test_that("paramtest regr.rvm train", {
learner = lrn("regr.rvm")
# The Learner actually calls the S4 method with class "formula", but this only creates the matrix
# and then calls the method for the class "matrix"
fun_list = list(
s4_helper(getMethod("rvm", "matrix"))
)
exclude = c(
"x", # handled by mlr3
"y", # handled by mlr3
"cross", # crossvalidation is done in mlrregr ksv3
"type", # train: set to regression
"subset", # mlr3
# on the residuals, can be implemented if wanted
# the kpar parameters are passed explicitly via "kpar", "degree", "scale", "order", "offset",
# "length", "lambda", "normalized"
"degree",
"sigma",
"scale",
"order",
"offset",
"length",
"lambda",
"normalized"
)
paramtest = run_paramtest(learner, fun_list, exclude, tag = "train")
expect_paramtest(paramtest)
})
test_that("paramtest regr.rvm predict", {
learner = lrn("regr.rvm")
# The Learner actually calls the S4 method with class "formula", but this only creates the matrix
# and then calls the method for the class "matrix"
fun_list = list(
s4_helper(getMethod("predict", "rvm"))
)
exclude = c(
"object", # handled internally
"newdata" # handled internally
)
paramtest = run_paramtest(learner, fun_list, exclude, tag = "predict")
expect_paramtest(paramtest)
})
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