tests/testthat/_snaps/lightgbm.md

In tidymodels/bonsai: Model Wrappers for Tree-Based Models

boost_tree with lightgbm

Code
  set_mode(set_engine(boost_tree(), "lightgbm"), "regression")
Output
  Boosted Tree Model Specification (regression)

  Computational engine: lightgbm

Code
  set_mode(set_engine(boost_tree(), "lightgbm", nrounds = 100), "classification")
Output
  Boosted Tree Model Specification (classification)

  Engine-Specific Arguments:
    nrounds = 100

  Computational engine: lightgbm

bonsai handles mtry vs mtry_prop gracefully

`mtry` must be greater than or equal to 1, not 0.5.
i `mtry` is currently being interpreted as a count rather than a proportion.
i Supply `counts = FALSE` to `set_engine()` to supply this argument as a proportion rather than a count.
i See `?train_lightgbm()` for more details.

`mtry` must be less than or equal to 1, not 3.
i `mtry` is currently being interpreted as a proportion rather than a count.
i Supply `counts = TRUE` to `set_engine()` to supply this argument as a count rather than a proportion.
i See `?train_lightgbm()` for more details.

tuning mtry vs mtry_prop

Code
  fit(set_mode(set_engine(boost_tree(mtry = tune::tune()), "lightgbm"),
  "regression"), bill_length_mm ~ ., data = penguins)
Condition
  Error in `fit()`:
  ! `feature_fraction_bynode` must be a number, not a call.

lightgbm warns if user uses param argument in set_engine()

Code
  fit(mod_spec, mpg ~ ., mtcars)
Condition
  Warning:
  Arguments passed in through `params` as a list will be ignored.
  Instead pass the arguments directly to the `...`.
Output
  parsnip model object

  LightGBM Model (1 tree)
  Objective: regression
  Fitted to dataset with 10 columns

training wrapper warns on protected arguments

Code
  .res <- fit(set_mode(set_engine(boost_tree(), "lightgbm", colnames = paste0("X",
    1:ncol(penguins))), "regression"), bill_length_mm ~ ., data = penguins)
Condition
  Warning:
  The following argument is guarded by bonsai and will not be passed to LightGBM: colnames.

Code
  .res <- fit(set_mode(set_engine(boost_tree(), "lightgbm", colnames = paste0("X",
    1:ncol(penguins)), callbacks = list(p = print)), "regression"),
  bill_length_mm ~ ., data = penguins)
Condition
  Warning:
  The following arguments are guarded by bonsai and will not be passed to LightGBM: colnames and callbacks.

Code
  .res <- fit(set_mode(set_engine(boost_tree(), "lightgbm", colnames = paste0("X",
    1:ncol(penguins))), "regression"), bill_length_mm ~ ., data = penguins)
Condition
  Warning:
  The following argument is guarded by bonsai and will not be passed to LightGBM: colnames.

Code
  fit(set_mode(set_engine(boost_tree(), "lightgbm", n_iter = 10), "regression"),
  bill_length_mm ~ ., data = penguins)
Condition
  Error in `fit()`:
  ! The `n_iter` argument passed to `set_engine()` (`?parsnip::set_engine()`) is an alias for a main model argument.
  i Please instead pass this argument via the `trees` argument to `boost_tree()` (`?parsnip::boost_tree()`).

Code
  fit(set_mode(set_engine(boost_tree(), "lightgbm", num_tree = 10), "regression"),
  bill_length_mm ~ ., data = penguins)
Condition
  Error in `fit()`:
  ! The `num_tree` argument passed to `set_engine()` (`?parsnip::set_engine()`) is an alias for a main model argument.
  i Please instead pass this argument via the `trees` argument to `boost_tree()` (`?parsnip::boost_tree()`).

Code
  fit(set_mode(set_engine(boost_tree(), "lightgbm", min_split_gain = 2),
  "regression"), bill_length_mm ~ ., data = penguins)
Condition
  Error in `fit()`:
  ! The `min_split_gain` argument passed to `set_engine()` (`?parsnip::set_engine()`) is an alias for a main model argument.
  i Please instead pass this argument via the `loss_reduction` argument to `boost_tree()` (`?parsnip::boost_tree()`).

Code
  fit(set_mode(set_engine(boost_tree(), "lightgbm", min_split_gain = 2,
  lambda_l2 = 0.5), "regression"), bill_length_mm ~ ., data = penguins)
Condition
  Error in `fit()`:
  ! The `min_split_gain` argument passed to `set_engine()` (`?parsnip::set_engine()`) is an alias for a main model argument.
  i Please instead pass this argument via the `loss_reduction` argument to `boost_tree()` (`?parsnip::boost_tree()`).

tidymodels/bonsai documentation built on July 3, 2025, 7:35 p.m.