Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Removing the deprecated categorical_feature parameter from lightgbm.train(...) function calls. #454

Merged
merged 3 commits into from
Sep 25, 2024
Merged

Removing the deprecated categorical_feature parameter from lightgbm.train(...) function calls. #454

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
e49c9f0
Removing the deprecated `categorical_feature` parameter from lightgbm…
drivanov Sep 18, 2024
830ce79
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 18, 2024
a6de849
Merge branch 'master' into cat_features
drivanov Sep 23, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 4 additions & 9 deletions torch_frame/gbdt/tuned_lightgbm.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -103,7 +103,6 @@ def objective(
trial: Any, # optuna.trial.Trial
train_data: Any, # lightgbm.Dataset
eval_data: Any, # lightgbm.Dataset
cat_features: list[int],
num_boost_round: int,
) -> float:
r"""Objective function to be optimized.
Expand All @@ -112,8 +111,6 @@ def objective(
trial (optuna.trial.Trial): Optuna trial object.
train_data (lightgbm.Dataset): Train data.
eval_data (lightgbm.Dataset): Validation data.
cat_features (list[int]): Array containing indexes of
categorical features.
num_boost_round (int): Number of boosting round.

Returns:
Expand Down Expand Up @@ -169,8 +166,7 @@ def objective(

boost = lightgbm.train(
self.params, train_data, num_boost_round=num_boost_round,
categorical_feature=cat_features, valid_sets=[eval_data],
callbacks=[
valid_sets=[eval_data], callbacks=[
lightgbm.early_stopping(stopping_rounds=50, verbose=False),
lightgbm.log_evaluation(period=2000)
])
Expand Down Expand Up @@ -199,19 +195,18 @@ def _tune(
assert train_y is not None
assert val_y is not None
train_data = lightgbm.Dataset(train_x, label=train_y,
categorical_feature=cat_features,
free_raw_data=False)
eval_data = lightgbm.Dataset(val_x, label=val_y, free_raw_data=False)

study.optimize(
lambda trial: self.objective(trial, train_data, eval_data,
cat_features, num_boost_round),
num_trials)
num_boost_round), num_trials)
self.params.update(study.best_params)

self.model = lightgbm.train(
self.params, train_data, num_boost_round=num_boost_round,
categorical_feature=cat_features, valid_sets=[eval_data],
callbacks=[
valid_sets=[eval_data], callbacks=[
lightgbm.early_stopping(stopping_rounds=50, verbose=False),
lightgbm.log_evaluation(period=2000)
])
Expand Down
Loading