https://optuna.readthedocs.io/en/latest/

simple_example_optuna.py

import sklearn
import sklearn.datasets
import sklearn.ensemble
import sklearn.model_selection
import sklearn.svm

import optuna

# Define an objective function to be minimized.
def objective(trial):

    # Invoke suggest methods of a Trial object to generate hyperparameters.
    regressor_name = trial.suggest_categorical("classifier", ["SVR", "RandomForest"])
    if regressor_name == "SVR":
        svr_c = trial.suggest_loguniform("svr_c", 1e-10, 1e10)
        regressor_obj = sklearn.svm.SVR(C=svr_c)
    else:
        rf_max_depth = trial.suggest_int("rf_max_depth", 2, 32)
        regressor_obj = sklearn.ensemble.RandomForestRegressor(max_depth=rf_max_depth)

    X, y = sklearn.datasets.load_boston(return_X_y=True)
    X_train, X_valid, y_train, y_valid = sklearn.model_selection.train_test_split(X, y, random_state=0)

    regressor_obj.fit(X_train, y_train)
    y_pred = regressor_obj.predict(X_valid)

    error = sklearn.metrics.mean_squared_error(y_valid, y_pred)
    return error # A objective value linked with the Trial object.


study = optuna.create_study()  # Create a new study.
study.optimize(objective, n_trials=100)  # Invoke optimization of the objective function.