Nothing
task = tsk("iris")
learner = lrn("classif.featureless")
resampling = rsmp("cv", folds = 3)
rr = resample(task, learner, resampling)
test_that("resample", {
expect_resample_result(rr)
scores = rr$score(msr("classif.ce"))
expect_list(scores$prediction, "Prediction")
expect_numeric(scores$classif.ce, any.missing = FALSE)
expect_number(rr$aggregate(msr("classif.ce")))
learners = rr$learners
expect_different_address(learners[[1L]], learners[[2L]])
expect_equal(uniqueN(hashes(learners)), 1L)
rr = rr$clone(TRUE)$filter(2:3)
tab = as.data.table(rr)
expect_data_table(tab, nrows = 2L)
expect_data_table(tab, nrows = 2L)
expect_equal(tab$iteration, 2:3)
expect_resample_result(rr, allow_incomplete = TRUE)
})
test_that("empty RR", {
rr = ResampleResult$new()
expect_resample_result(rr)
})
test_that("resample with no or multiple measures", {
for (measures in list(mlr_measures$mget(c("classif.ce", "classif.acc")), list())) {
tab = rr$score(measures, ids = FALSE)
expect_data_table(tab, ncols = length(mlr_reflections$rr_names) + length(measures), nrows = 3L)
expect_set_equal(names(tab), c(mlr_reflections$rr_names, ids(measures)))
perf = rr$aggregate(measures)
expect_numeric(perf, any.missing = FALSE, len = length(measures), names = "unique")
expect_equal(names(perf), unname(ids(measures)))
}
})
test_that("as_benchmark_result.ResampleResult", {
measures = list(msr("classif.ce"), msr("classif.acc"))
bmr = as_benchmark_result(rr)
expect_benchmark_result(bmr)
expect_equal(nrow(get_private(bmr)$.data), nrow(get_private(rr)$.data))
expect_set_equal(bmr$uhashes, rr$uhash)
aggr = bmr$aggregate()
expect_data_table(aggr, nrows = 1)
expect_set_equal(bmr$uhashes, rr$uhash)
})
test_that("discarding model", {
expect_equal(map(as.data.table(rr)$learner, "model"), vector("list", 3L))
})
test_that("inputs are cloned", {
expect_different_address(task, get_private(rr)$.data$data$tasks$task[[1]])
expect_different_address(learner, get_private(rr)$.data$data$learners$learner[[1]])
expect_different_address(resampling, get_private(rr)$.data$data$resamplings$resampling[[1]])
})
test_that("memory footprint", {
expect_equal(nrow(get_private(rr)$.data$data$learners), 1L)
expect_equal(nrow(get_private(rr)$.data$data$tasks), 1L)
expect_equal(nrow(get_private(rr)$.data$data$resamplings), 1L)
})
test_that("predict_type is checked", {
task = tsk("sonar")
learner = lrn("classif.featureless")
resampling = rsmp("cv", folds = 3L)
measure = msr("classif.auc")
rr = resample(task, learner, resampling)
expect_warning(rr$score(measure), "predict type", fixed = TRUE)
expect_warning(rr$aggregate(measure), "predict type", fixed = TRUE)
})
test_that("empty train/predict sets", {
task = tsk("mtcars")
learner = lrn("regr.rpart")
expect_error(learner$train(task, integer()))
learner$train(task)
expect_prediction(learner$predict(task, integer()))
resampling = rsmp("holdout", ratio = 0)
expect_error(resample(task, learner, resampling))
resampling = rsmp("holdout", ratio = 1)
expect_prediction(resample(task, learner, resampling)$predictions()[[1]])
})
test_that("conditions are returned", {
expect_true(all(c("warnings", "errors") %in% names(rr$score(conditions = TRUE))))
})
test_that("save/load roundtrip", {
path = tempfile()
saveRDS(rr, file = path)
rr2 = readRDS(path)
expect_resample_result(rr2)
})
test_that("debug branch", {
task = tsk("iris")
learner = lrn("classif.featureless")
resampling = rsmp("cv", folds = 2)
rr = invoke(resample, task = task, learner = learner, resampling = resampling, .opts = list(mlr3.debug = TRUE))
expect_resample_result(rr)
})
test_that("encapsulation", {
task = tsk("iris")
learner = lrn("classif.debug", error_train = 1)
resampling = rsmp("holdout")
expect_error(resample(task, learner, resampling), "classif.debug->train()")
rr = resample(task, learner, resampling, encapsulate = "evaluate")
expect_data_table(rr$errors, nrows = 1L)
expect_class(rr$learner$fallback, "LearnerClassifFeatureless")
expect_equal(rr$learner$encapsulate[["train"]], "evaluate")
expect_equal(rr$learner$encapsulate[["predict"]], "evaluate")
})
test_that("disable cloning", {
task = tsk("iris")
learner = lrn("classif.featureless")
resampling = rsmp("holdout")
rr = resample(task, learner, resampling, clone = c())
expect_same_address(task, rr$task)
expect_same_address(learner, get_private(rr)$.data$data$learners$learner[[1]])
expect_same_address(resampling, rr$resampling)
expect_identical(task$hash, rr$task$hash)
expect_identical(learner$hash, rr$learner$hash)
expect_true(resampling$is_instantiated)
expect_identical(resampling$hash, rr$resampling$hash)
})
test_that("as_resample_result works for result data", {
task = tsk("iris")
learner = lrn("classif.featureless")
resampling = rsmp("holdout")
rr = resample(task, learner, resampling, clone = c())
result_data = get_private(rr)$.data
rr2 = as_resample_result(result_data)
expect_class(rr2, "ResampleResult")
})
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