[PYTHON] Adjusting LightGBM parameters with Optuna

• I confirmed that it works with Colab.

reference

https://tech.preferred.jp/ja/blog/hyperparameter-tuning-with-optuna-integration-lightgbm-tuner/

Install Optuna

pip install optuna

Parameter search

import optuna

def objective(trial):
    #x_train, y_train, x_test, y_Prepare test

    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 and y_Calculate the evaluation value with test

    return precision #This time, parameter search with precision
 
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)