Optuna: 一个超参数优化框架
添加时间:2024-04-22
import ... # 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_float('svr_c', 1e-10, 1e10, log=True) 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_val, y_train, y_val = sklearn.model_selection.train_test_split(X, y, random_state=0) regressor_obj.fit(X_train, y_train) y_pred = regressor_obj.predict(X_val) error = sklearn.metrics.mean_squared_error(y_val, y_pred) return error # An 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.