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test_that("FailureModel", {
configureMlr(on.learner.error = "quiet", show.learner.output = FALSE)
# classif: response
lrn = makeLearner("classif.qda", predict.type = "prob")
m = train(lrn, multiclass.task, subset = c(1, 51, 101))
expect_true(inherits(m, "FailureModel"))
expect_true(!is.null(m$learner.model))
expect_true(grep("some group is too small", getFailureModelMsg(m)) == 1L)
p = predict(m, newdata = iris)
expect_true(all(is.na(p$data$response)))
# classif: probs
lrn = makeLearner("classif.qda", predict.type = "prob")
m = train(lrn, multiclass.task, subset = c(1, 51, 101))
expect_true(inherits(m, "FailureModel"))
expect_true(grep("some group is too small", getFailureModelMsg(m)) == 1L)
expect_true(!is.null(m$learner.model))
p = predict(m, newdata = iris)
expect_true(all(is.na(p$data$response)))
prob = getPredictionProbabilities(p)
expect_true(all(dim(prob) == c(150, 3)))
expect_true(all(is.na(prob)))
task = dropFeatures(regr.task, "chas")
# regr: response
wl = makeLearner("regr.km")
m = train(wl, task, subset = 1:2)
expect_true(inherits(m, "FailureModel"))
expect_true(!is.null(m$learner.model))
p = predict(m, task = task)
expect_true(all(is.na(p$data$response)))
# regr: se
wl = makeLearner("regr.km", predict.type = "se")
m = train(wl, task, subset = 1:2)
expect_true(inherits(m, "FailureModel"))
expect_true(!is.null(m$learner.model))
p = predict(m, task = task)
expect_true(all(is.na(p$data$response)))
expect_true(all(is.na(p$data$se)))
# costens: response
lrn = makeCostSensClassifWrapper("classif.__mlrmocklearners__3")
m = train(lrn, costsens.task)
expect_true(isFailureModel(m))
expect_true(!is.null(m$learner.model))
expect_true(grep("foo", getFailureModelMsg(m)) == 1L)
expect_output(print(m), "Training failed")
expect_output(print(m), "foo")
p = predict(m, newdata = iris)
expect_true(all(is.na(p$data$response)))
configureMlr(on.learner.error = "stop", show.learner.output = FALSE)
})
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