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test_that("classif_mda", {
requirePackagesOrSkip("mda")
parset.list1 = list(
list(start.method = "lvq"),
list(start.method = "lvq", subclasses = 2),
list(start.method = "lvq", subclasses = 3)
)
parset.list2 = list(
list(),
list(start.method = "lvq", subclasses = 2),
list(start.method = "lvq", subclasses = 3)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list1)) {
parset = parset.list1[[i]]
pars = list(formula = multiclass.formula, data = multiclass.train)
pars = c(pars, parset)
set.seed(getOption("mlr.debug.seed"))
m = do.call(mda::mda, pars)
p = predict(m, newdata = multiclass.test)
p2 = predict(m, newdata = multiclass.test, type = "posterior")
old.predicts.list[[i]] = p
old.probs.list[[i]] = p2
}
testSimpleParsets("classif.mda", multiclass.df, multiclass.target,
multiclass.train.inds, old.predicts.list, parset.list2)
testProbParsets("classif.mda", multiclass.df, multiclass.target,
multiclass.train.inds, old.probs.list, parset.list2)
tt = mda::mda
tp = function(model, newdata) predict(model, newdata)
testCVParsets("classif.mda", multiclass.df, multiclass.target,
tune.train = tt, tune.predict = tp, parset.list = parset.list1)
testCV("classif.mda", multiclass.df, multiclass.target, tune.train = tt,
tune.predict = tp, parset = list(start.method = "lvq", subclasses = 17))
})
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