Enable parallel tests

DESCRIPTION

@@ -53,11 +53,13 @@ Suggests:
     mlr3pipelines,
     nloptr,
     rpart,
-    testthat
+    testthat (>= 3.0.0)
 Remotes:
     mlr-org/bbotk
 Encoding: UTF-8
 NeedsCompilation: no
+Config/testthat/edition: 3
+Config/testthat/parallel: true
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.1.1
 Collate:

tests/testthat/helper.R

@@ -6,15 +6,15 @@ expect_tuner = function(tuner) {
     public = c("optimize", "param_set"),
     private = ".optimize"
   )
-  expect_is(tuner$param_set, "ParamSet")
+  expect_class(tuner$param_set, "ParamSet")
   expect_function(tuner$optimize, args = "inst")
 }
 
 expect_terminator = function(term) {
   expect_r6(term, "Terminator",
     public = c("is_terminated", "param_set")
   )
-  expect_is(term$param_set, "ParamSet")
+  expect_class(term$param_set, "ParamSet")
 }
 
 #FIXME: This function should be exported so it can be used for tests in other packages

tests/testthat/setup.R

@@ -1,4 +1,5 @@
 library(mlr3)
+attachNamespace("checkmate")
 old_opts = options(
   warnPartialMatchArgs = TRUE,
   warnPartialMatchAttr = TRUE,

tests/testthat/test_AutoTuner.R

@@ -1,5 +1,3 @@
-context("AutoTuner")
-
 test_that("AutoTuner / train+predict", {
   te = trm("evals", n_evals = 4)
   task = tsk("iris")
@@ -19,7 +17,7 @@ test_that("AutoTuner / train+predict", {
   expect_equal(r$learner_param_vals[[1]], list(xval = 0, cp = 0.2))
   prd = at$predict(task)
   expect_prediction(prd)
-  expect_is(at$learner$model, "rpart")
+  expect_s3_class(at$learner$model, "rpart")
 })
 
 test_that("AutoTuner / resample", {
@@ -169,7 +167,7 @@ test_that("AutoTuner works with graphlearner", {
   expect_equal(r$learner_param_vals[[1]]$classif.rpart.cp, 0.2)
   prd = at$predict(task)
   expect_prediction(prd)
-  expect_is(at$learner$model$classif.rpart$model, "rpart")
+  expect_s3_class(at$learner$model$classif.rpart$model, "rpart")
 })
 
 test_that("Nested resampling works with graphlearner", {
@@ -209,8 +207,8 @@ test_that("Nested resampling works with graphlearner", {
   expect_data_table(tab$learner[[1]]$archive$data(), nrows = 3L)
   expect_data_table(tab$learner[[2]]$archive$data(), nrows = 3L)
 
-  expect_is(tab$learner[[1]]$model$learner$model$classif.rpart$model, "rpart")
-  expect_is(tab$learner[[1]]$model$learner$model$classif.rpart$model, "rpart")
+  expect_s3_class(tab$learner[[1]]$model$learner$model$classif.rpart$model, "rpart")
+  expect_s3_class(tab$learner[[1]]$model$learner$model$classif.rpart$model, "rpart")
 })
 
 test_that("store_tuning_instance, store_benchmark_result and store_models flags work", {
@@ -283,10 +281,10 @@ test_that("predict_type works", {
     tuner = tuner)
 
   at$train(task)
-  expect_equal(at$predict_type, "response")  
+  expect_equal(at$predict_type, "response")
   expect_equal(at$model$learner$predict_type, "response")
 
   at$predict_type = "prob"
-  expect_equal(at$predict_type, "prob")  
+  expect_equal(at$predict_type, "prob")
   expect_equal(at$model$learner$predict_type, "prob")
 })

tests/testthat/test_ObjectiveTuning.R

@@ -1,5 +1,3 @@
-context("ObjectiveTuning")
-
 test_that("ObjectiveTuning", {
   task = tsk("iris")
   learner = lrn("classif.rpart")

tests/testthat/test_Tuner.R

@@ -1,5 +1,3 @@
-context("Tuner")
-
 test_that("API", {
   for (n_evals in c(1, 5)) {
     rs = TunerRandomSearch$new()

tests/testthat/test_TunerCmaes.R

@@ -1,5 +1,3 @@
-context("TunerCmaes")
-
 test_that("TunerCmaes", {
   test_tuner("cmaes", par = 0.3)
 })

tests/testthat/test_TunerDesignPoints.R

@@ -1,5 +1,3 @@
-context("TunerDesignPoints")
-
 test_that("TunerDesignPoints", {
   d = data.table(cp = c(0.1, 0.3))
   test_tuner("design_points", design = d, term_evals = 2L, real_evals = 2, n_dim = 1L)

tests/testthat/test_TunerGenSA.R

@@ -1,5 +1,3 @@
-context("TunerGenSA")
-
 skip_if_not_installed("GenSA")
 
 test_that("TunerGenSA", {

tests/testthat/test_TunerGridSearch.R

@@ -1,5 +1,3 @@
-context("TunerGridSearch")
-
 test_that("TunerGridSearch", {
   test_tuner("grid_search", resolution = 7, term_evals = 5L, real_evals = 5, n_dim = 1L)
   test_tuner_dependencies("grid_search")

tests/testthat/test_TunerNLoptr.R

@@ -1,5 +1,3 @@
-context("TunerNLoptr")
-
 test_that("TunerNLoptr", {
   skip_on_os("windows")
   test_tuner("nloptr", x0 = 0.3, algorithm = "NLOPT_LN_BOBYQA", term_evals = 4)

tests/testthat/test_TunerRandomSearch.R

@@ -1,5 +1,3 @@
-context("TunerRandomSearch")
-
 test_that("TunerRandomSearch", {
   test_tuner("random_search")
   test_tuner_dependencies("random_search")

tests/testthat/test_TuningInstanceMultiCrit.R

@@ -1,5 +1,3 @@
-context("TuningInstanceMultiCrit")
-
 test_that("tuning with multiple objectives", {
   task = tsk("pima")
   resampling = rsmp("holdout")
@@ -41,7 +39,8 @@ test_that("store_benchmark_result and store_models flag works", {
   inst$eval_batch(data.table(cp = c(0.3, 0.25), minsplit = c(3, 4)))
   expect_r6(inst$archive$benchmark_result, "BenchmarkResult")
 
-  expect_error(TEST_MAKE_INST1_2D(store_benchmark_result = FALSE,
+  expect_error(TEST_MAKE_INST1_2D(
+    store_benchmark_result = FALSE,
     store_models = TRUE),
   regexp = "Models can only be stored if store_benchmark_result is set to TRUE",
   fixed = TRUE)
@@ -75,5 +74,5 @@ test_that("check_values flag with parameter set dependencies", {
     rsmp("holdout"), msr("regr.mse"), search_space, terminator,
     check_values = TRUE)
   expect_error(tuner$optimize(inst),
-    fixed = "The parameter 'yy' can only be set")
+    regexp = "The parameter 'yy' can only be set")
 })

tests/testthat/test_TuningInstanceSingleCrit.R

@@ -1,5 +1,3 @@
-context("TuningInstanceSingleCrit")
-
 test_that("TuningInstanceSingleCrit", {
   inst = TEST_MAKE_INST1(values = list(maxdepth = 10), folds = 2L, measure = msr("dummy.cp.classif", fun = function(pv) pv$cp), n_dim = 2)
   # test empty inst
@@ -186,5 +184,5 @@ test_that("check_values flag with parameter set dependencies", {
     rsmp("holdout"), msr("regr.mse"), search_space, terminator,
     check_values = TRUE)
   expect_error(tuner$optimize(inst),
-    fixed = "The parameter 'yy' can only be set")
+    regexp = "The parameter 'yy' can only be set")
 })

tests/testthat/test_error_handling.R

@@ -1,5 +1,3 @@
-context("error handling")
-
 test_that("failing learner", {
   learner = lrn("classif.debug")
   param_set = ParamSet$new(list(

tests/testthat/test_mlr_tuners.R

@@ -1,5 +1,3 @@
-context("mlr_tuners")
-
 test_that("mlr_tuners", {
   expect_dictionary(mlr_tuners)
   for (key in mlr_tuners$keys()) {

tests/testthat/test_trafos.R

@@ -1,5 +1,3 @@
-context("trafos")
-
 test_that("simple exp trafo works", {
   ll = lrn("classif.rpart")
   ps = ParamSet$new(params = list(