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context("chains")
test_that("chains", {
lrn1 = makeLearner("classif.rpart", minsplit = 10)
lrn4 = makeFilterWrapper(lrn1, fw.perc = 0.5)
m = train(lrn4, multiclass.task)
p = predict(m, multiclass.task)
perf = performance(p, mmce)
expect_true(perf < 0.1)
outer = makeResampleDesc("Holdout")
inner = makeResampleDesc("CV", iters = 2)
ps = makeParamSet(
makeDiscreteParam(id = "minsplit", values = c(5, 10)),
makeDiscreteParam(id = "fw.perc", values = c(0.8, 1))
)
lrn5 = makeTuneWrapper(lrn4, resampling = inner, par.set = ps,
control = makeTuneControlGrid())
m = train(lrn5, task = multiclass.task)
p = predict(m, task = multiclass.task)
or = m$learner.model$opt.result
expect_equal(length(or$x), 2)
expect_equal(getOptPathLength(or$opt.path), 2 * 2)
perf = performance(p, mmce)
expect_true(perf < 0.1)
})
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