Optuna: A hyperparameter optimization framework
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API. Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters.
Optuna has modern functionalities as follows:
- Parallel distributed optimization
- Pruning of unpromising trials
- Lightweight, versatile, and platform agnostic architecture
We use the terms study and trial as follows:
- Study: optimization based on an objective function
- Trial: a single execution of the objective function
Please refer to sample code below. The goal of a study is to find out the optimal set of
hyperparameter values (e.g.,
svm_c) through multiple trials (e.g.,
n_trials=100). Optuna is a framework designed for the automation and the acceleration of the
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_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_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 # 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.
To install Optuna, use
pip as follows:
$ pip install optuna
Optuna supports Python 3.5 or newer.
Any contributions to Optuna are welcome! When you send a pull request, please follow the contribution guide.
MIT License (see LICENSE).
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019. Optuna: A Next-generation Hyperparameter Optimization Framework. In KDD (arXiv).