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Description:

optunaintegration 4.0.0

Optuna-Integration

This package is an integration module of Optuna, an automatic Hyperparameter optimization software framework.
The modules in this package provide users with extended functionalities for Optuna in combination with third-party libraries such as PyTorch, sklearn, and TensorFlow.

[!NOTE]
You can find more information in our official documentations and API reference.

Installation
Optuna-Integration is available via pip and
on conda.
# PyPI
$ pip install optuna-integration

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

[!IMPORTANT]
As dependencies of all the modules are large and complicated, the commands above install only the common dependencies.
Dependencies for each module can be installed via pip.
For example, if you would like to install the dependencies of optuna_integration.botorch and optuna_integration.lightgbm, you can install them via:
$ pip install optuna-integration[botorch,lightgbm]

[!NOTE]
Optuna-Integration supports from Python 3.7 to Python 3.11.
Optuna Docker image is also provided at DockerHub.

Integration Modules
Here is the table of optuna-integration modules:

Third Party Library
Example

BoTorch
Unavailable

CatBoost
CatBoostPruningCallback

Dask
DaskStorage

FastAI
FastAIPruningCallback

Keras
KerasPruningCallback

LightGBM
LightGBMPruningCallback / LightGBMTuner

MLflow
MLflowCallback

MXNet
Unavailable

PyTorch Distributed
TorchDistributedTrial

PyTorch Ignite
PyTorchIgnitePruningHandler

PyTorch Lightning
PyTorchLightningPruningCallback

pycma
Unavailable

SHAP
Unavailable

scikit-learn
OptunaSearchCV

skorch
SkorchPruningCallback

TensorBoard
TensorBoardCallback

tf.keras
TFKerasPruningCallback

Weights & Biases
WeightsAndBiasesCallback

XGBoost
XGBoostPruningCallback

AllenNLP*
AllenNLPPruningCallback

Chainer*
ChainerPruningExtension

ChainerMN*
ChainerMNStudy

[!WARNING]
* shows deprecated modules and they might be removed in the future.

Communication

GitHub Discussions for questions.
GitHub Issues for bug reports and feature requests.

Contribution
Any contributions to Optuna-Integration are more than welcome!
For general guidelines how to contribute to the project, take a look at CONTRIBUTING.md.
Reference
If you use Optuna in one of your research projects, please cite our KDD paper "Optuna: A Next-generation Hyperparameter Optimization Framework":

BibTeX
@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}
}