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test_that("ConstantClassWrapper predicts with response", {
lrn1 = makeLearner("classif.rpart")
lrn2 = makeConstantClassWrapper(lrn1)
# multiple classes present
m1 = train(lrn1, multiclass.task, subset = multiclass.train.inds)
m2 = train(lrn2, multiclass.task, subset = multiclass.train.inds)
expect_false(inherits(m2, "FailureModel"))
p1 = predict(m1, task = multiclass.task, subset = multiclass.test.inds)
p2 = predict(m2, task = multiclass.task, subset = multiclass.test.inds)
p1$time = 0
p2$time = 0
expect_equal(p1, p2)
# one class present
train.inds = 1:20
try({
suppressAll({
train(lrn1, multiclass.task, subset = train.inds)
})
fail("Data has more than one class.")
}, silent = TRUE)
m2 = train(lrn2, multiclass.task, subset = train.inds)
expect_false(inherits(m2, "FailureModel"))
p2 = predict(m2, task = multiclass.task, subset = multiclass.test.inds)
have = getPredictionResponse(p2)
want = rep.int(unique(multiclass.df[train.inds, multiclass.target]), length(multiclass.test.inds))
expect_equal(have, want)
})
test_that("ConstantClassWrapper predicts with frac", {
lrn1 = makeLearner("classif.rpart")
lrn2 = makeConstantClassWrapper(lrn1, frac = 0.1)
train.inds = 1:51
m1 = train(lrn1, multiclass.task, subset = multiclass.train.inds)
m2 = train(lrn2, multiclass.task, subset = train.inds)
expect_false(inherits(m2, "FailureModel"))
p1 = predict(m1, task = multiclass.task, subset = multiclass.test.inds)
p2 = predict(m2, task = multiclass.task, subset = multiclass.test.inds)
expect_false(all(getPredictionResponse(p1) == getPredictionResponse(p2)))
have = getPredictionResponse(p2)
want = rep.int(multiclass.df[1, multiclass.target], length(multiclass.test.inds))
expect_equal(have, want)
})
test_that("ConstantClassWrapper predicts with probs", {
lrn1 = makeLearner("classif.rpart", predict.type = "prob")
lrn2 = makeConstantClassWrapper(lrn1)
# multiple classes present
m1 = train(lrn1, multiclass.task, subset = multiclass.train.inds)
m2 = train(lrn2, multiclass.task, subset = multiclass.train.inds)
expect_false(inherits(m2, "FailureModel"))
p1 = predict(m1, task = multiclass.task, subset = multiclass.test.inds)
p2 = predict(m2, task = multiclass.task, subset = multiclass.test.inds)
p1$time = 0
p2$time = 0
expect_equal(p1, p2)
# one class present
train.inds = 1:20
try({
suppressAll({
train(lrn1, multiclass.task, subset = train.inds)
})
fail("Data has more than one class.")
}, silent = TRUE)
m2 = train(lrn2, multiclass.task, subset = train.inds)
expect_false(inherits(m2, "FailureModel"))
p2 = predict(m2, task = multiclass.task, subset = multiclass.test.inds)
have = getPredictionResponse(p2)
want = rep.int(unique(multiclass.df[train.inds, multiclass.target]), length(multiclass.test.inds))
expect_equal(have, want)
probs = getPredictionProbabilities(p2)
sapply(names(probs), function(col) {
prob = ifelse(col == unique(multiclass.df[train.inds, multiclass.target]), 1, 0)
expect_true(all(probs[col] == prob))
})
})
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