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test_that("costsens", {
rdesc = makeResampleDesc("CV", iters = 2L)
lrn = makeCostSensClassifWrapper(makeLearner("classif.rpart"))
r = resample(lrn, costsens.task, rdesc)
expect_true(!is.na(r$aggr))
# check case where some costs are totally equal
costs = costsens.costs
costs[1, ] = 1
costs[2, ] = 2
task = makeCostSensTask(data = costsens.feat, costs = costs)
r = resample(lrn, task, rdesc)
expect_true(!is.na(r$aggr))
# check case where all costs are totally equal
costs[, ] = 1
task = makeCostSensTask(data = costsens.feat, costs = costs)
r = resample(lrn, task, rdesc)
expect_true(!is.na(r$aggr))
# check that hyperpars are propagated
lrn2 = setHyperPars(lrn, minsplit = 50)
m = train(lrn2, costsens.task)
m2 = m$learner.model$next.model$learner.model
expect_equal(m2$control$minsplit, 50)
lrn = makeCostSensRegrWrapper(makeLearner("regr.rpart"))
r = resample(lrn, costsens.task, rdesc)
expect_true(!is.na(r$aggr))
lrn = makeCostSensWeightedPairsWrapper(makeLearner("classif.rpart"))
r = resample(lrn, costsens.task, rdesc)
expect_true(!is.na(r$aggr))
})
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