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context("generateCalibration")
test_that("generateCalibrationData", {
## single prediction
lrn = makeLearner("classif.rpart", predict.type = "prob")
mod = train(lrn, binaryclass.task)
pred = predict(mod, binaryclass.task)
cd = generateCalibrationData(pred)
expect_that(cd$proportion, is_a("data.frame"))
expect_that(cd$data, is_a("data.frame"))
plotCalibration(cd)
dir = tempdir()
path = file.path(dir, "test.svg")
ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, red.line.xpath, ns.svg)), equals(length(unique(cd$data$Learner))))
expect_that(length(XML::getNodeSet(doc, red.circle.xpath, ns.svg)), equals(nrow(cd$proportion) + 1))
## resample prediction
rdesc = makeResampleDesc("CV", iters = 2L)
r = resample(lrn, binaryclass.task, rdesc)
cd = generateCalibrationData(r)
expect_that(cd$proportion, is_a("data.frame"))
expect_that(cd$data, is_a("data.frame"))
plotCalibration(cd)
dir = tempdir()
path = file.path(dir, "test.svg")
ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, red.line.xpath, ns.svg)), equals(length(unique(cd$data$Learner))))
expect_that(length(XML::getNodeSet(doc, red.circle.xpath, ns.svg)), equals(nrow(cd$proportion) + 1))
## benchmark result
lrns = list(lrn, makeLearner("classif.lda", predict.type = "prob"))
res = benchmark(lrns, binaryclass.task, rdesc, show.info = FALSE)
cd = generateCalibrationData(res)
plotCalibration(cd)
ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)), equals(length(unique(cd$data$Learner))))
expect_that(length(XML::getNodeSet(doc, red.circle.xpath, ns.svg)), equals(nrow(cd$proportion) + 1))
## list of resample predictions
rs = lapply(lrns, crossval, task = binaryclass.task, iters = 2L)
names(rs) = c("a", "b")
cd = generateCalibrationData(rs)
plotCalibration(cd)
ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)), equals(length(unique(cd$data$Learner))))
expect_that(length(XML::getNodeSet(doc, red.circle.xpath, ns.svg)), equals(nrow(cd$proportion) + 1))
# facetting works:
q = q = plotCalibration(cd, facet.wrap.nrow = 2L)
testFacetting(q, 2L)
q = q = plotCalibration(cd, facet.wrap.ncol = 2L)
testFacetting(q, ncol = 2L)
})
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