test_that("classif.priority_lasso train", {
learner = lrn("classif.priority_lasso")
fun = list(prioritylasso::prioritylasso, glmnet::cv.glmnet, glmnet::glmnet.control, glmnet::glmnet)
exclude = c(
"x", # handled internally
"y", # handled internally,
"X", # handled internally
"Y", # handled internally,
"weights", # handled internally,
"family", # handled internally
"itrace", # supported via param trace.it
"factory", # only used in scripts, no effect within mlr3
"scale.y", # only applies to regression
"mcontrol" # this is tricky with the "missings" property as the learner still fails
# unless parameters are set correctly
)
paramtest = run_paramtest(learner, fun, exclude, tag = "train")
expect_paramtest(paramtest)
})
test_that("classif.priority_lasso predict", {
learner = lrn("classif.priority_lasso")
fun = list(prioritylasso:::predict.prioritylasso) # nolint
exclude = c(
"object", # handled internally
"newdata", # handled internally
"type", # handled internally
"lambda.type", # predict.glmnet
"predict.gamma", # is passed as gamma to predict.glmnet
"s" # predict.glmnet
)
paramtest = run_paramtest(learner, fun, exclude, tag = "predict")
expect_paramtest(paramtest)
})
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