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) }) 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") })