test_that("paramtest classif.lssvm train", {
learner = lrn("classif.lssvm")
# 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(
s4_helper(getMethod("lssvm", "matrix"))
)
exclude = c(
"x", # handled by mlr3
"y", # handled by mlr3
"class.weights", # handled by the task
"prob.model", # Would calculate the scale parameter of the Laplacian distribution fitted
"cross", # crossvalidation is done in mlr3
"subset", # mlr3
"type", # 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", ...
"kpar",
"degree",
"sigma",
"scale",
"order",
"offset",
"normalized",
"length",
"lambda"
)
paramtest = run_paramtest(learner, fun, exclude, tag = "train")
expect_paramtest(paramtest)
})
test_that("paramtest classif.lssvm predict", {
learner = lrn("classif.lssvm")
# 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(
s4_helper(getMethod("predict", "lssvm"))
)
exclude = c(
# predict
"object",
"newdata",
"coupler",
"type"
)
paramtest = run_paramtest(learner, fun, exclude, tag = "predict")
expect_paramtest(paramtest)
})
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