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test_that("classif_ada", {
requirePackagesOrSkip("ada", default.method = "load")
parset.list = list(
list(),
list(iter = 5L)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
pars = list(binaryclass.formula, data = binaryclass.train)
pars = c(pars, parset)
set.seed(getOption("mlr.debug.seed"))
m = do.call(ada::ada, pars)
set.seed(getOption("mlr.debug.seed"))
p = predict(m, newdata = binaryclass.test, type = "probs")
old.probs.list[[i]] = p[, 1]
old.predicts.list[[i]] = as.factor(binaryclass.class.levs[ifelse(p[, 2] > 0.5, 2, 1)])
}
testSimpleParsets("classif.ada", binaryclass.df, binaryclass.target,
binaryclass.train.inds, old.predicts.list, parset.list)
testProbParsets("classif.ada", binaryclass.df, binaryclass.target,
binaryclass.train.inds, old.probs.list, parset.list)
})
test_that("classif_ada passes parameters correctly to rpart.control (#732)", {
cp.vals = c(0.022, 0.023)
loss.vals = c("exponential", "logistic")
for (cp in cp.vals) {
for (loss in loss.vals) {
lrn = makeLearner("classif.ada", cp = cp, loss = loss)
mod = getLearnerModel(train(lrn, pid.task))
expect_equal(mod$model$trees[[1]]$control$cp, cp)
expect_equal(mod$model$lossObj$loss, loss)
}
}
})
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