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test_that("extractFDAFeaturesWrapper", {
methods = list("UVVIS" = extractFDAMultiResFeatures(), "NIR" = extractFDAFourier())
lrn = makeExtractFDAFeatsWrapper("regr.rpart", feat.methods = methods)
expect_class(lrn, "extractFDAFeatsWrapper")
mod = train(lrn, fuelsubset.task)
expect_class(mod, "PreprocModel")
prd = predict(mod, fuelsubset.task)
expect_data_frame(prd$data)
expect_numeric(prd$data$response, lower = 0, upper = Inf)
res = resample(lrn, fuelsubset.task, cv2)
expect_class(res, "ResampleResult")
})
test_that("extractFDAFeaturesWrapper ParSet Works", {
methods = list("NIR" = extractFDAFourier())
lrn = makeExtractFDAFeatsWrapper("regr.rpart", feat.methods = methods)
ps = getLearnerParamSet(lrn)
# Check whether all Ids are contained in the resulting param set
ps.rpart = getLearnerParamSet(makeLearner("regr.rpart"))
expect_subset(getParamIds(ps.rpart), getParamIds(ps))
expect_subset(getParamIds(methods$NIR$par.set), getParamIds(ps))
ps2 = makeParamSet(
makeDiscreteParam("trafo.coeff", values = c("phase", "amplitude")),
makeIntegerParam("minsplit", lower = 1, upper = 30))
mod = tuneParams(lrn, subsetTask(fuelsubset.task, features = 3), cv2, mse, ps2, makeTuneControlRandom(maxit = 2))
})
test_that("extractFDAFeaturesWrapper ParSet Works II", {
methods = list("fd1" = extractFDAFourier())
lrn = makeExtractFDAFeatsWrapper("classif.xgboost", feat.methods = methods)
ps = getLearnerParamSet(lrn)
# Check whether all Ids are contained in the resulting param set
ps.xgboost = getLearnerParamSet(makeLearner("classif.xgboost"))
expect_subset(getParamIds(ps.xgboost), getParamIds(ps))
expect_subset(getParamIds(methods$fd1$par.set), getParamIds(ps))
ps2 = makeParamSet(makeDiscreteParam("trafo.coeff", values = c("phase", "amplitude")))
df = getTaskData(fuelsubset.task, functionals.as = "matrix")[, c("heatan", "UVVIS")]
colnames(df) = c("target", "fd1")
df$target = as.factor(round(df$target / 10, 0))
mod = tuneParams(lrn, makeClassifTask(data = df, target = "target"), cv2, acc, ps2, makeTuneControlGrid(resolution = 2L))
})
test_that("extractFDAFeaturesWrapper ParSet Works III", {
methods = list("fd" = extractFDAWavelets())
lrn = makeExtractFDAFeatsWrapper("classif.rpart", feat.methods = methods)
ps = makeParamSet(
makeNumericParam("cp", lower = 0.01, upper = 0.3),
makeDiscreteParam("filter", values = c("la8", "haar"))
)
lrn = makeTuneWrapper(learner = lrn, resampling = cv2, measure = acc, par.set = ps, control = makeTuneControlMBO(budget = 5L))
mod = train(lrn, subsetTask(gunpoint.task, subset = 2:10))
expect_class(mod, "TuneModel")
})
test_that("extractFDAFeaturesWrapper works for dtwkernel", {
methods = list("fd" = extractFDADTWKernel(n.refs = 0.7))
lrn = makeExtractFDAFeatsWrapper("classif.rpart", feat.methods = methods)
ps = makeParamSet(
makeNumericParam("cp", lower = 0.01, upper = 0.3),
makeNumericParam("n.refs", lower = 0.01, upper = 0.05)
)
lrn = makeTuneWrapper(learner = lrn, resampling = cv2, measure = acc, par.set = ps, control = makeTuneControlRandom(budget = 2L))
mod = train(lrn, subsetTask(gunpoint.task, subset = 2:30))
expect_class(mod, "TuneModel")
})
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