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test_that("NoFeaturesModel", {
task = subsetTask(multiclass.task, features = character(0))
lrn = makeLearner("classif.lda", predict.type = "prob")
m = train(lrn, task)
expect_s3_class(m$learner.model, "NoFeaturesModel")
expect_s3_class(m$learner, "classif.lda")
expect_equal(m$learner$predict.type, "prob")
p = predict(m, newdata = multiclass.df)
expect_true(setequal(colnames(as.data.frame(p)),
c("prob.setosa", "prob.virginica", "prob.versicolor", "truth", "response")))
res = makeResampleDesc("CV", iter = 2)
rf = resample(lrn, task, res)
expect_true(setequal(colnames(as.data.frame(p)),
c("prob.setosa", "prob.virginica", "prob.versicolor", "truth", "response")))
task = subsetTask(regr.task, features = character(0))
lrn = makeLearner("regr.lm")
m = train(lrn, task)
p = predict(m, newdata = regr.df)
expect_true(all(p$data$response == mean(p$data$response)))
rf = resample(lrn, task, res)$pred
expect_equal(length(unique(rf$data$response)), 2)
})
test_that("NoFeaturesModel works with FilterWrapper", {
lrn = makeLearner("classif.rpart")
lrn = makeFilterWrapper(lrn, fw.method = "anova.test", fw.perc = 0.1)
m = train(lrn, multiclass.task)
p = predict(m, multiclass.task)
expect_true(!is.na(performance(p, measures = getDefaultMeasure(multiclass.task))))
})
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