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OPTUNA

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.

Key Features

Optuna has modern functionalities as follows:

  • Lightweight, versatile, and platform agnostic architecture <tutorial/10_key_features/001_first>
    • Handle a wide variety of tasks with a simple installation that has few requirements.
  • Pythonic search spaces <tutorial/10_key_features/002_configurations>
    • Define search spaces using familiar Python syntax including conditionals and loops.
  • Efficient optimization algorithms <tutorial/10_key_features/003_efficient_optimization_algorithms>
    • Adopt state-of-the-art algorithms for sampling hyperparameters and efficiently pruning unpromising trials.
  • Easy parallelization <tutorial/10_key_features/004_distributed>
    • Scale studies to tens or hundreds or workers with little or no changes to the code.
  • Quick visualization <tutorial/10_key_features/005_visualization>
    • Inspect optimization histories from a variety of plotting functions.

Basic Concepts

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., classifier and svm_c) through multiple trials (e.g., n_trials=100). Optuna is a framework designed for the automation and the acceleration of the optimization studies.

Open in Colab

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.

Communication

Contribution

Any contributions to Optuna are welcome! When you send a pull request, please follow the contribution guide.

License

MIT License (see LICENSE).

Reference

Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019. Optuna: A Next-generation Hyperparameter Optimization Framework. In KDD (arXiv).

installation tutorial/index reference/index faq

Indices and tables

  • genindex
  • modindex
  • search