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test_that("getHyperPars", {
lrn = makeLearner("classif.rpart")
expect_equal(getHyperPars(lrn), list(xval = 0))
lrn = makeLearner("classif.lda")
named.list = list()
names(named.list) = character(0)
expect_equal(getHyperPars(lrn), named.list)
lrn = makeFilterWrapper(makeLearner("classif.rpart"))
expect_true(setequal(names(getHyperPars(lrn)), c("xval", "fw.method")))
lrn = makeModelMultiplexer(list("classif.rpart", "classif.lda"))
expect_true(setequal(names(getHyperPars(lrn)), c("classif.rpart.xval", "selected.learner")))
lrn = makeLearner("multilabel.rFerns")
expect_true(setequal(getHyperPars(lrn), list()))
lrn = makeMultilabelBinaryRelevanceWrapper("classif.rpart")
expect_true(setequal(getHyperPars(lrn), list(xval = 0)))
# Missing values should not be omitted and printed
lrn = makeLearner("classif.xgboost", missing = NA)
expect_output(print(lrn), "missing=NA")
lrn = makeLearner("regr.xgboost", missing = NA)
expect_output(print(lrn), "missing=NA")
})
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