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compatibility: mlr3 0.21.0 #159

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Sep 16, 2024
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compatibility: mlr3 0.21.0 #159

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compatibility: mlr3 0.21.0
be-marc Sep 11, 2024
b43d542
...
be-marc Sep 11, 2024
cb224d5
Merge remote-tracking branch 'origin/main' into compat_mlr3
sumny Sep 15, 2024
bbe9ac5
cleanup tests
sumny Sep 15, 2024
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3 changes: 2 additions & 1 deletion DESCRIPTION
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Original file line number Diff line number Diff line change
Expand Up @@ -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
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6 changes: 2 additions & 4 deletions R/mbo_defaults.R
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Expand Up @@ -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")
Expand All @@ -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"))
}
}

Expand Down
10 changes: 5 additions & 5 deletions tests/testthat/helper.R
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Expand Up @@ -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,
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2 changes: 1 addition & 1 deletion tests/testthat/test_bayesopt_ego.R
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Expand Up @@ -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
Expand Down
35 changes: 11 additions & 24 deletions tests/testthat/test_mbo_defaults.R
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Expand Up @@ -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
Expand All @@ -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
Expand All @@ -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)
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uncommented these tests again, should be working


# twocrit all numeric, noisy
Expand All @@ -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
Expand All @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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")
Expand All @@ -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)
})

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