context("classif_nodeHarvest")
test_that("classif_nodeHarvest", {
requirePackagesOrSkip("nodeHarvest", default.method = "load")
parset.list = list(
list(),
list(nodes = 100L),
list(nodes = 100L, maxinter = 1L),
list(nodes = 100L, mode = "outbag")
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
Y = ifelse(binaryclass.df[binaryclass.train.inds, binaryclass.class.col] == binaryclass.class.levs[1], 1, 0)
parset = c(parset, list(X = binaryclass.df[binaryclass.train.inds, -binaryclass.class.col], Y = Y, silent = TRUE))
set.seed(getOption("mlr.debug.seed"))
m = do.call(nodeHarvest::nodeHarvest, parset)
p = predict(m, binaryclass.df[-binaryclass.train.inds, ])
old.predicts.list[[i]] = ifelse(p > 0.5, binaryclass.class.levs[1], binaryclass.class.levs[2])
old.probs.list[[i]] = p
}
testSimpleParsets("classif.nodeHarvest", binaryclass.df, binaryclass.target, binaryclass.train.inds,
old.predicts.list, parset.list)
testProbParsets("classif.nodeHarvest", binaryclass.df, binaryclass.target,
binaryclass.train.inds, old.probs.list, parset.list)
})
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