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test_that("plotLearnerPrediction", {
requirePackagesOrSkip("clusterSim", default.method = "load")
gs = 10
plotLearnerPrediction("classif.rpart", multiclass.task, gridsize = gs)
suppressMessages(ggsave(tempfile(fileext = ".png")))
plotLearnerPrediction("classif.rpart", multiclass.task, gridsize = gs, err.mark = "none")
suppressMessages(ggsave(tempfile(fileext = ".png")))
plotLearnerPrediction("classif.rpart", binaryclass.task, gridsize = gs)
suppressMessages(ggsave(tempfile(fileext = ".png")))
plotLearnerPrediction("classif.rpart", binaryclass.task, gridsize = gs, err.mark = "none")
suppressMessages(ggsave(tempfile(fileext = ".png")))
plotLearnerPrediction("regr.rpart", regr.task, gridsize = gs)
suppressMessages(ggsave(tempfile(fileext = ".png")))
plotLearnerPrediction("regr.lm", regr.task,
features = getTaskFeatureNames(regr.task)[1], gridsize = gs)
suppressMessages(ggsave(tempfile(fileext = ".png")))
plotLearnerPrediction("cluster.kmeans", noclass.task, gridsize = gs)
suppressMessages(ggsave(tempfile(fileext = ".png")))
# pretty.names works
lrn = makeLearner("classif.rpart")
plotLearnerPrediction(lrn, multiclass.task)
dir = tempdir()
path = file.path(dir, "test.svg")
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
testDocForStrings(doc, getLearnerShortName(lrn))
plotLearnerPrediction(lrn, multiclass.task, pretty.names = FALSE)
dir = tempdir()
path = file.path(dir, "test.svg")
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
testDocForStrings(doc, getLearnerId(lrn))
})
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