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test_that("BaseEnsemble", {
bl1 = makeLearner("classif.rpart", minsplit = 2L, id = "a")
bl2 = makeLearner("classif.ksvm", C = 2, id = "b")
ps = makeParamSet(makeNumericLearnerParam("foo"))
pv = list(foo = 3)
be = makeBaseEnsemble(id = "foo", base.learners = list(bl1, bl2), par.set = ps, par.vals = pv,
cl = "mywrapper")
expect_true(setequal(getHyperPars(be), list(a.xval = 0L, a.minsplit = 2L,
b.fit = FALSE, b.C = 2, foo = 3)))
be = setHyperPars(be, a.minsplit = 11)
expect_true(setequal(getHyperPars(be), list(a.xval = 0L, a.minsplit = 11L,
b.fit = FALSE, b.C = 2, foo = 3)))
be = setHyperPars(be, foo = 12)
expect_true(setequal(getHyperPars(be), list(a.xval = 0L, a.minsplit = 11L,
b.fit = FALSE, b.C = 2, foo = 12)))
# check removing hyperpars
be1 = removeHyperPars(be, names(getHyperPars(be)))
expect_true(length(getHyperPars(be1)) == 0)
bl1 = be
bl2 = makeOversampleWrapper(makeFilterWrapper(bl2, fw.perc = 0.5), osw.rate = 1)
ps = makeParamSet(makeNumericLearnerParam("foo"))
pv = list(foo = 3)
be = makeBaseEnsemble(id = "foo", base.learners = list(bl1, bl2), par.set = ps, par.vals = pv,
cl = "mywrapper")
be1 = removeHyperPars(be, names(getHyperPars(be)))
# check that we get error if predict types are unequal
bl1 = makeLearner("classif.rpart", predict.type = "prob")
bl2 = makeLearner("classif.ksvm", predict.type = "response")
expect_error(makeBaseEnsemble(id = "foo", base.learners = list(bl1, bl2),
par.set = ps, par.vals = pv, cl = "mywrapper"), "predict.type")
# check getHyperPars when we have multiple wrappers
bl1 = makeLearner("classif.rpart", minsplit = 2L, id = "rpart")
bl2 = makeLearner("classif.ksvm", C = 2, id = "ksvm")
# now make a wrapper around bl2 (ksvm)
bl2 = makeOversampleWrapper(makeFilterWrapper(bl2, fw.perc = 0.5), osw.rate = 1)
be = makeBaseEnsemble(id = "foo", base.learners = list(bl1, bl2), cl = "mywrapper")
expect_output(print(be), "mywrapper")
expect_true(setequal(getHyperPars(be),
list(rpart.xval = 0L, rpart.minsplit = 2L,
ksvm.filtered.oversampled.fit = FALSE,
ksvm.filtered.oversampled.C = 2,
ksvm.filtered.oversampled.fw.method = "FSelectorRcpp_information.gain",
ksvm.filtered.oversampled.fw.perc = 0.5,
ksvm.filtered.oversampled.osw.rate = 1)))
# check removing hyperpars
be.rm = removeHyperPars(be, names(getHyperPars(be)))
expect_true(length(getHyperPars(be.rm)) == 0)
# check setPredictType
be.pt = setPredictType(be, predict.type = "prob")
expect_equal(be.pt$predict.type, "prob")
expect_equal(lapply(be.pt$base.learners, function(x) x$predict.type), list(rpart = "prob", ksvm.filtered.oversampled = "prob"))
be.pt = setPredictType(be, predict.type = "response")
expect_equal(be.pt$predict.type, "response")
expect_equal(lapply(be.pt$base.learners, function(x) x$predict.type), list(rpart = "response", ksvm.filtered.oversampled = "response"))
})
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