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test_that("classif_C50", {
# test for issue #1573: make C50 learner objects not stochastic
lrn1 = makeLearner("classif.C50")
lrn2 = makeLearner("classif.C50")
expect_equal(lrn1, lrn2)
requirePackages("C50", default.method = "load")
parset.list = list(
list(),
list(control = C50::C5.0Control(), seed = getOption("mlr.debug.seed")),
list(trials = 5L, control = C50::C5.0Control(minCases = 10L, seed = getOption("mlr.debug.seed"))),
list(rules = TRUE, control = C50::C5.0Control(bands = 100L, seed = getOption("mlr.debug.seed"))),
list(control = C50::C5.0Control(CF = 0.5, sample = 0.444, seed = getOption("mlr.debug.seed")))
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
pars = list(x = multiclass.train[, names(multiclass.train) %nin% multiclass.target],
y = multiclass.train[, multiclass.target])
pars = c(pars, parset)
m = do.call(C50::C5.0, pars)
set.seed(getOption("mlr.debug.seed"))
p = predict(m, newdata = multiclass.test, type = "class")
set.seed(getOption("mlr.debug.seed"))
p2 = predict(m, newdata = multiclass.test, type = "prob")
old.predicts.list[[i]] = p
old.probs.list[[i]] = p2
}
parset.list = list(
list(),
list(seed = getOption("mlr.debug.seed")),
list(trials = 5L, minCases = 10L, seed = getOption("mlr.debug.seed")),
list(rules = TRUE, bands = 100L, seed = getOption("mlr.debug.seed")),
list(CF = 0.5, sample = 0.444, seed = getOption("mlr.debug.seed"))
)
testSimpleParsets("classif.C50", multiclass.df, multiclass.target,
multiclass.train.inds, old.predicts.list, parset.list)
testProbParsets("classif.C50", multiclass.df, multiclass.target,
multiclass.train.inds, old.probs.list, parset.list)
})
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