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test_that("caching works with most filters", {
filters = as.character(listFilterMethods()$id)
filter.list = listFilterMethods(desc = FALSE, tasks = TRUE, features = FALSE)
filter.list.classif = as.character(filter.list$id)[filter.list$task.classif]
filter.list.classif = setdiff(filter.list.classif, c(
"univariate.model.score", "permutation.importance", "auc",
"univariate", "rf.importance", "randomForestSRC_var.select"))
filter.list.regr = as.character(filter.list$id)[!filter.list$task.classif & filter.list$task.regr]
# tune over various filters using all possible caching options
# TRUE is not tested, as we are not allowed to write in the user's home dir
out = lapply(list(FALSE, tempdir()), function(i) {
tune.out = lapply(filter.list.regr, function(.x) {
lrn = makeFilterWrapper(learner = "regr.ksvm", fw.method = .x, cache = i)
ps = makeParamSet(
makeNumericParam("fw.perc", lower = 0, upper = 1),
makeNumericParam("C",
lower = -1, upper = 1,
trafo = function(x) 2^x),
makeNumericParam("sigma",
lower = -1, upper = 1,
trafo = function(x) 2^x)
)
rdesc = makeResampleDesc("CV", iters = 2)
tuneParams(lrn,
task = regr.num.task, resampling = rdesc, par.set = ps,
control = makeTuneControlRandom(maxit = 2),
show.info = FALSE, measures = getDefaultMeasure(regr.num.task))
})
})
expect_equal(
out[[1]][["opt.path"]][["env"]][["path"]][["mse.test.mean"]],
out[[2]][["opt.path"]][["env"]][["path"]][["mse.test.mean"]])
})
test_that("cache dir is successfully deleted", {
skip_on_cran() # we are not allowed to write to the user's home dir!
dir = getCacheDir()
if (!dir.exists(dir)) {
dir.create(dir, recursive = TRUE)
}
expect_true(dir.exists(getCacheDir()))
suppressMessages(deleteCacheDir())
expect_false(dir.exists(getCacheDir()))
})
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