tuning_lightgbm.py

# License: MIT

import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.datasets import load_digits

from openbox import get_config_space, get_objective_function
from openbox import Optimizer


if __name__ == "__main__":
    # prepare your data
    X, y = load_digits(return_X_y=True)
    x_train, x_val, y_train, y_val = train_test_split(X, y, test_size=0.2, stratify=y, random_state=1)

    # get config_space and objective_function
    config_space = get_config_space('lightgbm')
    objective_function = get_objective_function('lightgbm', x_train, x_val, y_train, y_val)

    # run
    opt = Optimizer(
        objective_function,
        config_space,
        max_runs=100,
        surrogate_type='prf',
        time_limit_per_trial=180,
        task_id='tuning_lightgbm',
    )
    history = opt.run()

    print(history)

    history.plot_convergence()
    plt.show()

    # install pyrfr to use get_importance()
    print(history.get_importance())

    # history.visualize_jupyter()