compatibility: mlr3 0.21.0 (#159)

* compatibility: mlr3 0.21.0 * ... * cleanup tests --------- Co-authored-by: Lennart Schneider <[email protected]>
mlr-org · Sep 16, 2024 · eb27ebb · eb27ebb
1 parent ba8297f
commit eb27ebb
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -64,7 +64,8 @@ Suggests:
  rpart,
  stringi,
  testthat (>= 3.0.0)
-Remotes: mlr-org/bbotk
+Remotes:
+ mlr-org/mlr3
 ByteCompile: no
 Encoding: UTF-8
 Config/testthat/edition: 3

diff --git a/R/mbo_defaults.R b/R/mbo_defaults.R
@@ -161,14 +161,13 @@ default_surrogate = function(instance, learner = NULL, n_learner = NULL) {
  default_rf(noisy)
  }
  # stability: evaluate and add a fallback
- learner$encapsulate[c("train", "predict")] = "evaluate"
  require_namespaces("ranger")
  fallback = mlr3learners::LearnerRegrRanger$new()
  fallback$param_set$values = insert_named(
  fallback$param_set$values,
  list(num.trees = 10L, keep.inbag = TRUE, se.method = "jack")
  )
- learner$fallback = fallback
+ learner$encapsulate("evaluate", fallback)
 
  if (has_deps) {
  require_namespaces("mlr3pipelines")
@@ -184,8 +183,7 @@ default_surrogate = function(instance, learner = NULL, n_learner = NULL) {
  learner
  )
  )
- learner$encapsulate[c("train", "predict")] = "evaluate"
- learner$fallback = LearnerRegrFeatureless$new()
+ learner$encapsulate("evaluate", lrn("regr.featureless"))
  }
  }
 

diff --git a/tests/testthat/helper.R b/tests/testthat/helper.R
@@ -101,13 +101,13 @@ MAKE_DESIGN = function(instance, n = 4L) {
 
 if (requireNamespace("mlr3learners") && requireNamespace("DiceKriging") && requireNamespace("rgenoud")) {
  library(mlr3learners)
- REGR_KM_NOISY = lrn("regr.km", covtype = "matern3_2", optim.method = "gen", control = list(trace = FALSE, max.generations = 2), nugget.estim = TRUE, jitter = 1e-12)
- REGR_KM_NOISY$encapsulate = c(train = "callr", predict = "callr")
- REGR_KM_DETERM = lrn("regr.km", covtype = "matern3_2", optim.method = "gen", control = list(trace = FALSE, max.generations = 2), nugget.stability = 10^-8)
- REGR_KM_DETERM$encapsulate = c(train = "callr", predict = "callr")
+ REGR_KM_NOISY = lrn("regr.km", covtype = "matern3_2", optim.method = "gen", control = list(trace = FALSE), nugget.estim = TRUE, jitter = 1e-12)
+ REGR_KM_NOISY$encapsulate("callr", lrn("regr.featureless"))
+ REGR_KM_DETERM = lrn("regr.km", covtype = "matern3_2", optim.method = "gen", control = list(trace = FALSE), nugget.stability = 10^-8)
+ REGR_KM_DETERM$encapsulate("callr", lrn("regr.featureless"))
 }
 REGR_FEATURELESS = lrn("regr.featureless")
-REGR_FEATURELESS$encapsulate = c(train = "callr", predict = "callr")
+REGR_FEATURELESS$encapsulate("callr", lrn("regr.featureless"))
 
 OptimizerError = R6Class("OptimizerError",
  inherit = OptimizerBatch,

diff --git a/tests/testthat/test_bayesopt_ego.R b/tests/testthat/test_bayesopt_ego.R
@@ -109,7 +109,7 @@ test_that("stable bayesopt_ego", {
  expect_true(nrow(instance$archive$data) == 5L)
  expect_number(acq_function$surrogate$assert_insample_perf, upper = 1)
  lines = readLines(f)
- # expect_true(sum(grepl("Optimizer Error", unlist(map(strsplit(lines, "\\[bbotk\\] "), 2L)))) == 1L)
+ expect_true(sum(grepl("Optimizer Error", unlist(map(strsplit(lines, "\\[bbotk\\] "), 2L)))) == 1L)
  expect_true(sum(grepl("Proposing a randomly sampled point", unlist(map(strsplit(lines, "\\[bbotk\\] "), 2L)))) == 2L)
 
  # Surrogate using LearnerRegrError as Learner that will fail during train

diff --git a/tests/testthat/test_mbo_defaults.R b/tests/testthat/test_mbo_defaults.R
@@ -23,7 +23,7 @@ test_that("default_surrogate", {
  expect_r6(surrogate$learner, "LearnerRegrKM")
  expect_equal_sorted(surrogate$learner$param_set$values,
  list(covtype = "matern5_2", optim.method = "gen", control = list(trace = FALSE), nugget.stability = 1e-08))
- expect_equal(surrogate$learner$encapsulate, c(train = "evaluate", predict = "evaluate"))
+ expect_equal(surrogate$learner$encapsulation, c(train = "evaluate", predict = "evaluate"))
  expect_r6(surrogate$learner$fallback, "LearnerRegrRanger")
 
  # singlecrit all numeric, noisy
@@ -32,7 +32,7 @@ test_that("default_surrogate", {
  expect_r6(surrogate$learner, "LearnerRegrKM")
  expect_equal_sorted(surrogate$learner$param_set$values,
  list(covtype = "matern5_2", optim.method = "gen", control = list(trace = FALSE), nugget.estim = TRUE, jitter = 1e-12))
- expect_equal(surrogate$learner$encapsulate, c(train = "evaluate", predict = "evaluate"))
+ expect_equal(surrogate$learner$encapsulation, c(train = "evaluate", predict = "evaluate"))
  expect_r6(surrogate$learner$fallback, "LearnerRegrRanger")
 
  # twocrit all numeric, deterministic
@@ -41,10 +41,10 @@ test_that("default_surrogate", {
  expect_list(surrogate$learner, types = "LearnerRegrKM")
  expect_equal_sorted(surrogate$learner[[1L]]$param_set$values,
  list(covtype = "matern5_2", optim.method = "gen", control = list(trace = FALSE), nugget.stability = 1e-08))
- expect_equal(surrogate$learner[[1L]]$encapsulate, c(train = "evaluate", predict = "evaluate"))
+ expect_equal(surrogate$learner[[1L]]$encapsulation, c(train = "evaluate", predict = "evaluate"))
  expect_r6(surrogate$learner[[1L]]$fallback, "LearnerRegrRanger")
  expect_equal(surrogate$learner[[1L]]$param_set$values, surrogate$learner[[2L]]$param_set$values)
- expect_equal(surrogate$learner[[1L]]$encapsulate, surrogate$learner[[2L]]$encapsulate)
+ expect_equal(surrogate$learner[[1L]]$encapsulation, surrogate$learner[[2L]]$encapsulation)
  expect_equal(surrogate$learner[[1L]]$fallback, surrogate$learner[[2L]]$fallback)
 
  # twocrit all numeric, noisy
@@ -53,10 +53,10 @@ test_that("default_surrogate", {
  expect_list(surrogate$learner, types = "LearnerRegrKM")
  expect_equal_sorted(surrogate$learner[[1L]]$param_set$values,
  list(covtype = "matern5_2", optim.method = "gen", control = list(trace = FALSE), nugget.estim = TRUE, jitter = 1e-12))
- expect_equal(surrogate$learner[[1L]]$encapsulate, c(train = "evaluate", predict = "evaluate"))
+ expect_equal(surrogate$learner[[1L]]$encapsulation, c(train = "evaluate", predict = "evaluate"))
  expect_r6(surrogate$learner[[1L]]$fallback, "LearnerRegrRanger")
  expect_equal(surrogate$learner[[1L]]$param_set$values, surrogate$learner[[2L]]$param_set$values)
- expect_equal(surrogate$learner[[1L]]$encapsulate, surrogate$learner[[2L]]$encapsulate)
+ expect_equal(surrogate$learner[[1L]]$encapsulation, surrogate$learner[[2L]]$encapsulation)
  expect_equal(surrogate$learner[[1L]]$fallback, surrogate$learner[[2L]]$fallback)
 
  # singlecrit mixed input
@@ -65,7 +65,7 @@ test_that("default_surrogate", {
  expect_r6(surrogate$learner, "LearnerRegrRanger")
  expect_equal_sorted(surrogate$learner$param_set$values,
  list(num.threads = 1L, num.trees = 100L, keep.inbag = TRUE, se.method = "jack"))
- expect_equal(surrogate$learner$encapsulate, c(train = "evaluate", predict = "evaluate"))
+ expect_equal(surrogate$learner$encapsulation, c(train = "evaluate", predict = "evaluate"))
  expect_r6(surrogate$learner$fallback, "LearnerRegrRanger")
 
  # twocrit mixed input
@@ -74,10 +74,10 @@ test_that("default_surrogate", {
  expect_list(surrogate$learner, types = "LearnerRegrRanger")
  expect_equal_sorted(surrogate$learner[[1L]]$param_set$values,
  list(num.threads = 1L, num.trees = 100L, keep.inbag = TRUE, se.method = "jack"))
- expect_equal(surrogate$learner[[1L]]$encapsulate, c(train = "evaluate", predict = "evaluate"))
+ expect_equal(surrogate$learner[[1L]]$encapsulation, c(train = "evaluate", predict = "evaluate"))
  expect_r6(surrogate$learner[[1L]]$fallback, "LearnerRegrRanger")
  expect_equal(surrogate$learner[[1L]]$param_set$values, surrogate$learner[[2L]]$param_set$values)
- expect_equal(surrogate$learner[[1L]]$encapsulate, surrogate$learner[[2L]]$encapsulate)
+ expect_equal(surrogate$learner[[1L]]$encapsulation, surrogate$learner[[2L]]$encapsulation)
  expect_equal(surrogate$learner[[1L]]$fallback, surrogate$learner[[2L]]$fallback)
 
  # singlecrit mixed input deps
@@ -152,12 +152,11 @@ test_that("stability and defaults", {
  # this should trigger a mbo_error
  instance = MAKE_INST_1D(terminator = trm("evals", n_evals = 5L))
  learner = LearnerRegrError$new()
- learner$encapsulate[c("train", "predict")] = "evaluate"
- learner$fallback = lrn("regr.ranger", num.trees = 10L, keep.inbag = TRUE, se.method = "jack")
+ learner$encapsulate("evaluate", lrn("regr.ranger", num.trees = 10L, keep.inbag = TRUE, se.method = "jack"))
  surrogate = default_surrogate(instance, learner = learner, n_learner = 1L)
  expect_r6(surrogate, "SurrogateLearner")
  expect_r6(surrogate$learner, "LearnerRegrError")
- expect_equal(surrogate$learner$encapsulate, c(train = "evaluate", predict = "evaluate"))
+ expect_equal(surrogate$learner$encapsulation, c(train = "evaluate", predict = "evaluate"))
  expect_r6(surrogate$learner$fallback, "LearnerRegrRanger")
  acq_function = default_acqfunction(instance)
  expect_r6(acq_function, "AcqFunctionEI")
@@ -172,17 +171,5 @@ test_that("stability and defaults", {
  lines = readLines(f)
  # Nothing should happen here due to the fallback learner
  expect_true(sum(grepl("Surrogate Train Error", unlist(map(strsplit(lines, "\\[bbotk\\] "), 2L)))) == 0L)
-
- acq_function$surrogate$learner$reset()
- acq_function$surrogate$learner$fallback = NULL
- instance$archive$clear()
- bayesopt_ego(instance, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer)
- expect_true(nrow(instance$archive$data) == 5L)
- lines = readLines(f)
- # Training fails but this error is not logged due to the "evaluate" encapsulate
- expect_equal(acq_function$surrogate$learner$errors, "Surrogate Train Error.")
- expect_true(sum(grepl("Surrogate Train Error", unlist(map(strsplit(lines, "\\[bbotk\\] "), 2L)))) == 0L)
- expect_true(sum(grepl("Cannot predict", unlist(map(strsplit(lines, "\\[bbotk\\] "), 2L)))) == 1L)
- expect_true(sum(grepl("Proposing a randomly sampled point", unlist(map(strsplit(lines, "\\[bbotk\\] "), 2L)))) == 1L)
 })