context("FilterWrapper")
test_that("FilterWrapper", {
lrn1 = makeLearner("classif.lda")
lrn2 = makeFilterWrapper(lrn1, fw.method = "chi.squared", fw.perc = 1)
m = train(lrn2, binaryclass.task)
expect_true(!inherits(m, "FailureModel"))
expect_equal(m$features, getTaskFeatureNames(binaryclass.task))
lrn2 = makeFilterWrapper(lrn1, fw.method = "chi.squared", fw.abs = 0L)
m = train(lrn2, binaryclass.task)
expect_equal(getLeafModel(m)$features, character(0))
expect_true(inherits(getLeafModel(m)$learner.model, "NoFeaturesModel"))
lrn2 = makeFilterWrapper(lrn1, fw.method = "chi.squared", fw.perc = 0.1)
res = makeResampleDesc("CV", iters = 2)
r = resample(lrn2, binaryclass.task, res)
expect_true(!any(is.na(r$aggr)))
expect_subset(r$extract[[1]][[1]], getTaskFeatureNames(binaryclass.task))
})
test_that("FilterWrapper univariate (issue #516)", {
lrn1 = makeLearner("classif.rpart")
lrn2 = makeFilterWrapper(lrn1, fw.method = "univariate.model.score", fw.perc = 1)
m = train(lrn2, binaryclass.task)
expect_true(!inherits(m, "FailureModel"))
expect_equal(m$features, getTaskFeatureNames(binaryclass.task))
})
test_that("Filterwrapper permutation.importance (issue #814)", {
lrn1 = makeLearner("classif.rpart")
lrn2 = makeFilterWrapper(lrn1, fw.method = "permutation.importance", imp.learner = "classif.rpart", fw.perc = 1L, nmc = 1L)
m = train(lrn2, binaryclass.task)
res = makeResampleDesc("CV", iters = 2)
r = resample(lrn2, binaryclass.task, res)
expect_true(!any(is.na(r$aggr)))
expect_subset(r$extract[[1]][[1]], getTaskFeatureNames(binaryclass.task))
})
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