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test_that("BenchmarkResult", {
lrns = list(makeLearner("classif.nnet"), makeLearner("classif.rpart"))
tasks = list(multiclass.task, binaryclass.task)
rdesc = makeResampleDesc("CV", iters = 2L)
meas = list(acc, mmce, ber, featperc)
res = benchmark(lrns, tasks, rdesc, meas)
plotBMRBoxplots(res)
dir = tempdir()
path = file.path(dir, "test.svg")
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
expect_equal(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)), length(getBMRTaskIds(res)))
# facetting works:
q = plotBMRBoxplots(res, facet.wrap.nrow = 2L)
testFacetting(q, 2L)
q = plotBMRBoxplots(res, facet.wrap.ncol = 2L)
testFacetting(q, ncol = 2L)
q = plotBMRBoxplots(res, facet.wrap.nrow = 2L, facet.wrap.ncol = 2L)
testFacetting(q, 2L, 2L)
# pretty names works
plotBMRBoxplots(res)
dir = tempdir()
path = file.path(dir, "test.svg")
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
testDocForStrings(doc, getBMRLearnerShortNames(res), grid.size = 2L)
testDocForStrings(doc, getBMRMeasures(res)[[1L]]$name)
plotBMRBoxplots(res, pretty.names = FALSE)
dir = tempdir()
path = file.path(dir, "test.svg")
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
testDocForStrings(doc, getBMRLearnerIds(res), grid.size = 2L)
testDocForStrings(doc, getBMRMeasureIds(res)[[1L]])
# test pretty.names in conjunction with order.lrns
new.order = c("classif.rpart", "classif.nnet")
plotBMRBoxplots(res, pretty.names = TRUE, order.lrns = new.order)
dir = tempdir()
path = file.path(dir, "test.svg")
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
testDocForStrings(doc, getBMRLearnerShortNames(res)[2:1],
grid.size = 2L, ordered = TRUE)
# check error when learner short names are not unique
lrns = list(
rf = makeLearner("classif.randomForest", id = "rf1"),
rf2 = makeLearner("classif.randomForest", id = "rf2")
)
res = benchmark(lrns, tasks, rdesc, meas)
expect_error(plotBMRSummary(res),
"names are not unique")
})
test_that("BenchmarkResult allows spaces", {
cv = makeResampleDesc("CV", iters = 2L)
measures = list(mlr::auc)
learners = list(
makeLearner("classif.rpart", predict.type = "prob")
)
res = benchmark(learners, sonar.task, cv, measures)
expect_s3_class(plotBMRBoxplots(res, measure = auc), "gg")
suppressMessages(ggsave(tempfile(fileext = ".png")))
})
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