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test_that("hyperpars", {
# RWeka not avail
skip_on_cran()
skip_on_os("windows")
lrn = makeLearner("classif.rpart", minsplit = 10)
expect_equal(getHyperPars(lrn), list(xval = 0, minsplit = 10))
m = train(lrn, task = multiclass.task)
expect_true(!inherits(m, "FailureModel"))
expect_equal(getHyperPars(m$learner), list(xval = 0, minsplit = 10))
# test equality after removing using removeHyperPars
lrn = makeLearner("classif.J48", C = 0.5)
expect_identical(getHyperPars(makeLearner("classif.J48")),
getHyperPars(removeHyperPars(lrn, "C")))
# test a more complex param object
lrn = makeLearner("classif.ksvm", class.weights = c(setosa = 1,
versicolor = 2, virginica = 3))
m = train(lrn, task = multiclass.task)
# check warnings
mlr.opts = getMlrOptions()
configureMlr(on.par.without.desc = "warn", show.learner.output = FALSE)
expect_warning(makeLearner("classif.rpart", foo = 1), "Setting parameter foo without")
configureMlr(on.par.without.desc = "quiet")
expect_warning(makeLearner("classif.rpart", foo = 1), NA)
configureMlr(show.learner.output = FALSE)
do.call(configureMlr, mlr.opts)
})
test_that("removing par settings works", {
lrn = makeLearner("classif.qda")
expect_error(removeHyperPars(lrn, "minsplit"), "Trying to remove")
expect_error(removeHyperPars(lrn, "xxx"), "Trying to remove")
lrn2 = setHyperPars(lrn, method = "mve", nu = 7)
lrn3 = removeHyperPars(lrn2, "method")
expect_equal(getHyperPars(lrn3), list(nu = 7))
# now with wrapper
lrn = makeBaggingWrapper(makeLearner("classif.qda"))
lrn2 = setHyperPars(lrn, method = "mve", bw.iters = 9)
lrn3 = removeHyperPars(lrn2, "method")
expect_equal(getHyperPars(lrn3), list(bw.iters = 9))
lrn3 = removeHyperPars(lrn2, "bw.iters")
expect_equal(getHyperPars(lrn3), list(method = "mve"))
# now remove all hyperpars using a wrapped wrapper
lrn = makeOversampleWrapper(makeFilterWrapper(makeLearner("classif.qda", nu = 2),
fw.perc = 0.5), osw.rate = 1)
lrn1 = removeHyperPars(lrn, ids = names(getHyperPars(lrn)))
expect_true(length(getHyperPars(lrn1)) == 0)
})
test_that("setting 'when' works for hyperpars", {
lrn = makeLearner("regr.__mlrmocklearners__4", p1 = 1, p2 = 2, p3 = 3)
hps = getHyperPars(lrn)
expect_equal(hps, list(p1 = 1, p2 = 2, p3 = 3))
# model stores p1 + p3 in fit, adds p2,p3 in predict to this (so it predicts constant val)
m = train(lrn, regr.task)
expect_equal(m$learner.model, list(foo = 1 + 3))
p = predict(m, regr.task)
expect_equal(p$data$response, rep(1 + 2 + 2 * 3, getTaskSize(regr.task)))
})
test_that("fuzzy matching works for mistyped hyperpars", {
msg = "classif.ksvm: Setting parameter sigm without available description object!\nDid you mean one of these hyperparameters instead: sigma fit type\nYou can switch off this check by using configureMlr!"
mlr.opts = getMlrOptions()
# test if config arg works properly in combination with show.info
cq = list(on.par.without.desc = "quiet")
cw = list(on.par.without.desc = "warn")
cs = list(on.par.without.desc = "stop")
# never print message when quiet
expect_silent(makeLearner("classif.ksvm", config = cq, sigm = 1))
configureMlr(on.par.without.desc = "quiet")
expect_silent(makeLearner("classif.ksvm", sigm = 1))
# print message and warn
expect_warning(makeLearner("classif.ksvm", config = cw, sigm = 1), msg)
configureMlr(on.par.without.desc = "warn")
expect_warning(makeLearner("classif.ksvm", sigm = 1), msg)
# print message and error
expect_error(makeLearner("classif.ksvm", config = cs, sigm = 1), msg)
configureMlr(on.par.without.desc = "stop")
expect_error(makeLearner("classif.ksvm", sigm = 1), msg)
# docu says: for warn and quiet parameter is passed, check if this is true
lrn = makeLearner("classif.ksvm",
config = list(on.par.without.desc = "quiet"))
expect_equal(getHyperPars(setHyperPars(lrn, sigm = 1))$sigm, 1)
lrn = makeLearner("classif.ksvm",
config = list(on.par.without.desc = "warn"))
expect_warning(expect_equal(getHyperPars(setHyperPars(lrn, sigm = 1))$sigm, 1))
do.call(configureMlr, mlr.opts)
})
test_that("options are respected", {
# with local option
lrn = makeLearner("classif.__mlrmocklearners__2")
expect_error(setHyperPars(lrn, beta = 1), "available description object")
lrn = makeLearner("classif.__mlrmocklearners__2", config = list(on.par.without.desc = "warn"))
expect_warning(setHyperPars(lrn, beta = 1), "available description object")
lrn = makeLearner("classif.__mlrmocklearners__2", config = list(on.par.without.desc = "quiet"))
expect_s3_class(setHyperPars(lrn, beta = 1), "Learner")
lrn = makeLearner("classif.__mlrmocklearners__2")
expect_error(setHyperPars(lrn, alpha = 2), "feasible")
lrn = makeLearner("classif.__mlrmocklearners__2", config = list(on.par.out.of.bounds = "warn"))
expect_warning(setHyperPars(lrn, alpha = 2), "feasible")
lrn = makeLearner("classif.__mlrmocklearners__2", config = list(on.par.out.of.bounds = "quiet"))
expect_s3_class(setHyperPars(lrn, alpha = 2), "Learner")
# with global option
mlr.opts = getMlrOptions()
lrn = makeLearner("classif.__mlrmocklearners__2")
configureMlr(on.par.without.desc = "quiet")
expect_s3_class(setHyperPars(lrn, beta = 1), "Learner")
configureMlr(on.par.out.of.bounds = "quiet")
expect_s3_class(setHyperPars(lrn, alpha = 2), "Learner")
do.call(configureMlr, mlr.opts)
})
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