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test_that("downsample", {
down.tsk = downsample(multiclass.task, perc = 1 / 3)
expect_equal(getTaskSize(down.tsk), 50L)
rsm.methods = c("Bootstrap", "Subsample", "Holdout")
for (rsm.method in rsm.methods) {
rin = makeResampleInstance(rsm.method, task = binaryclass.task)
rin2 = downsample(rin, perc = 0.5)
sapply(seq_along(rin$train.inds), function(i) {
expect_equal(
length(rin2$train.inds[[i]]),
length(rin$train.inds[[i]]) / 2
)
})
}
})
test_that("downsample wrapper", {
# test it with classif
rdesc = makeResampleDesc("CV", iters = 2)
lrn = makeDownsampleWrapper("classif.rpart", dw.perc = 0.5)
r = resample(lrn, binaryclass.task, rdesc)
expect_true(!is.na(r$aggr))
# test it with regr
rdesc = makeResampleDesc("CV", iters = 2)
lrn = makeDownsampleWrapper("regr.rpart", dw.perc = 0.5)
r = resample(lrn, regr.task, rdesc)
expect_true(!is.na(r$aggr))
})
test_that("downsample wrapper works with xgboost, we had issue #492", {
skip_if_not_installed("xgboost") # xgboost broken on CRAN, they cannot run our tests
rdesc = makeResampleDesc("CV", iters = 2)
lrn = makeDownsampleWrapper("classif.xgboost", dw.perc = 0.5)
expect_output(print(lrn), "down")
r = resample(lrn, binaryclass.task, rdesc)
expect_true(!is.na(r$aggr))
})
test_that("downsample wrapper works with weights, we had issue #838", {
n = nrow(regr.df)
w = 1:n
task = makeRegrTask(data = regr.df, target = regr.target, weights = w)
# weights from task, use all
lrn = makeDownsampleWrapper("regr.__mlrmocklearners__6", dw.perc = 1)
m = train(lrn, task)
expect_set_equal(getLearnerModel(m, more.unwrap = TRUE)$weights, w)
# weights from task, really downsample
lrn = makeDownsampleWrapper("regr.__mlrmocklearners__6", dw.perc = 0.5)
m = train(lrn, task)
u = getLearnerModel(m, more.unwrap = TRUE)$weights
expect_equal(length(u), n / 2)
expect_subset(u, w)
# weights from train
lrn = makeDownsampleWrapper("regr.__mlrmocklearners__6", dw.perc = 0.5)
m = train(lrn, task, subset = 11:20, weights = 1:10)
u = getLearnerModel(m, more.unwrap = TRUE)$weights
expect_equal(length(u), 5)
expect_subset(u, 1:10)
})
test_that("training performance works as expected (#1357)", {
num = makeMeasure(id = "num", minimize = FALSE,
properties = c("classif", "classif.multi", "req.pred", "req.truth"),
name = "Number",
fun = function(task, model, pred, feats, extra.args) {
length(pred$data$response)
}
)
rdesc = makeResampleDesc("Holdout", predict = "both")
lrn = makeDownsampleWrapper("classif.rpart", dw.perc = 0.1)
r = resample(lrn, multiclass.task, rdesc, measures = list(setAggregation(num, train.mean)))
expect_lte(r$measures.train$num, getTaskSize(multiclass.task) * 0.1)
})
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