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test_that("learners work: multilabel", {
# settings to make learners faster and deal with small sample size
hyperpars = list()
# multiabel
lrns = listLearnersCustom("multilabel", create = TRUE)
lapply(lrns, testThatLearnerParamDefaultsAreInParamSet)
lapply(lrns, testBasicLearnerProperties, task = multilabel.task,
hyperpars = hyperpars)
# multilabel, probs
lrns = listLearnersCustom("multilabel", properties = "prob", create = TRUE)
lapply(lrns, testBasicLearnerProperties, task = multilabel.task,
hyperpars = hyperpars, pred.type = "prob")
# multilabel, factors
lrns = listLearnersCustom("multilabel", properties = "factors", create = TRUE)
lapply(lrns, testThatLearnerHandlesFactors, task = multilabel.task,
hyperpars = hyperpars)
# multilabel, ordered
lrns = listLearnersCustom("multilabel", properties = "ordered", create = TRUE)
lapply(lrns, testThatLearnerHandlesOrderedFactors, task = multilabel.task,
hyperpars = hyperpars)
# multilabel, missings
lrns = listLearnersCustom("multilabel", properties = "missings", create = TRUE)
lapply(lrns, testThatLearnerHandlesMissings, task = multilabel.task,
hyperpars = hyperpars)
# multilabel, weights
lrns = listLearnersCustom("multilabel", properties = "weights", create = TRUE)
lapply(lrns, testThatLearnerRespectsWeights, hyperpars = hyperpars,
task = multilabel.task, train.inds = multilabel.train.inds, multilabel.test.inds,
weights = rep(c(10000L, 1L), c(10L, length(multilabel.train.inds) - 10L)),
pred.type = "prob", get.pred.fun = getPredictionProbabilities)
})
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