An example of `StepwiseLightGBMTuner`.

lightgbm_stepwise_tuner_simple.py

import numpy as np
import sklearn.datasets
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

import optuna.integration.lightgbm as lgb


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

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

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

    model.optimize()

    booster = model.get_best_booster()
    prediction = np.rint(booster.predict(val_x, num_iteration=booster.best_iteration))
    accuracy = accuracy_score(val_y, prediction)

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