1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
|
# FIXME: This is a copy of knn to be used for kernel, because no unittest
# for kernel exists.
test_that("classif_fdausc.kernel 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"]]
a1 = suppressWarnings(fda.usc::classif.kernel(glearn, mlearn))
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.kernel")
fdata = makeFunctionalData(ph, fd.features = NULL, exclude.cols = "label")
ftest = makeFunctionalData(phtst, fd.features = NULL, exclude.cols = "label")
task = makeClassifTask(data = fdata, target = "label")
m = 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))
})
test_that("predicttype prob for fda.usc", {
requirePackagesOrSkip("fda.usc", default.method = "load")
lrn = makeLearner("classif.fdausc.kernel", predict.type = "prob")
m = train(lrn, fda.binary.gp.task)
cp = predict(m, newdata = getTaskData(fda.binary.gp.task, target.extra = TRUE,
functionals.as = "matrix")$data)
expect_equal(class(cp)[1], "PredictionClassif")
})
test_that("resampling fdausc.kernel", {
requirePackagesOrSkip("fda.usc", default.method = "load")
lrn = makeLearner("classif.fdausc.kernel", par.vals = list(trim = 0.5), predict.type = "prob")
r = resample(lrn, fda.binary.gp.task.small, cv2)
expect_class(r, "ResampleResult")
})
|
