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test_that("clustering predict", {
# RWeka causes problems
skip_on_cran()
lrn = makeLearner("cluster.cmeans", predict.type = "prob")
model = train(lrn, noclass.task)
pred = predict(model, task = noclass.task)
y = pred$data$response
expect_true(is.integer(y))
p = getPredictionProbabilities(pred)
expect_true(is.data.frame(p) && nrow(noclass.df) && ncol(p) == max(y))
})
test_that("clustering performance", {
requirePackagesOrSkip("clusterSim", default.method = "load")
# RWeka causes problems
skip_on_cran()
# RWeka not avail
skip_on_os("windows")
lrn = makeLearner("cluster.SimpleKMeans")
model = train(lrn, noclass.task)
pred = predict(model, task = noclass.task)
expect_true(is.numeric(performance(pred, task = noclass.task,
measures = db)))
expect_true(is.numeric(performance(pred, task = noclass.task,
measures = G1)))
expect_true(is.numeric(performance(pred, task = noclass.task,
measures = G2)))
expect_true(is.numeric(performance(pred, task = noclass.task,
measures = silhouette)))
})
test_that("clustering performance with missing clusters", {
requirePackagesOrSkip("clusterSim", default.method = "load")
# RWeka causes problems
skip_on_cran()
# RWeka not avail
skip_on_os("windows")
lrn = makeLearner("cluster.SimpleKMeans")
model = train(lrn, noclass.task)
pred = predict(model, task = noclass.task)
pred$data$response = sample(c(1, 3, 4), length(pred$data$response),
replace = TRUE)
expect_warning(performance(pred, task = noclass.task, measures = db), NA)
expect_warning(performance(pred, task = noclass.task, measures = G1), NA)
expect_warning(performance(pred, task = noclass.task, measures = G2), NA)
expect_warning(performance(pred, task = noclass.task, measures = silhouette),
NA)
})
test_that("clustering resample", {
requirePackagesOrSkip("clusterSim", default.method = "load")
# RWeka causes problems
skip_on_cran()
# RWeka not avail
skip_on_os("windows")
rdesc = makeResampleDesc("Subsample", split = 0.3, iters = 2)
lrn = makeLearner("cluster.SimpleKMeans")
res = resample(lrn, noclass.task, rdesc)
expect_true(all(!is.na(res$measures.test)))
expect_false(is.na(res$aggr))
})
test_that("clustering benchmark", {
requirePackagesOrSkip("clusterSim", default.method = "load")
# RWeka causes problems
skip_on_cran()
# RWeka not avail
skip_on_os("windows")
task.names = "noclass"
tasks = list(noclass.task)
learner.names = "cluster.SimpleKMeans"
learners = lapply(learner.names, makeLearner)
rin = makeResampleDesc("CV", iters = 2L)
res = benchmark(learners = learners, task = tasks,
resamplings = makeResampleDesc("CV", iters = 2L))
expect_true("BenchmarkResult" %in% class(res))
})
test_that("clustering downsample", {
down.tsk = downsample(noclass.task, perc = 1 / 3)
expect_equal(getTaskSize(down.tsk), 50L)
})
test_that("clustering tune", {
requirePackagesOrSkip("clusterSim", default.method = "load")
# RWeka causes problems
skip_on_cran()
# RWeka not avail
skip_on_os("windows")
lrn = makeLearner("cluster.SimpleKMeans")
rdesc = makeResampleDesc("Holdout")
ps = makeParamSet(
makeIntegerParam("N", lower = 2, upper = 10)
)
ctrl = makeTuneControlRandom(maxit = 2)
tr = tuneParams(lrn, noclass.task, rdesc, par.set = ps, control = ctrl)
expect_equal(getOptPathLength(tr$opt.path), 2)
expect_true(!is.na(tr$y))
})
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