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test_that("classif_fdausc.np behaves like original api", {
requirePackagesOrSkip("fda.usc", default.method = "load")
data(phoneme, package = "fda.usc")
mlearn = phoneme[["learn"]]
# Use only 10 obs. for 5 classes, as knn training is really slow
index = c(1:10, 50:60, 100:110, 150:160, 200:210)
mlearn$data = mlearn$data[index, ]
glearn = phoneme[["classlearn"]][index]
mtest = phoneme[["test"]]
gtest = phoneme[["classtest"]]
# suppressing "executing %dopar% sequentially: no parallel backend registered"
a1 = suppressWarnings(fda.usc::classif.np(glearn, mlearn))
# Fix bug in package. The changed slot looks different when called with
# `fda.usc::classif.np()` than just `classif.np()`
a1$C[[1]] = quote(classif.np)
# newdat = list("x"=mtest)
p1 = predict(a1, mtest)
p2 = predict(a1, mlearn)
ph = as.data.frame(mlearn$data)
ph[, "label"] = glearn
phtst = as.data.frame(mtest$data)
phtst[, "label"] = gtest
lrn = makeLearner("classif.fdausc.np")
fdata = makeFunctionalData(ph, fd.features = NULL, exclude.cols = "label")
ftest = makeFunctionalData(phtst, fd.features = NULL, exclude.cols = "label")
task = makeClassifTask(data = fdata, target = "label")
# suppressing "executing %dopar% sequentially: no parallel backend registered"
m = suppressWarnings(train(lrn, task))
cp = predict(m, newdata = ftest)
cp = unlist(cp$data$response, use.names = FALSE)
cp2 = predict(m, newdata = fdata)
cp2 = unlist(cp2$data$response, use.names = FALSE)
# check if the output from the original API matches the mlr learner's output
expect_equal(as.character(cp2), as.character(p2))
expect_equal(as.character(cp), as.character(p1))
})
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