Nothing
skip_if_not_installed("mlr3")
set.seed(42)
tasks = mlr3::tsks(c("iris", "pima", "sonar"))
learner = mlr3::lrns(c("classif.featureless", "classif.rpart"), predict_type = "prob")
resampling = mlr3::rsmp("cv", folds = 3)
bmr = mlr3::benchmark(mlr3::benchmark_grid(tasks, learner, resampling))
test_that("fortify BenchmarkResult", {
f = fortify(bmr, measure = msr("classif.ce"))
expect_data_table(f, nrows = 18, ncols = 5)
expect_names(names(f), permutation.of = c(
"nr", "task_id", "learner_id",
"resampling_id", "classif.ce"))
})
test_that("autoplot BenchmarkResult", {
p = autoplot(bmr, measure = msr("classif.ce"), type = "boxplot")
expect_true(is.ggplot(p))
expect_doppelganger("bmr_boxplot", p)
expect_error(autoplot(bmr, type = "roc"), "multiple")
object = bmr$clone(deep = TRUE)$filter(task_ids = "sonar")
p = autoplot(object, type = "roc")
expect_true(is.ggplot(p))
expect_doppelganger("bmr_roc", p)
object = bmr$clone(deep = TRUE)$filter(task_ids = "pima")
p = autoplot(object, type = "prc")
expect_true(is.ggplot(p))
expect_doppelganger("bmr_prc", p)
})
test_that("holdout roc plot (#54)", {
tasks = tsks("german_credit")
learners = c("classif.featureless", "classif.rpart")
learners = lapply(learners, lrn,
predict_type = "prob")
resamplings = rsmp("holdout", ratio = .8) # holdout instead of cv
design = benchmark_grid(tasks, learners, resamplings)
bmr = benchmark(design)
p = autoplot(bmr, type = "roc")
expect_true(is.ggplot(p))
expect_doppelganger("bmr_holdout_roc", p)
})
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