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test_that("PreprocWrapper", {
f1 = function(data, target, args) {
data[, 2] = args$x * data[, 2]
return(list(data = data, control = list()))
}
f2 = function(data, target, args, control) {
data[, 2] = args$x * data[, 2]
return(data)
}
ps = makeParamSet(
makeNumericLearnerParam(id = "x"),
makeNumericLearnerParam(id = "y")
)
lrn1 = makeLearner("classif.rpart", minsplit = 10)
lrn2 = makePreprocWrapper(lrn1, train = f1, predict = f2, par.set = ps, par.vals = list(x = 1, y = 2))
capture.output(print(lrn2))
expect_true(setequal(getHyperPars(lrn2), list(xval = 0, minsplit = 10, x = 1, y = 2)))
expect_true(setequal(getHyperPars(lrn2, "train"), list(xval = 0, minsplit = 10, x = 1, y = 2)))
expect_true(setequal(lrn2$par.vals, list(x = 1, y = 2)))
lrn3 = setHyperPars(lrn2, minsplit = 77, x = 88)
expect_true(setequal(getHyperPars(lrn3), list(xval = 0, minsplit = 77, x = 88, y = 2)))
expect_true(setequal(lrn3$par.vals, list(x = 88, y = 2)))
m = train(lrn2, task = multiclass.task)
capture.output(print(m))
expect_true(setequal(getHyperPars(m$learner), list(xval = 0, minsplit = 10, x = 1, y = 2)))
})
test_that("getLearnerModel on nested PreprocWrapper", {
lrn = makeLearner("classif.rpart")
lrn = makeDummyFeaturesWrapper(lrn)
lrn = makeImputeWrapper(lrn, classes = list(numeric = imputeMax(5), factor = imputeConstant("NA")))
m = train(lrn, binaryclass.task)
expect_s3_class(getLearnerModel(m), "PreprocModel")
expect_s3_class(getLearnerModel(m, TRUE), "rpart")
})
test_that("PreprocWrapper with glmnet (#958)", {
requirePackagesOrSkip("glmnet", default.method = "load")
lrn = makeLearner("classif.glmnet", predict.type = "response")
lrn2 = makePreprocWrapper(lrn,
train = function(data, target, args) {
return(list(data = data, control = list()))
},
predict = function(data, target, args, control) {
return(data)
}
)
mod = train(lrn2, multiclass.task)
pred = predict(mod, multiclass.task)
expect_error(pred, NA)
})
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