Nothing
context("PipeOpSubsample")
test_that("PipeOpSubsample - basic properties", {
op = PipeOpSubsample$new()
task = mlr_tasks$get("iris")
expect_pipeop(op)
train_pipeop(op, inputs = list(task))
predict_pipeop(op, inputs = list(task))
expect_datapreproc_pipeop_class(PipeOpSubsample, task = task,
predict_like_train = FALSE, deterministic_train = FALSE)
})
test_that("PipeOpSubsample works unstratified", {
task = mlr_tasks$get("iris")
po = PipeOpSubsample$new()
tnew = train_pipeop(po, list(task))
expect_true(tnew[[1]]$nrow == ceiling(po$param_set$values$frac * task$nrow))
po = PipeOpSubsample$new()
po$param_set$values$frac = 0.7
tnew = train_pipeop(po, list(task))
expect_true(tnew[[1]]$nrow == ceiling(task$nrow * 0.7))
expect_subset(tnew[[1]]$row_roles$use, task$row_roles$use)
pnew = predict_pipeop(po, list(task))
expect_true(pnew[[1]]$nrow == task$nrow)
expect_equal(pnew[[1]], task)
task = mlr_tasks$get("iris")$filter(1L) # actually has to be an int m(
po = PipeOpSubsample$new()
tnew = train_pipeop(po, list(task))
task = mlr_tasks$get("boston_housing_classic")$filter(1L) # actually has to be an int m(
po = PipeOpSubsample$new()
po$param_set$values = list(stratify = TRUE, frac = 0.6)
expect_error(train_pipeop(po, list(task)))
})
test_that("PipeOpSubsample works stratified", {
task = mlr_tasks$get("iris")
po = PipeOpSubsample$new()
po$param_set$values = list(stratify = TRUE, frac = 0.6, replace = FALSE)
expect_class(po, "PipeOpSubsample")
tnew = train_pipeop(po, list(task))
expect_true(tnew[[1]]$nrow == ceiling(task$nrow * 0.6))
# Proportions as expected
expect_equal(table(tnew[[1]]$data(cols = tnew[[1]]$target_names)),
table(list(Species = rep(c("setosa", "versicolor", "virginica"), 30))))
po = PipeOpSubsample$new()
po$param_set$values = list(stratify = TRUE, frac = 0.6, replace = TRUE)
expect_class(po, "PipeOpSubsample")
tnew = train_pipeop(po, list(task))
expect_true(tnew[[1]]$nrow == ceiling(task$nrow * 0.6))
# Proportions as expected
expect_equal(table(tnew[[1]]$data(cols = tnew[[1]]$target_names)),
table(list(Species = rep(c("setosa", "versicolor", "virginica"), 30))))
po = PipeOpSubsample$new()
po$param_set$values = list(stratify = TRUE, frac = 2, replace = TRUE)
expect_class(po, "PipeOpSubsample")
tnew = train_pipeop(po, list(task))
expect_true(tnew[[1]]$nrow == ceiling(task$nrow * 2))
# Proportions as expected
expect_equal(table(tnew[[1]]$data(cols = tnew[[1]]$target_names)),
table(list(Species = rep(c("setosa", "versicolor", "virginica"), 100))))
})
test_that("task filter utility function", {
task = mlr_tasks$get("iris")
rowidx = as.integer(c(1, 2, 3, 2, 1, 2, 3, 2, 1)) # annoying and unnecessary mlr3 type strictness
tfiltered = task_filter_ex(task$clone(), rowidx)
expect_equal(tfiltered$data(), task$data(rows = rowidx))
task$select(c("Petal.Length", "Petal.Width"))
tfiltered = task_filter_ex(task$clone(), rowidx)
expect_equal(tfiltered$data(), task$data(rows = rowidx))
})
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