Nothing
# test-ALE-categorical.R
# Because this is the most complex type of ALE object, try to test almost every option here.
test_that(
'bootstrapped binary outcome with full 1D and 2D ALE', {
skip_on_ci()
## Create and verify cat_cars_ale object ---------------
cat_cars_ale <- ALE(
test_nn_categorical,
x_cols = list(d1 = TRUE, d2 = TRUE),
data = test_cars,
pred_type = 'probs',
boot_it = 2,
parallel = 0,
p_values = 'auto',
output_boot_data = TRUE,
# sample_size = 25, # test sampled rug plots
silent = TRUE
)
cat_cars_ale |>
unclass() |>
expect_snapshot()
## Test get.ALE methods --------------------
# get.ALE with a simple 1D ALE object (no bootstrap, numeric y) uses default arguments
get(cat_cars_ale) |> expect_snapshot()
# get.ALE with a bootstrapped ALE object returns boot_data and stats
get(cat_cars_ale, what = "boot_data") |> expect_snapshot()
get(cat_cars_ale, stats = "estimate") |> expect_snapshot()
# get.ALE works for a categorical ALE object
get(cat_cars_ale, cats = c('Asia', 'Europe')) |> expect_snapshot()
# get.ALE can exclude specific columns (edge case with 2D) and still return a snapshot
get(cat_cars_ale, exclude_cols = list(d2_all = 'am')) |> expect_snapshot()
# get.ALE can retrieve conf_regions or conf_sig if p-values exist (edge case)
get(cat_cars_ale, stats = "conf_regions") |> expect_snapshot()
get(cat_cars_ale, stats = "conf_sig") |> expect_snapshot()
## Test plot.ALE methods --------------------
# Because it is complex to save entire ggplot objects, only save the core data from the plots
cat_cars_ale_plots <- plot(
cat_cars_ale,
rug_sample_size = 25 # test sampled rug plots
)
cat_cars_ale_plots |>
ale_plots_to_data() |>
expect_snapshot()
# # Create snapshot tests
# get(cat_cars_ale_plots, 'wt', cats = 'Asia')
# get(cat_cars_ale_plots, 'gear:carb', cats = c('Europe', 'North America'))
# get(cat_cars_ale_plots, type = 'effect')
## Test print.ALE methods --------------------
print(cat_cars_ale) |>
capture.output() |>
expect_snapshot()
}
)
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