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test_that("BenchmarkSummary", {
lrns = list(makeLearner("classif.nnet"), makeLearner("classif.rpart"))
tasks = list(multiclass.task, binaryclass.task)
rdesc = makeResampleDesc("CV", iters = 2L)
meas = list(acc, mmce, ber, timeboth)
res = benchmark(lrns, tasks, rdesc, meas)
n.tasks = length(getBMRTaskIds(res))
n.lrns = length(getBMRLearnerIds(res))
plotBMRSummary(res)
# pretty.names works
plotBMRSummary(res)
dir = tempdir()
path = file.path(dir, "test.svg")
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
testDocForStrings(doc, getBMRLearnerShortNames(res))
plotBMRSummary(res, pretty.names = FALSE)
dir = tempdir()
path = file.path(dir, "test.svg")
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
testDocForStrings(doc, getBMRLearnerIds(res))
# 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")
})
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