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test_that("generateLearningCurve", {
r = generateLearningCurveData(list("classif.rpart", "classif.knn"),
task = binaryclass.task, percs = c(0.1, 0.3),
measures = list(acc, timeboth))
expect_true(all(c("learner", "percentage", "acc", "timeboth") %in% colnames(r$data)))
plotLearningCurve(r)
dir = tempdir()
path = file.path(dir, "test.svg")
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
expect_equal(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)),
length(r$measures))
expect_equal(length(XML::getNodeSet(doc, red.line.xpath, ns.svg)),
length(unique(r$data$learner)))
expect_equal(length(XML::getNodeSet(doc, blue.line.xpath, ns.svg)),
length(unique(r$data$learner)))
r = generateLearningCurveData(learners = list("regr.lm", "regr.svm"),
task = regr.num.task, percs = c(0.1, 0.2),
resampling = makeResampleDesc(method = "CV", iters = 2),
measures = list(sse, timeboth))
expect_true(all(c("learner", "percentage", "sse", "timeboth") %in% colnames(r$data)))
plotLearningCurve(r)
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
expect_equal(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)),
length(r$measures))
expect_equal(length(XML::getNodeSet(doc, red.line.xpath, ns.svg)),
length(unique(r$data$learner)))
expect_equal(length(XML::getNodeSet(doc, blue.line.xpath, ns.svg)),
length(unique(r$data$learner)))
r = generateLearningCurveData(list("classif.rpart", "classif.knn"),
task = binaryclass.task, percs = c(0.1, 0.3),
resampling = makeResampleDesc("Holdout", predict = "both"),
measures = list(acc, setAggregation(acc, train.mean)))
plotLearningCurve(r)
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
expect_equal(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)),
length(r$measures))
expect_equal(length(XML::getNodeSet(doc, red.line.xpath, ns.svg)),
length(unique(r$data$learner)))
expect_equal(length(XML::getNodeSet(doc, blue.line.xpath, ns.svg)),
length(unique(r$data$learner)))
# facetting works for plotLearningCurveData
q = plotLearningCurve(r, facet.wrap.nrow = 2L)
testFacetting(q, nrow = 2L)
q = plotLearningCurve(r, facet.wrap.ncol = 2L, facet = "learner")
testFacetting(q, ncol = 2L)
})
test_that("generateLearningCurve works if single learner is passed (not wrapped in list)", {
# see issue #1046
r = generateLearningCurveData(makeLearner("classif.rpart"), task = binaryclass.task,
percs = c(0.1, 0.7), measures = list(acc, timeboth))
expect_true(all(c("learner", "percentage", "acc", "timeboth") %in% colnames(r$data)))
})
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