holdout_ci", p)
-
-  bmr = benchmark(benchmark_grid(tsk("iris"), lrn("classif.rpart"),
-    rsmps(c("holdout", "cv"))))
-  expect_error(autoplot(bmr, "ci", msr("ci", "classif.acc")), "one resampling method")
-})