Regression example of LightGBMTuner

lightgbm_tuner_regression.py

import sklearn.datasets
from sklearn.model_selection import train_test_split

import optuna.integration.lightgbm as lgb


if __name__ == "__main__":
    X, y = sklearn.datasets.load_boston(return_X_y=True)
    train_x, val_x, train_y, val_y = train_test_split(X, y, test_size=0.25)
    dtrain = lgb.Dataset(train_x, label=train_y)
    dval = lgb.Dataset(val_x, label=val_y)

    params = {
        "objective": "binary",
        "metric": "gamma",
        "verbosity": -1,
        "boosting_type": "gbdt",
    }

    model = lgb.train(
        params, dtrain, valid_sets=[dtrain, dval], verbose_eval=100, early_stopping_rounds=100
    )

    best_params = model.params
    print("Best params:", best_params)
    print("  Params: ")
    for key, value in best_params.items():
        print("    {}: {}".format(key, value))