test_that("autotest", {
learner = lrn("classif.glmer")
expect_learner(learner)
# Set a formula and run through each test in turn.
# Test with single logical feature
learner$param_set$values$formula = as.formula("target ~ (1|logical)")
result_logical = run_autotest(
learner,
exclude = "(sanity)|(all)|(utf)|(integer)|(numeric)|(factor)"
)
expect_true(result_logical, info = result_logical$error)
# Test with single factor feature
learner$param_set$values$formula = as.formula("target ~ (1|factor)")
result_factor = run_autotest(
learner,
exclude = "(sanity)|(all)|(utf)|(integer)|(numeric)|(logical)"
)
expect_true(result_factor, info = result_factor$error)
# Test with single integer feature
learner$param_set$values$formula = as.formula("target ~ (1|integer)")
result_integer = run_autotest(
learner,
exclude = "(sanity)|(all)|(utf)|(factor)|(numeric)|(logical)"
)
expect_true(result_integer, info = result_integer$error)
# Note: a mixed effects model cannot be built with only float features since
# the number of effect groups will not be fewer than the number of observations,
# so the numeric single test and the sanity tests are skipped.
# Test with all features
learner$param_set$values$formula = as.formula("target ~ (numeric+integer+logical|factor)")
result_all = run_autotest(
learner,
exclude = "(sanity)|(utf)|(fact)|(int)|(log)|(num)"
)
expect_true(result_all, info = result_all$error)
})
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