Nothing
test_that("early stopping callback works", {
skip_on_cran()
skip_if_not_installed("mlr3learners")
skip_if_not_installed("xgboost")
library(mlr3learners) # nolint
library(mlr3pipelines) # nolint
instance = tune(
tuner = tnr("random_search", batch_size = 1),
task = tsk("pima"),
learner = lrn("classif.xgboost", eta = to_tune(1e-04, 1e-1, logscale = TRUE), early_stopping_rounds = 20, nrounds = 1000, early_stopping_set = "test"),
resampling = rsmp("cv", folds = 3),
measures = msr("classif.ce"),
term_evals = 2,
callbacks = clbk("mlr3tuning.early_stopping")
)
expect_numeric(instance$archive$best()$max_nrounds)
expect_equal(instance$archive$best()$max_nrounds, instance$result_learner_param_vals$nrounds)
expect_equal(instance$result_learner_param_vals$early_stopping_set, "none")
expect_null(instance$result_learner_param_vals$early_stopping_rounds)
at = auto_tuner(
tuner = tnr("random_search", batch_size = 1),
learner = lrn("classif.xgboost", eta = to_tune(1e-04, 1e-1, logscale = TRUE), early_stopping_rounds = 20, nrounds = 1000, early_stopping_set = "test"),
resampling = rsmp("cv", folds = 3),
measure = msr("classif.ce"),
term_evals = 2,
callbacks = clbk("mlr3tuning.early_stopping")
)
at$train(tsk("pima"))
expect_numeric(instance$archive$best()$max_nrounds)
expect_equal(at$tuning_instance$archive$best()$max_nrounds, at$learner$param_set$values$nrounds)
expect_equal(at$learner$param_set$values$early_stopping_set, "none")
expect_null(at$learner$param_set$values$early_stopping_rounds)
expect_error(tune(
tuner = tnr("random_search", batch_size = 5),
task = tsk("pima"),
learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE)),
resampling = rsmp("cv", folds = 3),
measures = msr("classif.ce"),
term_evals = 10,
callbacks = clbk("mlr3tuning.early_stopping")
), "incompatible")
})
test_that("backup callback works", {
file = tempfile(fileext = ".rds")
instance = tune(
tuner = tnr("random_search", batch_size = 2),
task = tsk("pima"),
learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE)),
resampling = rsmp("cv", folds = 3),
measures = msr("classif.ce"),
term_evals = 4,
callbacks = clbk("mlr3tuning.backup", path = file)
)
expect_file_exists(file)
expect_benchmark_result(readRDS(file))
})
test_that("backup callback works with standalone tuner", {
file = tempfile(fileext = ".rds")
instance = tune(
tuner = tnr("grid_search", batch_size = 2),
task = tsk("pima"),
learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE)),
resampling = rsmp("cv", folds = 3),
measures = msr("classif.ce"),
term_evals = 4,
callbacks = clbk("mlr3tuning.backup", path = file)
)
expect_file_exists(file)
expect_benchmark_result(readRDS(file))
})
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