test_that("paramtest classif.ksvm train", {
learner = lrn("classif.ksvm")
fun_list = list(
s4_helper(getMethod("ksvm", "matrix"))
)
exclude = c(
"x", # handled by mlr3
"y", # handled by mlr3
"class.weights", # handled by the task
"prob.model", # handled by mlr3 predict_type
"cross", # crossvalidation is done in mlr3
"fit", # whether fitted values should be kept (not implemented by author)
"na.action", # not implemented by author
"epsilon", # hyperparameter for loss for "eps-svr", "nu-svr", "eps-bsvm" (regression)
"subset", # mlr3
# on the residuals, can be implemented if wanted
# https://stackoverflow.com/questions/34323072/probability-model-in-kernlabksvm
# the kpar parameters are passed explicitly via "kpar"; "degree", "scale", "order", "offset"
"kpar",
"degree",
"sigma",
"scale",
"order",
"offset"
)
paramtest = run_paramtest(learner, fun_list, exclude, tag = "train")
expect_paramtest(paramtest)
})
test_that("paramtest classif.ksvm predict", {
learner = lrn("classif.ksvm")
fun_list = list(
s4_helper(getMethod("predict", "ksvm"))
)
exclude = c(
# predict
"object", # mlr3
"newdata", # mlr3
"type" # mlr3
)
paramtest = run_paramtest(learner, fun_list, exclude, tag = "predict")
expect_paramtest(paramtest)
})
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