OptunaでLightGBMのパラメータ調整

※Colabで動くことを確認しました．

参考

Optunaのインストール

pip install optuna

パラメータ探索

import optuna

def objective(trial):
    #x_train, y_train, x_test, y_testを準備

    dtrain = lgb.Dataset(x_train, label=y_train)

    param = {
        'objective': 'binary',
        'metric': 'binary_logloss',
        'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
        'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
        'num_leaves': trial.suggest_int('num_leaves', 2, 256),
        'feature_fraction': trial.suggest_uniform('feature_fraction', 0.4, 1.0),
        'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.4, 1.0),
        'bagging_freq': trial.suggest_int('bagging_freq', 1, 7),
        'min_child_samples': trial.suggest_int('min_child_samples', 5, 100),
    }

    gbm = lgb.train(param, dtrain)
    preds = gbm.predict(x_test)
    pred_labels = np.rint(preds)

    #pred_labelsとy_testで評価値を算出

    return precision #今回はprecisionでパラメータ探索

study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=100)

print('Number of finished trials:', len(study.trials))
print('Best trial:', study.best_trial.params)