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test_that("BaseWrapper", {
lrn1 = makeLearner("classif.rpart", minsplit = 2L)
ps = makeParamSet(makeNumericLearnerParam("foo"))
pv = list(foo = 3)
lrn2 = makeBaseWrapper(id = "foo", lrn1$type, lrn1, par.set = ps, par.vals = pv,
learner.subclass = "mywrapper", model.subclass = "mymodel")
expect_equal(getHyperPars(lrn2), list(xval = 0L, minsplit = 2L, foo = 3))
lrn2 = setHyperPars(lrn2, minsplit = 11)
expect_equal(getHyperPars(lrn2), list(xval = 0L, minsplit = 11L, foo = 3))
lrn2 = setHyperPars(lrn2, foo = 12)
expect_equal(getHyperPars(lrn2), list(xval = 0L, minsplit = 11L, foo = 12))
lrn2 = setHyperPars(lrn2, foo = 12)
expect_equal(getHyperPars(lrn2), list(xval = 0L, minsplit = 11L, foo = 12))
lrn2.rm = removeHyperPars(lrn2, names(getHyperPars(lrn2)))
expect_equal(length(getHyperPars(lrn2.rm)), 0)
lrn1 = makeOversampleWrapper(makeFilterWrapper(lrn1, fw.perc = 0.5), osw.rate = 1)
lrn2 = makeBaseWrapper(id = "foo", lrn1$type, lrn1, par.set = ps, par.vals = pv,
learner.subclass = "mywrapper", model.subclass = "mymodel")
lrn2.rm = removeHyperPars(lrn2, names(getHyperPars(lrn2)))
expect_equal(length(getHyperPars(lrn2.rm)), 0)
})
test_that("Joint model performance estimation, tuning, and model performance", {
lrn = makeLearner("classif.ksvm", predict.type = "prob")
lrn2 = makeTuneWrapper(
learner = lrn,
par.set = makeParamSet(
makeDiscreteParam("C", values = 2^(-2:2)),
makeDiscreteParam("sigma", values = 2^(-2:2))
),
measures = list(auc, acc),
control = makeTuneControlRandom(maxit = 3L),
resampling = makeResampleDesc(method = "Holdout")
)
lrn3 = makeFeatSelWrapper(
learner = lrn2,
measures = list(auc, acc),
control = makeFeatSelControlRandom(maxit = 3L),
resampling = makeResampleDesc(method = "Holdout")
)
bmrk = benchmark(lrn3, pid.task, makeResampleDesc(method = "Holdout"), measures = getDefaultMeasure(pid.task))
expect_s3_class(bmrk, "BenchmarkResult")
})
test_that("Error when wrapping tune wrapper around another optimization wrapper", {
expect_error({
lrn = makeLearner("classif.ksvm", predict.type = "prob")
lrn2 = makeFeatSelWrapper(
learner = lrn,
measures = list(auc, acc),
control = makeFeatSelControlRandom(maxit = 3L),
resampling = makeResampleDesc(method = "Holdout")
)
lrn3 = makeTuneWrapper(
learner = lrn2,
par.set = makeParamSet(
makeDiscreteParam("C", values = 2^(-2:2)),
makeDiscreteParam("sigma", values = 2^(-2:2))
),
measures = list(auc, acc),
control = makeTuneControlRandom(maxit = 3L),
resampling = makeResampleDesc(method = "Holdout")
)
bmrk = benchmark(lrn3, pid.task, resampling, measures = getDefaultMeasure(pid.task))
}, "Cannot wrap a tuning wrapper around another optimization wrapper!")
})
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