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test_that("classif_OneR", {
requirePackagesOrSkip("RWeka", default.method = "load")
parset.list = list(
list(),
list(B = 3)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
ctrl = do.call(RWeka::Weka_control, parset)
m = RWeka::OneR(formula = multiclass.formula, data = multiclass.train,
control = ctrl)
p = predict(m, newdata = multiclass.test, type = "class")
p2 = predict(m, newdata = multiclass.test, type = "prob")
old.predicts.list[[i]] = p
old.probs.list[[i]] = p2
}
testSimpleParsets("classif.OneR", multiclass.df, multiclass.target,
multiclass.train.inds, old.predicts.list, parset.list)
testProbParsets("classif.OneR", multiclass.df, multiclass.target,
multiclass.train.inds, old.probs.list, parset.list)
tt = function(formula, data, subset, ...) {
RWeka::OneR(formula, data = data[subset, ], control = RWeka::Weka_control(...))
}
tp = function(model, newdata) predict(model, newdata, type = "class")
testCVParsets("classif.OneR", multiclass.df, multiclass.target,
tune.train = tt, tune.predict = tp, parset.list = parset.list)
})
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