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context("generateThreshVsPerf")
test_that("generateThreshVsPerfData", {
## single prediction
lrn = makeLearner("classif.rpart", predict.type = "prob")
mod = train(lrn, binaryclass.task)
pred = predict(mod, binaryclass.task)
pvs = generateThreshVsPerfData(pred, list(tpr, fpr))
plotThreshVsPerf(pvs)
dir = tempdir()
path = file.path(dir, "test.svg")
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)), equals(length(pvs$measures)))
expect_that(length(XML::getNodeSet(doc, black.line.xpath, ns.svg)), equals(length(pvs$measures)))
plotROCCurves(pvs, list(fpr, tpr), diagonal = FALSE)
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, black.line.xpath, ns.svg)), equals(1L))
## resample prediction
rdesc = makeResampleDesc("CV", iters = 2L)
r = resample(lrn, binaryclass.task, rdesc)
pvs = generateThreshVsPerfData(r, list(tpr, fpr))
plotThreshVsPerf(pvs, pretty.names = FALSE)
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)), equals(length(pvs$measures)))
expect_that(length(XML::getNodeSet(doc, black.line.xpath, ns.svg)), equals(length(pvs$measures)))
pvs = generateThreshVsPerfData(r, list(tpr, fpr, acc), aggregate = FALSE)
plotThreshVsPerf(pvs, measures = list(tpr, fpr, acc))
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, black.line.xpath, ns.svg)), equals(length(pvs$measures) * length(unique(pvs$data$iter))))
plotROCCurves(pvs, list(fpr, tpr), diagonal = FALSE)
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, black.line.xpath, ns.svg)), equals(length(unique(pvs$data$iter))))
pvs = generateThreshVsPerfData(r, list(tpr, fpr), aggregate = FALSE)
plotROCCurves(pvs, list(fpr, tpr), diagonal = FALSE)
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, black.line.xpath, ns.svg)), equals(rdesc$iters))
## benchmark result
lrns = list(lrn, makeLearner("classif.lda", predict.type = "prob"))
rdesc = makeResampleDesc("CV", iters = 2L)
res = benchmark(lrns, binaryclass.task, rdesc, show.info = FALSE,
measures = getDefaultMeasure(binaryclass.task))
pvs = generateThreshVsPerfData(res, list(tpr, fpr))
plotThreshVsPerf(pvs)
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)), equals(length(pvs$measures)))
expect_that(length(XML::getNodeSet(doc, red.line.xpath, ns.svg)), equals(length(unique(pvs$data$learner))))
expect_that(length(XML::getNodeSet(doc, blue.line.xpath, ns.svg)), equals(length(unique(pvs$data$learner))))
plotROCCurves(pvs, list(fpr, tpr), diagonal = FALSE, facet.learner = TRUE)
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, black.line.xpath, ns.svg)), equals(length(unique(pvs$data$learner))))
plotROCCurves(pvs, list(fpr, tpr), diagonal = FALSE, facet.learner = FALSE)
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, red.line.xpath, ns.svg)), equals(1))
expect_that(length(XML::getNodeSet(doc, blue.line.xpath, ns.svg)), equals(1))
pvs = generateThreshVsPerfData(res, list(tpr, fpr), aggregate = FALSE)
plotROCCurves(pvs, list(fpr, tpr), diagonal = FALSE, facet.learner = TRUE)
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, black.line.xpath, ns.svg)), equals(rdesc$iters * length(unique(pvs$data$learner))))
plotROCCurves(pvs, list(fpr, tpr), diagonal = FALSE, facet.learner = FALSE)
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, red.line.xpath, ns.svg)), equals(rdesc$iters))
expect_that(length(XML::getNodeSet(doc, blue.line.xpath, ns.svg)), equals(rdesc$iters))
## list of resample predictions
rs = lapply(lrns, crossval, task = binaryclass.task, iters = 2L)
names(rs) = c("a", "b")
pvs = generateThreshVsPerfData(rs, list(tpr, fpr))
plotThreshVsPerf(pvs)
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)), equals(length(pvs$measures)))
expect_that(length(XML::getNodeSet(doc, red.line.xpath, ns.svg)), equals(length(unique(pvs$data$learner))))
expect_that(length(XML::getNodeSet(doc, blue.line.xpath, ns.svg)), equals(length(unique(pvs$data$learner))))
plotROCCurves(pvs, list(fpr, tpr), diagonal = FALSE, facet.learner = TRUE)
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)), equals(length(unique(pvs$data$learner))))
expect_that(length(XML::getNodeSet(doc, black.line.xpath, ns.svg)), equals(length(unique(pvs$data$learner))))
plotROCCurves(pvs, list(fpr, tpr), diagonal = FALSE, facet.learner = FALSE)
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, blue.line.xpath, ns.svg)), equals(1))
expect_that(length(XML::getNodeSet(doc, red.line.xpath, ns.svg)), equals(1))
pvs = generateThreshVsPerfData(rs, list(tpr, fpr), aggregate = FALSE)
plotROCCurves(pvs, list(fpr, tpr), diagonal = FALSE, facet.learner = TRUE)
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)), equals(length(unique(pvs$data$learner))))
expect_that(length(XML::getNodeSet(doc, black.line.xpath, ns.svg)), equals(rdesc$iters * length(unique(pvs$data$learner))))
## test prediction obj with custom measure
classes = levels(getTaskTargets(binaryclass.task))
mcm = matrix(sample(0:3, size = (length(classes))^2, TRUE), ncol = length(classes))
rownames(mcm) = colnames(mcm) = classes
costs = makeCostMeasure(id = "asym.costs", name = "Asymmetric costs",
minimize = TRUE, costs = mcm, combine = mean)
pvs.custom = generateThreshVsPerfData(pred, costs)
plotThreshVsPerf(pvs.custom)
ggplot2::ggsave(path)
doc = XML::xmlParse(path)
expect_that(length(XML::getNodeSet(doc, black.line.xpath, ns.svg)), equals(1L))
# test that facetting works for plotThreshVsPerf
q = plotThreshVsPerf(pvs, facet.wrap.nrow = 2L)
testFacetting(q, nrow = 2L)
q = plotThreshVsPerf(pvs, facet.wrap.ncol = 2L)
testFacetting(q, ncol = 2L)
})
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