test_that("PreprocWrapperRemoveOutliers", {
lrn1 = makeLearner("classif.rpart", minsplit = 10)
lrn2 = makePreprocWrapperRemoveOutliers(lrn1, ro.alpha = 1)
m = train(lrn2, multiclass.task)
p = predict(m, multiclass.task)
perf = performance(p, mmce)
expect_equal(m$task.desc$size, 150)
expect_true(perf < 0.1)
lrn2 = makePreprocWrapperRemoveOutliers(lrn1, ro.alpha = 1)
lrn2 = setHyperPars(lrn2, ro.alpha = 0.5)
m = train(lrn2, multiclass.task)
p = predict(m, multiclass.task)
expect_true(getLeafModel(m)$task.desc$size < 150)
})
test_that("PreprocWrapperPCA works with factors", {
f = function() as.factor(sample(1:2, 100, replace = TRUE))
data = data.frame(x1 = f(), x2 = runif(100), x3 = runif(100), y = f())
task = makeClassifTask(data = data, target = "y")
lrn1 = makeLearner("classif.multinom")
lrn2 = makePreprocWrapperRemoveOutliers(lrn1)
m = train(lrn2, task)
p = predict(m, task)
perf = performance(p, mmce)
expect_true(!is.na(perf))
f = function() as.factor(sample(1:2, 100, replace = TRUE))
data = data.frame(x1 = f(), x2 = runif(100), y = f())
task = makeClassifTask(data = data, target = "y")
lrn1 = makeLearner("classif.multinom")
lrn2 = makePreprocWrapperRemoveOutliers(lrn1)
m = train(lrn2, task)
p = predict(m, task)
perf = performance(p, mmce)
expect_true(!is.na(perf))
f = function() as.factor(sample(1:2, 100, replace = TRUE))
data = data.frame(x1 = f(), x2 = f(), y = f())
task = makeClassifTask(data = data, target = "y")
lrn1 = makeLearner("classif.multinom")
lrn2 = makePreprocWrapperRemoveOutliers(lrn1)
m = train(lrn2, task)
p = predict(m, task)
perf = performance(p, mmce)
expect_true(!is.na(perf))
})
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