OptGBM

OptGBM (= Optuna + LightGBM) provides a scikit-learn compatible estimator that tunes hyperparameters in LightGBM with Optuna.

Examples

import optgbm as lgb
from sklearn.datasets import load_boston

reg = lgb.LGBMRegressor(random_state=0)
X, y = load_boston(return_X_y=True)

reg.fit(X, y)

y_pred = reg.predict(X, y)

By default, the following hyperparameters will be searched.

• bagging_fraction
• bagging_freq
• feature_fractrion
• lambda_l1
• lambda_l2
• max_depth
• min_data_in_leaf
• num_leaves

Installation

pip install optgbm

Testing

tox