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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.
INGO), a sampler newly supported in OptunaHub.ArtifactStore officially supported from Optuna 4.0.CatCMA, a sampler newly supported in OptunaHub.TPESampler in Optuna 4.0.pip install -U --pre optuna. Find the latest here.Optuna has modern functionalities as follows:
We use the terms study and trial as follows:
Please refer to the sample code below. The goal of a study is to find out the optimal set of
hyperparameter values (e.g., regressor and svr_c) through multiple trials (e.g.,
n_trials=100). Optuna is a framework designed for automation and acceleration of
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('regressor', ['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.fetch_california_housing(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.
[!NOTE] More examples can be found in optuna/optuna-examples.
The examples cover diverse problem setups such as multi-objective optimization, constrained optimization, pruning, and distributed optimization.
Optuna is available at the Python Package Index and on Anaconda Cloud.
# PyPI
$ pip install optuna
# Anaconda Cloud
$ conda install -c conda-forge optuna
[!IMPORTANT] Optuna supports Python 3.7 or newer.
Also, we provide Optuna docker images on DockerHub.
Optuna has integration features with various third-party libraries. Integrations can be found in optuna/optuna-integration and the document is available here.
Optuna Dashboard is a real-time web dashboard for Optuna. You can check the optimization history, hyperparameter importance, etc. in graphs and tables. You don't need to create a Python script to call Optuna's visualization functions. Feature requests and bug reports are welcome!
optuna-dashboard can be installed via pip:
$ pip install optuna-dashboard
[!TIP] Please check out the convenience of Optuna Dashboard using the sample code below.
Save the following code as optimize_toy.py.
import optuna
def objective(trial):
x1 = trial.suggest_float("x1", -100, 100)
x2 = trial.suggest_float("x2", -100, 100)
return x1 ** 2 + 0.01 * x2 ** 2
study = optuna.create_study(storage="sqlite:///db.sqlite3") # Create a new study with database.
study.optimize(objective, n_trials=100)
Then try the commands below:
# Run the study specified above
$ python optimize_toy.py
# Launch the dashboard based on the storage `sqlite:///db.sqlite3`
$ optuna-dashboard sqlite:///db.sqlite3
...
Listening on http://localhost:8080/
Hit Ctrl-C to quit.
OptunaHub is a feature-sharing platform for Optuna. You can use the registered features and publish your packages.
optunahub can be installed via pip:
$ pip install optunahub
You can load registered module with optunahub.load_module.
import optuna
import optunahub
def objective(trial: optuna.Trial) -> float:
x = trial.suggest_float("x", 0, 1)
return x
mod = optunahub.load_module("samplers/simulated_annealing")
study = optuna.create_study(sampler=mod.SimulatedAnnealingSampler())
study.optimize(objective, n_trials=20)
print(study.best_trial.value, study.best_trial.params)
For more details, please refer to the optunahub documentation.
You can publish your package via optunahub-registry. See the OptunaHub tutorial.
Any contributions to Optuna are more than welcome!
If you are new to Optuna, please check the good first issues. They are relatively simple, well-defined, and often good starting points for you to get familiar with the contribution workflow and other developers.
If you already have contributed to Optuna, we recommend the other contribution-welcome issues.
For general guidelines on how to contribute to the project, take a look at CONTRIBUTING.md.
If you use Optuna in one of your research projects, please cite our KDD paper "Optuna: A Next-generation Hyperparameter Optimization Framework":
@inproceedings{akiba2019optuna,
title={{O}ptuna: A Next-Generation Hyperparameter Optimization Framework},
author={Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori},
booktitle={The 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages={2623--2631},
year={2019}
}
MIT License (see LICENSE).
Optuna uses the codes from SciPy and fdlibm projects (see LICENSE_THIRD_PARTY).
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