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test_that("convertBMRToRankMatrix", {
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)
n.tsks = length(getBMRTaskIds(res))
n.lrns = length(getBMRLearnerIds(res))
# measure = NULL
r = convertBMRToRankMatrix(res)
expect_class(r, "matrix")
expect_equal(dim(r), c(n.lrns, n.tsks))
expect_equal(ignore_attr = TRUE, colnames(r), getBMRTaskIds(res))
expect_equal(ignore_attr = TRUE, rownames(r), getBMRLearnerIds(res))
expect_equal(sum(r), sum(1:n.lrns * n.tsks))
# measure = ber
r = convertBMRToRankMatrix(res, ber)
expect_class(r, "matrix")
expect_equal(dim(r), c(n.lrns, n.tsks))
expect_equal(ignore_attr = TRUE, rownames(r), getBMRLearnerIds(res))
expect_equal(sum(r), sum(1:n.lrns * n.tsks))
# check ties.method
r = convertBMRToRankMatrix(res, featperc, ties.method = "first")
expect_equal(as.numeric(r[, 1]), 1:2)
expect_equal(as.numeric(r[, 2]), 1:2)
r = convertBMRToRankMatrix(res, featperc, ties.method = "average")
expect_equal(as.numeric(r[, 1]), c(1.5, 1.5))
expect_equal(as.numeric(r[, 2]), c(1.5, 1.5))
# check that col and row names are right if only one task is given
res = benchmark(lrns, binaryclass.task, rdesc, meas)
r = convertBMRToRankMatrix(res)
expect_equal(ignore_attr = TRUE, rownames(r), getBMRLearnerIds(res))
expect_equal(ignore_attr = TRUE, colnames(r), getBMRTaskIds(res))
})
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