Nothing
context("PipeOpClassBalancing")
test_that("PipeOpClassBalancing - basic properties", {
op = PipeOpClassBalancing$new()
task = mlr_tasks$get("iris")
expect_pipeop(op)
train_pipeop(op, inputs = list(task))
predict_pipeop(op, inputs = list(task))
expect_datapreproc_pipeop_class(PipeOpClassBalancing, task = task,
predict_like_train = FALSE, deterministic_train = FALSE)
})
test_that("PipeOpClassBalancing", {
op = PipeOpClassBalancing$new()
task = mlr_tasks$get("pima")
op$param_set$values = list(ratio = 0.5, reference = "major", adjust = "major", shuffle = TRUE)
nt = op$train(list(task))[[1L]]
expect_true(table(task$truth())[["pos"]] == table(nt$truth())[["pos"]])
expect_true(table(task$truth())[["neg"]] > table(nt$truth())[["neg"]])
op$param_set$values = list(ratio = 0.5, reference = "major", adjust = "minor", shuffle = TRUE)
nt = op$train(list(task))[[1L]]
expect_true(table(task$truth())[["pos"]] > table(nt$truth())[["pos"]])
expect_true(table(task$truth())[["neg"]] == table(nt$truth())[["neg"]])
op$param_set$values = list(ratio = 0.5, reference = "major", adjust = "all", shuffle = TRUE)
nt = op$train(list(task))[[1L]]
expect_true(table(task$truth())[["pos"]] > table(nt$truth())[["pos"]])
expect_true(table(task$truth())[["neg"]] > table(nt$truth())[["neg"]])
})
test_that("PipeOpClassBalancing: rate and multiple classes", {
task = mlr_tasks$get("zoo")
op = PipeOpClassBalancing$new()
intbl = table(task$truth())
op$param_set$values$reference = "nonmajor"
op$param_set$values$adjust = "downsample"
nt = op$train(list(task))[[1L]]
outtbl = intbl
outtbl[outtbl > 10] = 10
expect_equal(table(nt$truth()), outtbl)
op$param_set$values$reference = "nonmajor"
op$param_set$values$adjust = "upsample"
nt = op$train(list(task))[[1L]]
outtbl = intbl
outtbl[outtbl < 10] = 10
expect_equal(table(nt$truth()), outtbl)
op$param_set$values$reference = "nonmajor"
op$param_set$values$adjust = "major"
nt = op$train(list(task))[[1L]]
outtbl = intbl
outtbl[which.max(outtbl)] = 10
expect_equal(table(nt$truth()), outtbl)
op$param_set$values$reference = "nonmajor"
op$param_set$values$adjust = "minor"
nt = op$train(list(task))[[1L]]
outtbl = intbl
outtbl[which.min(outtbl)] = 10
expect_equal(table(nt$truth()), outtbl)
op$param_set$values$reference = "one"
op$param_set$values$adjust = "all"
nt = op$train(list(task))[[1L]]
outtbl = intbl
outtbl[TRUE] = 1
expect_equal(table(nt$truth()), outtbl)
})
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