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test_that("over and undersample works", {
y = binaryclass.df[, binaryclass.target]
tab1 = table(y)
task = oversample(binaryclass.task, rate = 2)
df = getTaskData(task)
tab2 = table(df[, binaryclass.target])
expect_equal(tab2["M"], tab1["M"])
expect_equal(tab2["R"], tab1["R"] * 2)
task = undersample(binaryclass.task, rate = 0.5)
df = getTaskData(task)
tab2 = table(df[, binaryclass.target])
expect_equal(tab2["M"], round(tab1["M"] / 2))
expect_equal(tab2["R"], tab1["R"])
})
test_that("over and undersample wrapper", {
rdesc = makeResampleDesc("CV", iters = 2)
lrn1 = makeLearner("classif.rpart")
lrn2 = makeUndersampleWrapper(lrn1, usw.rate = 0.5)
r = resample(lrn2, binaryclass.task, rdesc)
expect_true(!is.na(r$aggr))
lrn2 = makeOversampleWrapper(lrn1, osw.rate = 1.5)
r = resample(lrn2, binaryclass.task, rdesc)
expect_true(!is.na(r$aggr))
})
test_that("over and undersample arg check works", {
task = makeClassifTask(data = multiclass.df, target = multiclass.target)
expect_error(undersample(task, rate = 0.5), "binary")
expect_error(oversample(task, rate = 0.5), "binary")
})
test_that("over and undersample works with weights", {
task = makeClassifTask(data = binaryclass.df, target = binaryclass.target,
weights = seq_len(nrow(binaryclass.df)))
task2 = undersample(task, rate = 0.5)
expect_true(length(task2$weights) < length(task$weights))
expect_true(all(task2$weights %in% task$weights))
})
test_that("oversampling keeps all min / max obs", {
y = binaryclass.df[, binaryclass.target]
z = getMinMaxClass(y)
new.inds = sampleBinaryClass(y, 1.05, cl = z$min.name, resample.other.class = FALSE)
expect_true(setequal(intersect(z$min.inds, new.inds), z$min.inds))
})
test_that("control which class gets over or under sampled", {
set.seed(getOption("mlr.debug.seed"))
# check function oversample(), undersample()
y = binaryclass.df[, binaryclass.target]
tab1 = table(y)
z = getMinMaxClass(y)
task = oversample(binaryclass.task, rate = 2, cl = z$max.name)
df = getTaskData(task)
tab2 = table(df[, binaryclass.target])
expect_equal(tab2["R"], tab1["R"])
expect_equal(tab2["M"], tab1["M"] * 2)
task = undersample(binaryclass.task, rate = 0.5, cl = z$min.name)
df = getTaskData(task)
tab2 = table(df[, binaryclass.target])
expect_equal(tab2["R"], round(tab1["R"] / 2))
expect_equal(tab2["M"], tab1["M"])
# check over- and undersample-wrapper
z = getMinMaxClass(binaryclass.df[, binaryclass.target])
rdesc = makeResampleDesc("CV", iters = 2)
lrn1 = makeLearner("classif.rpart")
lrn2 = makeUndersampleWrapper(lrn1, usw.rate = 0.1, usw.cl = z$min.name)
r = resample(lrn2, binaryclass.task, rdesc)
expect_true(!is.na(r$aggr))
lrn2 = makeOversampleWrapper(lrn1, osw.rate = 1.5, osw.cl = z$max.name)
r = resample(lrn2, binaryclass.task, rdesc)
expect_true(!is.na(r$aggr))
})
test_that("training performance works as expected (#1357)", {
num = makeMeasure(id = "num", minimize = FALSE,
properties = c("classif", "classif.multi", "req.pred", "req.truth"),
name = "Number",
fun = function(task, model, pred, feats, extra.args) {
length(pred$data$response)
}
)
y = binaryclass.df[, binaryclass.target]
z = getMinMaxClass(y)
rdesc = makeResampleDesc("Holdout", split = .5, predict = "both")
lrn = makeUndersampleWrapper("classif.rpart", usw.rate = 0.1, usw.cl = z$max.name)
r = resample(lrn, binaryclass.task, rdesc, measures = list(setAggregation(num, train.mean)))
expect_lt(r$measures.train$num, getTaskSize(binaryclass.task) * 0.5 - 1)
lrn = makeOversampleWrapper("classif.rpart", osw.rate = 2, osw.cl = z$max.name)
r = resample(lrn, binaryclass.task, rdesc, measures = list(setAggregation(num, train.mean)))
expect_gt(r$measures.train$num, getTaskSize(binaryclass.task) * 0.5 + 1)
})
test_that("Wrapper works with weights, we had issue #2047", {
n = nrow(binaryclass.df)
w = 1:n
task = makeClassifTask(data = binaryclass.df, target = binaryclass.target, weights = w)
b = table(getTaskTargets(task))
# weights from task, use all
lrn = makeOversampleWrapper("classif.__mlrmocklearners__6", osw.rate = 1)
m = train(lrn, task)
expect_set_equal(getLearnerModel(m, more.unwrap = TRUE)$weights, w)
lrn = makeUndersampleWrapper("classif.__mlrmocklearners__6", usw.rate = 1)
m = train(lrn, task)
expect_set_equal(getLearnerModel(m, more.unwrap = TRUE)$weights, w)
# weights from task, really sample
lrn = makeOversampleWrapper("classif.__mlrmocklearners__6", osw.rate = 2)
m = train(lrn, task)
u = getLearnerModel(m, more.unwrap = TRUE)$weights
expect_equal(length(u), min(b) * 2 + max(b))
expect_subset(u, w)
lrn = makeUndersampleWrapper("classif.__mlrmocklearners__6", usw.rate = 0.5)
m = train(lrn, task)
u = getLearnerModel(m, more.unwrap = TRUE)$weights
expect_equal(length(u), round(max(b) / 2) + min(b))
expect_subset(u, w)
# weights from train
subset = c(head(which(getTaskTargets(task) == names(b)[1]), 5), head(which(getTaskTargets(task) == names(b)[2]), 5))
lrn = makeOversampleWrapper("classif.__mlrmocklearners__6", osw.rate = 2)
m = train(lrn, task, subset = subset, weights = 1:10)
u = getLearnerModel(m, more.unwrap = TRUE)$weights
expect_equal(length(u), 15)
expect_subset(u, 1:10)
lrn = makeUndersampleWrapper("classif.__mlrmocklearners__6", usw.rate = 2 / 5)
m = train(lrn, task, subset = subset, weights = 1:10)
u = getLearnerModel(m, more.unwrap = TRUE)$weights
expect_equal(length(u), 7)
expect_subset(u, 1:10)
})
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