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test_that("predict.type gets propagated", {
inner = makeResampleDesc("Holdout")
lrn1 = makeLearner("classif.rpart")
ps = makeParamSet(makeNumericParam("cp", lower = 0.1, upper = 1))
ctrl = makeTuneControlRandom(maxit = 2)
lrn2 = makeTuneWrapper(lrn1, resampling = inner, control = ctrl, par.set = ps,
measures = getDefaultMeasure(iris.task))
expect_equal(lrn2$predict.type, "response")
lrn2 = setPredictType(lrn2, "prob")
expect_equal(lrn2$predict.type, "prob")
m = train(lrn2, iris.task)
p = predict(m, iris.task)
prob = getPredictionProbabilities(p)
expect_true(is.data.frame(prob) && nrow(prob) == nrow(iris))
})
test_that("predict.type works with BaggingWrapper, special case", {
lrn1 = makeLearner("classif.rpart", predict.type = "prob")
expect_error(makeBaggingWrapper(lrn1, bw.iters = 3), "response")
lrn1 = makeLearner("classif.rpart")
lrn2 = makeBaggingWrapper(lrn1, bw.iters = 3)
expect_equal(lrn2$predict.type, "response")
lrn2 = setPredictType(lrn2, "prob")
expect_equal(lrn2$predict.type, "prob")
expect_equal(lrn2$next.learner$predict.type, "response")
m = train(lrn2, iris.task)
p = predict(m, iris.task)
prob = getPredictionProbabilities(p)
expect_true(is.data.frame(prob) && nrow(prob) == nrow(iris))
})
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