test_that("classif_PART", {
requirePackagesOrSkip("RWeka", default.method = "load")
parset.list = list(
list(),
list(M = 10),
list(M = 5, C = 0.4),
list(M = 5, R = TRUE)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
parset$Q = as.integer(runif(1, min = -.Machine$integer.max,
max = .Machine$integer.max))
ctrl = do.call(RWeka::Weka_control, parset)
set.seed(getOption("mlr.debug.seed"))
m = RWeka::PART(formula = multiclass.formula, data = multiclass.train,
control = ctrl)
p = predict(m, newdata = multiclass.test, type = "class")
p2 = predict(m, newdata = multiclass.test, type = "prob")
old.predicts.list[[i]] = p
old.probs.list[[i]] = p2
}
testSimpleParsets("classif.PART", multiclass.df, multiclass.target,
multiclass.train.inds, old.predicts.list, parset.list)
testProbParsets("classif.PART", multiclass.df, multiclass.target,
multiclass.train.inds, old.probs.list, parset.list)
tt = function(formula, data, subset, ...) {
RWeka::PART(formula, data = data[subset, ],
control = RWeka::Weka_control(..., Q = as.integer(runif(1,
min = -.Machine$integer.max, max = .Machine$integer.max))))
}
tp = function(model, newdata) predict(model, newdata, type = "class")
testCVParsets("classif.PART", multiclass.df, multiclass.target,
tune.train = tt, tune.predict = tp, parset.list = parset.list)
})
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