context("classif_rotationForest")
test_that("classif_rotationForest", {
requirePackagesOrSkip("rotationForest", default.method = "load")
parset.list = list(
list(),
list(L = 5L, K = 2L),
list(L = 10L, K = 2L)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
train = binaryclass.train
target = train[, binaryclass.target]
target = as.factor(ifelse(target == binaryclass.task$task.desc$positive, 1, 0))
train[, binaryclass.target] = NULL
pars = list(x = train, y = target)
pars = c(pars, parset)
set.seed(getOption("mlr.debug.seed"))
m = do.call(rotationForest::rotationForest, pars)
binaryclass.test[, binaryclass.target] = NULL
set.seed(getOption("mlr.debug.seed"))
p = predict(m, newdata = binaryclass.test)
p = as.factor(ifelse(p > 0.5, binaryclass.task$task.desc$positive, binaryclass.task$task.desc$negative))
set.seed(getOption("mlr.debug.seed"))
p2 = predict(m, newdata = binaryclass.test)
old.predicts.list[[i]] = p
old.probs.list[[i]] = p2
}
testSimpleParsets("classif.rotationForest", binaryclass.df, binaryclass.target,
binaryclass.train.inds, old.predicts.list, parset.list)
testProbParsets("classif.rotationForest", binaryclass.df, binaryclass.target,
binaryclass.train.inds, old.probs.list, parset.list)
})
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