pankajparkar / optuna

A hyperparameter optimization framework

Optuna: A hyperparameter optimization framework

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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:

• Parallel distributed optimization
• Pruning of unpromising trials
• Lightweight, versatile, and platform agnostic architecture

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.

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.

Integrations

Integrations modules, which allow pruning, or early stopping, of unpromising trials are available for the following libraries:

• XGBoost
• LightGBM
• Chainer
• Keras
• TensorFlow
• tf.keras
• MXNet
• PyTorch Ignite
• PyTorch Lightning
• FastAI
• AllenNLP

Installation

Optuna is available at the Python Package Index and on Anaconda Cloud.

# PyPI
$ pip install optuna

# Anaconda Cloud
$ conda install -c conda-forge optuna

Optuna supports Python 3.5 or newer.

Communication

• GitHub Issues for bug reports, feature requests and questions.
• Gitter for interactive chat with developers.
• Stack Overflow for questions.

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).