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ci: update no suggests (#195)
* ci: update no suggests * ...
1 parent 8d68945 commit 42d2e3f

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‎.github/workflows/no-suggest-cmd-check.yml‎

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# r cmd check workflow without suggests of the mlr3 ecosystem v0.3.1
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# https://github.com/mlr-org/actions
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# modified to use supercharge/[email protected]
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on:
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workflow_dispatch:
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inputs:
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with:
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r-version: ${{ matrix.config.r }}
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- uses: supercharge/[email protected]
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with:
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redis-version: 7
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- uses: r-lib/actions/setup-r-dependencies@v2
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with:
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extra-packages: |

‎R/sugar.R‎

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@@ -137,7 +137,7 @@ acqo = function(optimizer, terminator, acq_function = NULL, callbacks = NULL, ..
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dots = list(...)
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if (is.character(optimizer)) {
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return(dictionary_sugar_get(mlr_acqoptimizers, optimizer, ...))
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return(dictionary_sugar_get(mlr_acqoptimizers, optimizer, acq_function = acq_function, ...))
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}
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acqopt = AcqOptimizer$new(optimizer = optimizer, terminator = terminator, acq_function = acq_function, callbacks = callbacks)

‎tests/testthat/helper.R‎

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@@ -108,6 +108,12 @@ MAKE_DESIGN = function(instance, n = 4L) {
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generate_design_random(instance$search_space, n)$data
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}
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skip_if_missing_regr_km = function() {
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skip_if_not_installed("mlr3learners")
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skip_if_not_installed("DiceKriging")
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skip_if_not_installed("rgenoud")
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}
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if (requireNamespace("mlr3learners") && requireNamespace("DiceKriging") && requireNamespace("rgenoud")) {
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library(mlr3learners)
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REGR_KM_NOISY = lrn("regr.km", covtype = "matern3_2", optim.method = "gen", control = list(trace = FALSE), nugget.estim = TRUE, jitter = 1e-12)

‎tests/testthat/test_AcqFunctionMulti.R‎

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@@ -26,6 +26,8 @@ test_that("AcqFunctionMulti works for single-objective instances", {
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})
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test_that("AcqFunctionMulti works for multi-objective instances", {
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skip_if_not_installed("emoa")
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skip_if_not_installed("fastGHQuad")
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inst = MAKE_INST(OBJ_1D_2, PS_1D, trm("evals", n_evals = 5L))
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surrogate = SurrogateLearnerCollection$new(list(REGR_FEATURELESS, REGR_FEATURELESS$clone(deep = TRUE)), archive = inst$archive)
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acqfs = list(AcqFunctionEHVI$new(), AcqFunctionEHVIGH$new())

‎tests/testthat/test_AcqOptimizerDirect.R‎

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test_that("AcqOptimizerDirect works", {
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skip_if_missing_regr_km()
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instance = oi(OBJ_1D, terminator = trm("evals", n_evals = 5L))
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design = generate_design_grid(instance$search_space, resolution = 4L)$data
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instance$eval_batch(design)
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})
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test_that("AcqOptimizerDirect works with 2D", {
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skip_if_missing_regr_km()
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instance = oi(OBJ_2D, terminator = trm("evals", n_evals = 5L))
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design = generate_design_grid(instance$search_space, resolution = 4L)$data
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instance$eval_batch(design)
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})
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test_that("AcqOptimizerDirect works with instance", {
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skip_if_missing_regr_km()
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instance = oi(OBJ_1D, terminator = trm("evals", n_evals = 10L))
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design = generate_design_grid(instance$search_space, resolution = 4L)$data
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instance$eval_batch(design)
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})
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test_that("AcqOptimizerDirect works with random restart", {
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skip_if_missing_regr_km()
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instance = oi(OBJ_2D, terminator = trm("evals", n_evals = 5L))
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design = generate_design_grid(instance$search_space, resolution = 4L)$data
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instance$eval_batch(design)

‎tests/testthat/test_AcqOptimizerLbfgsb.R‎

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test_that("AcqOptimizerLbfgsb works", {
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skip_if_missing_regr_km()
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instance = oi(OBJ_1D, terminator = trm("evals", n_evals = 5L))
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design = generate_design_grid(instance$search_space, resolution = 4L)$data
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instance$eval_batch(design)
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})
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test_that("AcqOptimizerLbfgsb works with 2D", {
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skip_if_missing_regr_km()
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instance = oi(OBJ_2D, terminator = trm("evals", n_evals = 5L))
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design = generate_design_grid(instance$search_space, resolution = 4L)$data
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instance$eval_batch(design)
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})
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test_that("AcqOptimizerLbfgsb works with instance", {
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skip_if_missing_regr_km()
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instance = oi(OBJ_1D, terminator = trm("evals", n_evals = 10L))
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design = generate_design_grid(instance$search_space, resolution = 4L)$data
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instance$eval_batch(design)
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})
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test_that("AcqOptimizerLbfgsb works with random restart", {
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skip_if_missing_regr_km()
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instance = oi(OBJ_2D, terminator = trm("evals", n_evals = 5L))
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design = generate_design_grid(instance$search_space, resolution = 4L)$data
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instance$eval_batch(design)

‎tests/testthat/test_AcqOptimizerLocalSearch.R‎

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test_that("AcqOptimizerLocalSearch works", {
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skip_if_missing_regr_km()
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instance = oi(OBJ_1D, terminator = trm("evals", n_evals = 5L))
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design = generate_design_grid(instance$search_space, resolution = 4L)$data
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instance$eval_batch(design)
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test_that("AcqOptimizerLocalSearch works with 2D", {
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skip_if_missing_regr_km()
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instance = oi(OBJ_2D, terminator = trm("evals", n_evals = 5L))
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design = generate_design_grid(instance$search_space, resolution = 4L)$data
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instance$eval_batch(design)
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test_that("AcqOptimizerLocalSearch works with instance", {
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skip_if_missing_regr_km()
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instance = oi(OBJ_1D, terminator = trm("evals", n_evals = 10L))
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design = generate_design_grid(instance$search_space, resolution = 4L)$data
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instance$eval_batch(design)

‎tests/testthat/test_AcqOptimizerRandomSearch.R‎

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test_that("AcqOptimizerRandomSearch works", {
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skip_if_missing_regr_km()
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instance = oi(OBJ_1D, terminator = trm("evals", n_evals = 5L))
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design = generate_design_grid(instance$search_space, resolution = 4L)$data
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instance$eval_batch(design)
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test_that("AcqOptimizerRandomSearch works with 2D", {
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skip_if_missing_regr_km()
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instance = oi(OBJ_2D, terminator = trm("evals", n_evals = 5L))
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design = generate_design_grid(instance$search_space, resolution = 4L)$data
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instance$eval_batch(design)
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test_that("AcqOptimizerRandomSearch works with instance", {
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skip_if_missing_regr_km()
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instance = oi(OBJ_1D, terminator = trm("evals", n_evals = 10L))
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design = generate_design_grid(instance$search_space, resolution = 4L)$data
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instance$eval_batch(design)

‎tests/testthat/test_InputTrafoUnitcube.R‎

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test_that("InputTrafoUnitcube works with SurrogateLearner", {
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skip_if_missing_regr_km()
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instance = MAKE_INST_1D()
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design = generate_design_random(instance$search_space, n = 4L)$data
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instance$eval_batch(design)
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test_that("InputTrafoUnitcube works with SurrogateLearnerCollection", {
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skip_if_missing_regr_km()
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instance = MAKE_INST(OBJ_1D_2, search_space = PS_1D, terminator = trm("evals", n_evals = 5L))
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design = generate_design_random(instance$search_space, n = 4L)$data
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instance$eval_batch(design)
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test_that("InputTrafoUnitcube works with OptimizerMbo and bayesopt_ego", {
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skip_if_missing_regr_km()
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instance = MAKE_INST_1D()
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surrogate = SurrogateLearner$new(REGR_KM_DETERM)
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it = InputTrafoUnitcube$new()
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test_that("InputTrafoUnitcube works with OptimizerMbo and bayesopt_parego", {
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skip_if_missing_regr_km()
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instance = MAKE_INST(OBJ_1D_2, search_space = PS_1D, terminator = trm("evals", n_evals = 5L))
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surrogate = SurrogateLearner$new(REGR_KM_DETERM)
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it = InputTrafoUnitcube$new()
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test_that("InputTrafoUnitcube works with OptimizerMbo and bayesopt_smsego", {
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skip_if_missing_regr_km()
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instance = MAKE_INST(OBJ_1D_2, search_space = PS_1D, terminator = trm("evals", n_evals = 5L))
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surrogate = SurrogateLearnerCollection$new(list(REGR_KM_DETERM, REGR_KM_DETERM$clone(deep = TRUE)))
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it = InputTrafoUnitcube$new()

‎tests/testthat/test_OutputTrafoLog.R‎

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test_that("OutputTrafoLog works with SurrogateLearner", {
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instance = MAKE_INST_1D()
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design = generate_design_random(instance$search_space, n = 4L)$data
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instance$eval_batch(design)
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test_that("OutputTrafoLog works with SurrogateLearnerCollection", {
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instance = MAKE_INST(OBJ_1D_2, search_space = PS_1D, terminator = trm("evals", n_evals = 5L))
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design = generate_design_random(instance$search_space, n = 4L)$data
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instance$eval_batch(design)
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test_that("OutputTrafoLog works with OptimizerMbo and bayesopt_ego", {
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instance = MAKE_INST_1D()
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surrogate = SurrogateLearner$new(REGR_KM_DETERM)
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ot = OutputTrafoLog$new()
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test_that("OutputTrafoLog works with OptimizerMbo and bayesopt_ego for maximization and EI on log scale", {
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instance = MAKE_INST(OBJ_1D_MAXIMIZE, search_space = PS_1D, terminator = trm("evals", n_evals = 5L))
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surrogate = SurrogateLearner$new(REGR_KM_DETERM)
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ot = OutputTrafoLog$new(invert_posterior = FALSE)
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test_that("OutputTrafoLog works with OptimizerMbo and bayesopt_parego", {
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skip_if_missing_regr_km()
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instance = MAKE_INST(OBJ_1D_2, search_space = PS_1D, terminator = trm("evals", n_evals = 5L))
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surrogate = SurrogateLearner$new(REGR_KM_DETERM)
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ot = OutputTrafoLog$new()
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test_that("OutputTrafoLog works with OptimizerMbo and bayesopt_smsego", {
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skip_if_missing_regr_km()
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instance = MAKE_INST(OBJ_1D_2, search_space = PS_1D, terminator = trm("evals", n_evals = 5L))
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surrogate = SurrogateLearnerCollection$new(list(REGR_KM_DETERM, REGR_KM_DETERM$clone(deep = TRUE)))
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ot = OutputTrafoLog$new()

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