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test_that("Wavelet method are equal to package", {
requirePackagesOrSkip("tsfeatures")
requirePackagesOrSkip("mboost")
requirePackagesOrSkip("FDboost")
requirePackagesOrSkip("stabs")
requirePackagesOrSkip("rucrdtw")
requirePackagesOrSkip("parallel")
requirePackagesOrSkip("wavelets", default.method = "load")
gp = getTaskData(gunpoint.task, subset = seq_len(10), target.extra = TRUE, functionals.as = "matrix")
# Extractor
extr = extractFDAWavelets()
wav.vals = extr$learn(data = gp$data, target = "X1", col = "fd", filter = "la8", boundary = "reflection")
wavelets.gp = extr$reextract(data = gp$data, target = "X1", col = "fd", vals = wav.vals, args = NULL)
# Reference
df = BBmisc::convertRowsToList(gp$data[, "fd", drop = FALSE])
wtdata = t(BBmisc::dapply(df, fun = function(x) {
wt = wavelets::dwt(as.numeric(x), filter = "la8", boundary = "reflection")
unlist(c(wt@W, wt@V[[wt@level]]))
}))
df = as.data.frame(wtdata)
colnames(df) = stringi::stri_paste("wav", "la8", seq_len(ncol(wtdata)), sep = ".")
expect_equal(nrow(wavelets.gp), nrow(gp$data))
expect_equal(wavelets.gp, df)
# Too many vanishing moments (support width) expected
expect_error(extractWaveletFeatures(data = gp, filter = "d10"))
})
test_that("extract and reextract Wavelets", {
requirePackagesOrSkip("wavelets", default.method = "load")
gp.subset = subsetTask(gunpoint.task, features = 1L)
fm = list("fd" = extractFDAWavelets(filter = "haar", boundary = "reflection"))
t3 = extractFDAFeatures(gp.subset, feat.methods = fm)
t4 = reextractFDAFeatures(gp.subset, t3$desc)
expect_equal(getTaskFeatureNames(t3$task), getTaskFeatureNames(t4))
expect_equal(t3$desc$target, getTaskTargetNames(t4))
expect_equal(dim(getTaskData(t3$task)), dim(getTaskData(t4)))
})
test_that("getFDAMultiResFeatures works on data.frame", {
gp = getTaskData(fda.binary.gp.task.small, functionals.as = "matrix")
ngp1 = extractFDAMultiResFeatures()$learn(
data = gp, col = "fd", res.level = 3,
shift = 0.5, seg.lens = NULL)
ngp1 = extractFDAMultiResFeatures()$reextract(data = gp, col = "fd", vals = ngp1)
expect_true(nrow(ngp1) == nrow(gp))
expect_true(ncol(ngp1) == 9L)
ngp2 = extractFDAMultiResFeatures()$learn(
data = gp, col = "fd", seg.lens = c(15, 15),
res.level = 3, shift = 0.5)
ngp2 = extractFDAMultiResFeatures()$reextract(data = gp, col = "fd", vals = ngp2)
expect_true(nrow(ngp2) == nrow(gp))
expect_true(ncol(ngp2) == 18L)
df = getTaskData(fuelsubset.task, functionals.as = "matrix")
# Learn args equal to method args
vals1 = extractFDAMultiResFeatures()$learn(df, col = "UVVIS", res.level = 2L, shift = 0.25)
dfn1 = extractFDAMultiResFeatures()$reextract(df, col = "UVVIS", vals = vals1)
expect_true(nrow(df) == nrow(dfn1))
expect_true(ncol(dfn1) == 5L)
fm = list("UVVIS" = extractFDAMultiResFeatures(res.level = 2L, shift = 0.25))
dfn2 = extractFDAFeatures(df, feat.methods = fm)
expect_true(nrow(df) == nrow(dfn2$data))
expect_true(ncol(dfn2$data) == 5L + 3L)
expect_equal(dfn1, setNames(dfn2$data[, seq_len(5)], colnames(dfn1)))
extr = extractFDAMultiResFeatures()
vals3 = extr$learn(df, col = "NIR", res.level = 3L, shift = 0.5, seg.lens = c(100L, 131L))
dfn3 = extr$reextract(df, col = "NIR", vals = vals3)
expect_true(nrow(df) == nrow(dfn3))
expect_true(ncol(dfn3) == 19L)
extr = extractFDAMultiResFeatures()
vals4 = extr$learn(df, col = "NIR", res.level = 1L, shift = 0.2, seg.lens = 231L)
dfn4 = extr$reextract(df, col = "NIR", vals = vals4)
expect_true(nrow(df) == nrow(dfn4))
expect_true(ncol(dfn4) == 1L)
})
test_that("extract and reextract MultiRes", {
gp.subset = subsetTask(gunpoint.task, subset = 1:20, features = 1L)
fm = list("fd" = extractFDAMultiResFeatures(3L, 0.4))
t3 = extractFDAFeatures(gp.subset, feat.methods = fm)
t4 = reextractFDAFeatures(gp.subset, t3$desc)
expect_equal(getTaskFeatureNames(t3$task), getTaskFeatureNames(t4))
expect_equal(t3$desc$target, getTaskTargetNames(t4))
expect_equal(dim(getTaskData(t3$task)), dim(getTaskData(t4)))
expect_equal(t3$task$task.desc$n.feat["numerics"], c(numerics = 12L))
})
test_that("extractFPCAFeatures is equivalent to prcomp", {
gp = getTaskData(gunpoint.task, subset = 1:10, target.extra = TRUE, functionals.as = "matrix")
extr = extractFDAFPCA()
fpca.vals = extr$learn(data = gp$data, target = "X1", col = "fd", rank. = 5L)
fpca.df = extr$reextract(data = gp$data, target = "X1", col = "fd", vals = fpca.vals)
expect_true((nrow(gp$data) == nrow(fpca.df)))
expect_true((ncol(fpca.df) == 5L))
expect_match(names(fpca.df), regexp = "[FPCA]")
# Is it equivalent to the mlr version?
gp.mat = gp$data$fd
fpca.df2 = predict(prcomp(gp.mat, rank. = 5L), gp.mat)
expect_true((nrow(gp.mat) == nrow(fpca.df2)))
expect_true((ncol(fpca.df2) == 5L))
expect_equal(ignore_attr = TRUE, fpca.df, data.frame(fpca.df2))
})
test_that("extract and reextract FPCA", {
gp.subset = subsetTask(gunpoint.task, subset = 1:20, features = 1L)
fm = list("fd" = extractFDAFPCA(rank. = 5))
t3 = extractFDAFeatures(gp.subset, feat.methods = fm)
t4 = reextractFDAFeatures(gp.subset, t3$desc)
expect_equal(getTaskFeatureNames(t3$task), getTaskFeatureNames(t4))
expect_equal(t3$desc$target, getTaskTargetNames(t4))
expect_equal(dim(getTaskData(t3$task)), dim(getTaskData(t4)))
expect_equal(t3$task$task.desc$n.feat["numerics"], c(numerics = 5L))
})
test_that("Fourier equal to expected", {
t = seq(from = 0, to = 1, length.out = 501)
data = data.frame(a = 1:2)
data$fd = matrix(c(
.6 * cos(2 * pi * t) + .3 * cos(4 * 2 * pi * t + pi / 4),
.8 * cos(2 * pi * t) + .1 * cos(4 * 2 * pi * t + pi / 4)
), nrow = 2, byrow = TRUE)
data$a = NULL
extr = extractFDAFourier()
fourier.vals = extr$learn(data = data, col = "fd", trafo.coeff = "phase")
fourier.gp = extr$reextract(data = data, col = "fd", vals = fourier.vals)
# Phase in range [-180; 180]
expect_true(all(fourier.gp >= -180 & fourier.gp <= 180))
# ~ pi/4 shift for 4th component
expect_true(all(abs(fourier.gp[, 5] - 45) < 10))
expect_true(all(abs(fourier.gp[, 2]) < 10))
expect_true(all(dim(fourier.gp) == c(2, 501)))
fourier.vals = extr$learn(data = data, col = "fd", trafo.coeff = "amplitude")
fourier.gp = extr$reextract(data = data, col = "fd", vals = fourier.vals)
# Amplitude sqrt(Re^2 + Im^2) >= 0
expect_true(all(fourier.gp >= 0))
expect_true(all(abs(fourier.gp[, 2] - c(0.6, 0.8)) < 0.01))
expect_true(all(abs(fourier.gp[, 5] - c(0.3, 0.1)) < 0.01))
expect_true(all(dim(fourier.gp) == c(2, 501)))
# Can not have factors
gp2 = data.frame(v1 = t(1:4), X1 = as.factor(1))
expect_error(extractFourierFeatures(data = gp2, trafo.coeff = "amplitude"))
})
test_that("tsfeatures works", {
gp1 = getTaskData(fuelsubset.task, functionals.as = "matrix")[1:30, ]
lrn = extractFDATsfeatures()$learn
gpvals = lrn(data = gp1, col = "UVVIS")
gpfeats = extractFDATsfeatures()$reextract(data = gp1, col = "UVVIS", vals = gpvals)
expect_equal(nrow(gpfeats), nrow(gp1))
extr = extractFDAFeatures(subsetTask(fuelsubset.task, subset = 1:30, features = 2),
feat.methods = list("UVVIS" = extractFDATsfeatures()))
expect_true(ncol(getTaskData(extr$task)) == 32L)
reextr = reextractFDAFeatures(subsetTask(fuelsubset.task, subset = 31:35, features = 2), extr$desc)
expect_true(ncol(getTaskData(reextr)) == 32L)
})
test_that("extraction returns correct cols", {
requirePackagesOrSkip("tsfeatures")
extr = extractFDAFeatures(subsetTask(fuelsubset.task, subset = 1:7),
feat.methods = list("UVVIS" = extractFDATsfeatures()))
reextr = reextractFDAFeatures(subsetTask(fuelsubset.task, subset = 8:14), extr$desc)
expect_equal(extr$task$task.desc$n.feat, reextr$task.desc$n.feat)
expect_equal(
colnames(getTaskData(extr$task, functionals.as = "matrix")),
colnames(getTaskData(reextr, functionals.as = "matrix")))
})
test_that("dtw extract works", {
requirePackagesOrSkip("rucrdtw")
task = subsetTask(fuelsubset.task, features = "UVVIS")
daf = getTaskData(task, functionals.as = "matrix")
daf = daf$UVVIS
fmethods = list("UVVIS" = extractFDADTWKernel())
res = extractFDAFeatures(fuelsubset.task, feat.methods = fmethods)
# check output data
df = getTaskData(res$task, functionals.as = "matrix")
expect_s3_class(df, "data.frame")
expect_equal(nrow(df), 129)
expect_equal(ncol(df), 9)
})
test_that("extractBsignal features", {
requirePackagesOrSkip("FDboost")
methods = list("UVVIS" = extractFDABsignal(), "NIR" = extractFDABsignal())
t = extractFDAFeatures(fuelsubset.task, feat.methods = methods)
t2 = reextractFDAFeatures(fuelsubset.task, t$desc)
# check output data
df = getTaskData(t$task)
expect_s3_class(df, "data.frame")
expect_equal(nrow(df), 129L)
expect_equal(ncol(df), 30L)
})
test_that("extractFDAFeaturesDTW", {
requirePackagesOrSkip("rucrdtw")
methods = list("UVVIS" = extractFDADTWKernel(), "NIR" = extractFDADTWKernel())
t = extractFDAFeatures(fuelsubset.task, feat.methods = methods)
# check output data
df = getTaskData(t$task)
expect_s3_class(df, "data.frame")
expect_equal(nrow(df), 129)
})
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