test_that("TunerMbo works", { skip_on_cran() skip_if_not_installed("mlr3learners") skip_if_not_installed("DiceKriging") skip_if_not_installed("rgenoud") tuner = TunerMbo$new() expect_r6(tuner, classes = "TunerMbo") expect_r6(tuner$.__enclos_env__$private$.optimizer, classes = "OptimizerMbo") learner = lrn("classif.debug", x = to_tune()) instance = TuningInstanceBatchSingleCrit$new(tsk("iris"), learner = learner, resampling = rsmp("holdout"), measure = msr("classif.ce"), terminator = trm("evals", n_evals = 5L)) design = MAKE_DESIGN(instance) instance$eval_batch(design) res = tuner$optimize(instance) expect_equal(res, instance$result) opdf = instance$archive$data expect_data_table(opdf, any.missing = TRUE, nrows = 5L) expect_data_table(tail(opdf, - nrow(design)), any.missing = FALSE, nrows = 5L - nrow(design)) tuner$optimize(instance) }) test_that("Constructing TunerMbo and ABs", { skip_if_not_installed("mlr3learners") skip_if_not_installed("DiceKriging") skip_if_not_installed("rgenoud") learner = lrn("classif.debug", x = to_tune()) instance = TuningInstanceBatchSingleCrit$new(tsk("iris"), learner = learner, resampling = rsmp("holdout"), measure = msr("classif.ce"), terminator = trm("evals", n_evals = 5L)) surrogate = default_surrogate(instance) tuner = tnr("mbo", loop_function = bayesopt_ego, surrogate = surrogate, acq_function = AcqFunctionEI$new(), acq_optimizer = AcqOptimizer$new(opt("random_search", batch_size = 2L), terminator = trm("evals", n_evals = 2L))) expect_r6(tuner, classes = "TunerMbo") expect_r6(tuner$.__enclos_env__$private$.optimizer, classes = "OptimizerMbo") optimizer = tuner[[".__enclos_env__"]][["private"]][[".optimizer"]] expect_identical(tuner$loop_function, optimizer$loop_function) expect_identical(tuner$surrogate, optimizer$surrogate) expect_identical(tuner$acq_function, optimizer$acq_function) expect_identical(tuner$acq_optimizer, optimizer$acq_optimizer) expect_identical(tuner$args, optimizer$args) expect_identical(tuner$result_assigner, optimizer$result_assigner) expect_identical(tuner$param_classes, optimizer$param_classes) expect_identical(tuner$properties, optimizer$properties) expect_identical(tuner$packages, optimizer$packages) tuner$optimize(instance) expect_identical(tuner$loop_function, optimizer$loop_function) expect_identical(tuner$surrogate, optimizer$surrogate) expect_identical(tuner$acq_function, optimizer$acq_function) expect_identical(tuner$acq_optimizer, optimizer$acq_optimizer) expect_identical(tuner$args, optimizer$args) expect_identical(tuner$result_assigner, optimizer$result_assigner) expect_identical(tuner$param_classes, optimizer$param_classes) expect_identical(tuner$properties, optimizer$properties) expect_identical(tuner$packages, optimizer$packages) }) test_that("TunerMbo sugar", { skip_if_not_installed("mlr3learners") skip_if_not_installed("DiceKriging") skip_if_not_installed("rgenoud") learner = lrn("classif.debug", x = to_tune()) instance = tune( tnr("mbo", acq_function = acqf("cb"), acq_optimizer = acqo(opt("random_search", batch_size = 2L), terminator = trm("evals", n_evals = 2L))), task = tsk("iris"), learner = learner, measures = msr("classif.ce"), resampling = rsmp("holdout"), term_evals = 5L ) expect_true(NROW(instance$archive$data) == 5L) expect_true("acq_cb" %in% colnames(instance$archive$data)) }) test_that("TunerMbo param_classes", { skip_if_not_installed("mlr3learners") skip_if_not_installed("DiceKriging") skip_if_not_installed("rgenoud") tuner = tnr("mbo") expect_equal(tuner$param_classes, c("ParamLgl", "ParamInt", "ParamDbl", "ParamFct")) instance = TuningInstanceBatchSingleCrit$new(tsk("iris"), learner = lrn("classif.debug", x = to_tune()), resampling = rsmp("holdout"), measure = msr("classif.ce"), terminator = trm("evals", n_evals = 5L)) tuner$surrogate = default_surrogate(instance) expect_equal(tuner$param_classes, c("ParamLgl", "ParamInt", "ParamDbl")) tuner$acq_optimizer = AcqOptimizer$new(opt("nloptr"), terminator = trm("evals", n_evals = 2L)) expect_equal(tuner$param_classes, "ParamDbl") }) test_that("TunerMbo properties", { skip_if_not_installed("mlr3learners") skip_if_not_installed("DiceKriging") skip_if_not_installed("rgenoud") tuner = tnr("mbo") expect_equal(tuner$properties, c("dependencies", "single-crit", "multi-crit")) instance = TuningInstanceBatchSingleCrit$new(tsk("iris"), learner = lrn("classif.debug", x = to_tune()), resampling = rsmp("holdout"), measure = msr("classif.ce"), terminator = trm("evals", n_evals = 5L)) tuner$surrogate = default_surrogate(instance) expect_equal(tuner$properties, c("single-crit", "multi-crit")) tuner$loop_function = bayesopt_ego expect_equal(tuner$properties, "single-crit") }) test_that("TunerMbo packages", { skip_if_not_installed("mlr3learners") skip_if_not_installed("DiceKriging") skip_if_not_installed("rgenoud") skip_if_not_installed("nloptr") skip_if_not_installed("ranger") tuner = tnr("mbo") expect_equal(tuner$packages, "mlr3mbo") instance = TuningInstanceBatchSingleCrit$new(tsk("iris"), learner = lrn("classif.debug", x = to_tune()), resampling = rsmp("holdout"), measure = msr("classif.ce"), terminator = trm("evals", n_evals = 5L)) tuner$surrogate = default_surrogate(instance) expect_equal(tuner$packages, c("mlr3mbo", "mlr3", "mlr3learners", "DiceKriging")) tuner$acq_optimizer = AcqOptimizer$new(opt("nloptr"), terminator = trm("evals", n_evals = 2L)) expect_equal(tuner$packages, c("mlr3mbo", "mlr3", "mlr3learners", "DiceKriging", "bbotk", "nloptr")) tuner$surrogate = SurrogateLearner$new(lrn("regr.ranger")) expect_equal(tuner$packages, c("mlr3mbo", "mlr3", "mlr3learners", "ranger", "bbotk", "nloptr")) }) test_that("TunerMbo args", { skip_if_not_installed("mlr3learners") skip_if_not_installed("DiceKriging") tuner = tnr("mbo", args = list(test = 1)) expect_equal(tuner$args, list(test = 1)) tuner$loop_function = bayesopt_ego expect_error(tuner$args, "Must be a subset of \\{'init_design_size','random_interleave_iter'\\}, but has additional elements \\{'test'\\}.") instance = TuningInstanceBatchSingleCrit$new(tsk("iris"), learner = lrn("classif.debug", x = to_tune()), resampling = rsmp("holdout"), measure = msr("classif.ce"), terminator = trm("evals", n_evals = 5L)) expect_error(tuner$optimize(instance), "Must be a subset of \\{'init_design_size','random_interleave_iter'\\}, but has additional elements \\{'test'\\}.") expect_equal(instance$archive$data, data.table()) tuner$args = list(random_interleave_iter = 1L) tuner$optimize(instance) expect_equal(nrow(instance$archive$data), 5L) expect_true(tuner$acq_function$id %nin% colnames(instance$archive$data)) }) test_that("TunerMbo reset", { skip_if_not_installed("mlr3learners") skip_if_not_installed("DiceKriging") tuner = tnr("mbo", acq_optimizer = acqo(opt("random_search", batch_size = 2L), terminator = trm("evals", n_evals = 2L))) learner = lrn("classif.debug", x = to_tune()) learner$predict_type = "prob" instance = TuningInstanceBatchSingleCrit$new(tsk("iris"), learner = learner, resampling = rsmp("holdout"), measure = msr("classif.ce"), terminator = trm("evals", n_evals = 5L)) tuner$optimize(instance) instance_mult = TuningInstanceBatchMultiCrit$new(tsk("iris"), learner = learner, resampling = rsmp("holdout"), measure = msrs(c("classif.ce", "classif.logloss")), terminator = trm("evals", n_evals = 5L)) expect_error(tuner$optimize(instance_mult), "does not support multi-crit objectives") expect_loop_function(tuner$loop_function) expect_r6(tuner$surrogate, "Surrogate") expect_r6(tuner$acq_function, "AcqFunction") expect_r6(tuner$acq_optimizer, "AcqOptimizer") tuner$reset() expect_null(tuner$loop_function) expect_null(tuner$surrogate) expect_null(tuner$acq_function) expect_null(tuner$acq_optimizer) tuner$optimize(instance_mult) expect_loop_function(tuner$loop_function) expect_r6(tuner$surrogate, "Surrogate") expect_r6(tuner$acq_function, "AcqFunction") expect_r6(tuner$acq_optimizer, "AcqOptimizer") })