Nothing
test_that("binary task", {
task = tsk("sonar")
costs = matrix(c(0, 5, 10, 0), nrow = 2)
rownames(costs) = colnames(costs) = task$class_names
m1 = mlr_measures$get("classif.costs")
m1$costs = costs
m1$id = "m1"
m2 = msr("classif.costs", costs = -1 * costs)
m2$id = "m2"
costs[] = 0
m3 = msr("classif.costs", costs = costs)
m3$id = "m3"
measures = list(m1, m2, m3)
lrn = lrn("classif.featureless")
perf = lrn$train(task)$predict(task)$score(measures)
expect_equal(perf[[1]], -perf[[2]])
expect_equal(perf[[3]], 0)
})
test_that("multiclass", {
task = tsk("iris")
costs = 1 - diag(3)
rownames(costs) = colnames(costs) = task$class_names
m = msr("classif.costs", costs = costs, normalize = FALSE)
measures = list(m, mlr_measures$get("classif.ce"))
lrn = lrn("classif.featureless")
p = lrn$train(task)$predict(task)
perf = p$score(measures, task = task)
expect_equal(perf[["classif.costs"]], perf[["classif.ce"]] * task$nrow)
})
test_that("multiclass / level reordering", {
task = tsk("iris")
costs = matrix(runif(9), nrow = 3)
diag(costs) = 0
rownames(costs) = colnames(costs) = task$class_names
m1 = msr("classif.costs", id = "c1", costs = costs, normalize = FALSE)
costs = costs[c(2, 1, 3), c(3, 2, 1)]
m2 = msr("classif.costs", id = "c2", costs = costs, normalize = FALSE)
measures = list(m1, m2)
lrn = lrn("classif.featureless")
perf = lrn$train(task)$predict(task)$score(measures, task = task)
expect_equal(unname(perf[1]), unname(perf[2]))
})
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