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Merged
EmilHvitfeldt merged 5 commits into from
Dec 12, 2025
Merged

Make work with all versions of xgboost #119

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43e8d20
outt our xgboost params correctly
EmilHvitfeldt Dec 12, 2025
56d7d9c
avoid issues in tests when split land on values in xgboost
EmilHvitfeldt Dec 12, 2025
da48393
update test descriptions
EmilHvitfeldt Dec 12, 2025
4ba1da8
test xgboost regression
EmilHvitfeldt Dec 12, 2025
7d9792a
add news
EmilHvitfeldt Dec 12, 2025
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2 changes: 2 additions & 0 deletions NEWS.md
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Original file line number Diff line number Diff line change
@@ -1,5 +1,7 @@
# orbital (development version)

* Make work with new versions of xgboost. (#119)

# orbital 0.4.0

* Added support for tailor package and its integration into workflows. The following adjustments have gained `orbital()` support. (#103)
Expand Down
7 changes: 5 additions & 2 deletions R/model-xgboost.R
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Expand Up @@ -10,7 +10,7 @@ orbital.xgb.Booster <- function(
type <- default_type(type)

if (mode == "classification") {
objective <- x$params$objective
objective <- x$params$objective %||% attr(x, "params")$objective
objective <- rlang::arg_match0(
objective,
c("multi:softprob", "binary:logistic")
Expand All @@ -32,7 +32,10 @@ orbital.xgb.Booster <- function(
xgboost_multisoft <- function(x, type, lvl) {
trees <- tidypredict::.extract_xgb_trees(x)

trees_split <- split(trees, rep(seq_along(lvl), x$niter))
trees_split <- split(
trees,
rep(seq_along(lvl), x$niter %||% nrow(attr(x, "evaluation_log")))
)
trees_split <- lapply(trees_split, collapse_stumps)
trees_split <- vapply(trees_split, paste, character(1), collapse = " + ")

Expand Down
56 changes: 53 additions & 3 deletions tests/testthat/test-model-xgboost.R
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Original file line number Diff line number Diff line change
@@ -1,3 +1,35 @@
test_that("boost_tree(), objective = reg:squarederror, works with type = numeric", {
skip_if_not_installed("parsnip")
skip_if_not_installed("tidypredict")
skip_if_not_installed("xgboost")

bt_spec <- parsnip::boost_tree(mode = "regression", engine = "xgboost")

bt_fit <- parsnip::fit(bt_spec, mpg ~ disp + vs + hp, mtcars)

orb_obj <- orbital(bt_fit)

# to avoid exact split values
mtcars <- mtcars + 0.1

preds <- predict(orb_obj, mtcars)
exps <- predict(bt_fit, mtcars)

expect_named(preds, ".pred")
expect_type(preds$.pred, "double")

exps <- as.data.frame(exps)

rownames(preds) <- NULL
rownames(exps) <- NULL

expect_equal(
preds,
exps,
tolerance = 0.0000001
)
})

test_that("boost_tree(), objective = binary:logistic, works with type = class", {
skip_if_not_installed("parsnip")
skip_if_not_installed("tidypredict")
Expand All @@ -11,6 +43,9 @@ test_that("boost_tree(), objective = binary:logistic, works with type = class",

orb_obj <- orbital(bt_fit, type = "class")

# to avoid exact split values
mtcars[, -8] <- mtcars[, -8] + 0.1

preds <- predict(orb_obj, mtcars)
exps <- predict(bt_fit, mtcars)

Expand All @@ -23,7 +58,7 @@ test_that("boost_tree(), objective = binary:logistic, works with type = class",
)
})

test_that("boost_tree(), objective = binary:logistic, works with type = class", {
test_that("boost_tree(), objective = multi:softprob, works with type = class", {
skip_if_not_installed("parsnip")
skip_if_not_installed("tidypredict")
skip_if_not_installed("xgboost")
Expand All @@ -34,6 +69,9 @@ test_that("boost_tree(), objective = binary:logistic, works with type = class",

orb_obj <- orbital(bt_fit, type = "class")

# to avoid exact split values
iris[, -5] <- iris[, -5] + 0.05

preds <- predict(orb_obj, iris)
exps <- predict(bt_fit, iris)

Expand All @@ -59,6 +97,9 @@ test_that("boost_tree(), objective = binary:logistic, works with type = prob", {

orb_obj <- orbital(bt_fit, type = "prob")

# to avoid exact split values
mtcars[, -8] <- mtcars[, -8] + 0.1

preds <- predict(orb_obj, mtcars)
exps <- predict(bt_fit, mtcars, type = "prob")

Expand All @@ -78,7 +119,7 @@ test_that("boost_tree(), objective = binary:logistic, works with type = prob", {
)
})

test_that("boost_tree(), objective = binary:logistic, works with type = prob", {
test_that("boost_tree(), objective = multi:softprob, works with type = prob", {
skip_if_not_installed("parsnip")
skip_if_not_installed("tidypredict")
skip_if_not_installed("xgboost")
Expand All @@ -89,6 +130,9 @@ test_that("boost_tree(), objective = binary:logistic, works with type = prob", {

orb_obj <- orbital(bt_fit, type = "prob")

# to avoid exact split values
iris[, -5] <- iris[, -5] + 0.05

preds <- predict(orb_obj, iris)
exps <- predict(bt_fit, iris, type = "prob")

Expand Down Expand Up @@ -122,6 +166,9 @@ test_that("boost_tree(), objective = binary:logistic, works with type = c(class,

orb_obj <- orbital(bt_fit, type = c("class", "prob"))

# to avoid exact split values
mtcars[, -8] <- mtcars[, -8] + 0.1

preds <- predict(orb_obj, mtcars)
exps <- dplyr::bind_cols(
predict(bt_fit, mtcars, type = c("class")),
Expand All @@ -146,7 +193,7 @@ test_that("boost_tree(), objective = binary:logistic, works with type = c(class,
)
})

test_that("boost_tree(), objective = binary:logistic, works with type = c(class, prob)", {
test_that("boost_tree(), objective = multi:softprob, works with type = c(class, prob)", {
skip_if_not_installed("parsnip")
skip_if_not_installed("tidypredict")
skip_if_not_installed("xgboost")
Expand All @@ -157,6 +204,9 @@ test_that("boost_tree(), objective = binary:logistic, works with type = c(class,

orb_obj <- orbital(bt_fit, type = c("class", "prob"))

# to avoid exact split values
iris[, -5] <- iris[, -5] + 0.05

preds <- predict(orb_obj, iris)
exps <- dplyr::bind_cols(
predict(bt_fit, iris, type = c("class")),
Expand Down
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