context("PreprocWrapperPCA")
test_that("PreprocWrapperPCA", {
lrn1 = makeLearner("classif.rpart", minsplit=10)
lrn2 = makePreprocWrapperPCA(lrn1)
m = train(lrn2, multiclass.task)
p = predict(m, multiclass.task)
perf = performance(p, mmce)
expect_true(perf < 0.1)
})
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 = makePreprocWrapperPCA(lrn1)
m = train(lrn2, task)
p = predict(m, task)
perf = performance(p, mmce)
expect_equal(getLeafModel(m)$features, c("x1", "PC1", "PC2"))
expect_true(!is.na(perf))
data = data.frame(x1=f(), x2=runif(100), y=f())
task = makeClassifTask(data=data, target="y")
lrn1 = makeLearner("classif.multinom")
lrn2 = makePreprocWrapperPCA(lrn1)
m = train(lrn2, task)
p = predict(m, task)
perf = performance(p, mmce)
expect_equal(getLeafModel(m)$features, c("x1", "PC1"))
expect_true(!is.na(perf))
data = data.frame(x1=f(), x2=f(), y=f())
task = makeClassifTask(data=data, target="y")
lrn1 = makeLearner("classif.multinom")
lrn2 = makePreprocWrapperPCA(lrn1)
m = train(lrn2, task)
p = predict(m, task)
perf = performance(p, mmce)
expect_equal(getLeafModel(m)$features, c("x1", "x2"))
expect_true(!is.na(perf))
})
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