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test_that("tuneThreshold", {
requirePackagesOrSkip("lhs", default.method = "load")
# binary classes, 1 th
lrn = makeLearner("classif.lda", predict.type = "prob")
m = train(lrn, binaryclass.task)
p = predict(m, binaryclass.task)
tr = tuneThreshold(p)
expect_true(length(tr$th) == 1 && tr$th >= 0 && tr$th <= 1)
expect_true(tr$perf >= 0 && tr$perf < 0.3)
# multiclass
m = train(lrn, multiclass.task)
p = predict(m, multiclass.task)
tr = tuneThreshold(p, mmce, control = list(maxit = 5L))
expect_true(length(tr$th) == 3 && all(tr$th >= 0) && all(tr$th <= 1))
expect_true(tr$perf >= 0 && tr$perf < 0.1)
})
test_that("tuneThreshold works with all tuning methods", {
lrn = makeLearner("classif.lda", predict.type = "prob")
ps = makeParamSet(makeNumericParam("nu", lower = 2, upper = 3))
ctrls = list(
gensa = makeTuneControlGenSA(
start = list(nu = 2.5), maxit = 1,
tune.threshold = TRUE),
cmaes = makeTuneControlCMAES(
start = list(nu = 2.5), maxit = 1,
tune.threshold = TRUE),
design = makeTuneControlDesign(
design = generateDesign(n = 2, par.set = ps),
tune.threshold = TRUE),
grid = makeTuneControlGrid(resolution = 2L, tune.threshold = TRUE),
irace = makeTuneControlIrace(
maxExperiments = 12, nbIterations = 1L,
minNbSurvival = 1, tune.threshold = TRUE)
)
for (ctrl in ctrls) {
lrn.tuned = makeTuneWrapper(lrn,
resampling = cv2, measures = acc,
par.set = ps, control = ctrl)
res = resample(lrn.tuned, binaryclass.task,
resampling = makeResampleDesc("Holdout"), extract = getTuneResult)
expect_number(res$extract[[1]]$threshold)
}
})
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