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test_that("learners work: cluster", {
requirePackagesOrSkip("clusterSim", default.method = "load")
skip_on_os("windows")
# RWeka not avail on CRAN
skip_on_cran()
RWeka::WPM("refresh-cache")
RWeka::WPM("install-package", "XMeans")
# settings to make learners faster and deal with small sample size
hyperpars = list()
# clustering, response
task = noclass.task
lrns = listLearnersCustom(task, create = TRUE)
# some learners are not avail on windows
if (Sys.info()[["sysname"]] == "Windows") {
names = vapply(lrns, function(x) x$id, FUN.VALUE = character(1))
row_ids = which(names %in% "cluster.Cobweb")
lrns[row_ids] = NULL
}
lapply(lrns, testThatLearnerParamDefaultsAreInParamSet)
lapply(lrns, testBasicLearnerProperties, task = task, hyperpars = hyperpars)
# clustering, prob
task = subsetTask(noclass.task, subset = 1:20,
features = getTaskFeatureNames(noclass.task)[1:2])
lrns = listLearnersCustom(task, properties = "prob", create = TRUE)
lapply(lrns, testBasicLearnerProperties, task = task, hyperpars = hyperpars,
pred.type = "prob")
# cluster with weights
lrns = listLearnersCustom("cluster", properties = "weights", create = TRUE)
lapply(lrns, testThatLearnerRespectsWeights, hyperpars = hyperpars,
task = task, train.inds = 1:20, test.inds = 1:20, weights = rep(c(1, 5),
length.out = 20),
pred.type = "prob", get.pred.fun = getPredictionProbabilities)
})
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