Metadata-Version: 2.1
Name: optuna
Version: 3.0.4
Summary: A hyperparameter optimization framework
Home-page: https://optuna.org/
Author: Takuya Akiba
Author-email: akiba@preferred.jp
License: UNKNOWN
Project-URL: Source, https://github.com/optuna/optuna
Project-URL: Documentation, https://optuna.readthedocs.io
Project-URL: Bug Tracker, https://github.com/optuna/optuna/issues
Description: <div align="center"><img src="https://raw.githubusercontent.com/optuna/optuna/master/docs/image/optuna-logo.png" width="800"/></div>
        
        # Optuna: A hyperparameter optimization framework
        
        [![Python](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue)](https://www.python.org)
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        [![Gitter chat](https://badges.gitter.im/optuna/gitter.svg)](https://gitter.im/optuna/optuna)
        
        [**Website**](https://optuna.org/)
        | [**Docs**](https://optuna.readthedocs.io/en/stable/)
        | [**Install Guide**](https://optuna.readthedocs.io/en/stable/installation.html)
        | [**Tutorial**](https://optuna.readthedocs.io/en/stable/tutorial/index.html)
        
        *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.
        
        ## News
        
        - **2022-02-14** Pre-releases of Optuna 3.0 are available! Early adopters may want to upgrade and provide feedback for a smoother transition to the coming full release. You can install a pre-release version by `pip install -U --pre optuna`. Find the latest one [here](https://github.com/optuna/optuna/releases)
        
        - **2021-10-11**  Optuna 3.0 Roadmap published for review. Please take a look at the [planned improvements to Optuna](https://github.com/optuna/optuna/wiki/Optuna-V3-Roadmap), and share your feedback in the github issues. PR contributions also welcome!
        
        ## Key Features
        
        Optuna has modern functionalities as follows:
        
        - [Lightweight, versatile, and platform agnostic architecture](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/001_first.html)
          - Handle a wide variety of tasks with a simple installation that has few requirements.
        - [Pythonic search spaces](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html)
          - Define search spaces using familiar Python syntax including conditionals and loops.
        - [Efficient optimization algorithms](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/003_efficient_optimization_algorithms.html)
          - Adopt state-of-the-art algorithms for sampling hyperparameters and efficiently pruning unpromising trials.
        - [Easy parallelization](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/004_distributed.html)
          - Scale studies to tens or hundreds or workers with little or no changes to the code.
        - [Quick visualization](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/005_visualization.html)
          - 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., `regressor` and `svr_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](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/optuna/optuna-examples/blob/main/quickstart.ipynb)
        
        ```python
        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.
        ```
        
        ## Examples
        
        Examples can be found in [optuna/optuna-examples](https://github.com/optuna/optuna-examples).
        
        ## Integrations
        
        [Integrations modules](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/003_efficient_optimization_algorithms.html#integration-modules-for-pruning), which allow pruning, or early stopping, of unpromising trials are available for the following libraries:
        
        * [AllenNLP](https://github.com/optuna/optuna-examples/tree/main/allennlp)
        * [Catalyst](https://github.com/optuna/optuna-examples/tree/main/pytorch/catalyst_simple.py)
        * [Catboost](https://github.com/optuna/optuna-examples/tree/main/catboost/catboost_pruning.py)
        * [Chainer](https://github.com/optuna/optuna-examples/tree/main/chainer/chainer_integration.py)
        * FastAI ([V1](https://github.com/optuna/optuna-examples/tree/main/fastai/fastaiv1_simple.py), [V2](https://github.com/optuna/optuna-examples/tree/main/fastai/fastaiv2_simple.py))
        * [Keras](https://github.com/optuna/optuna-examples/tree/main/keras/keras_integration.py)
        * [LightGBM](https://github.com/optuna/optuna-examples/tree/main/lightgbm/lightgbm_integration.py)
        * [MXNet](https://github.com/optuna/optuna-examples/tree/main/mxnet/mxnet_integration.py)
        * [PyTorch](https://github.com/optuna/optuna-examples/tree/main/pytorch/pytorch_simple.py)
        * [PyTorch Ignite](https://github.com/optuna/optuna-examples/tree/main/pytorch/pytorch_ignite_simple.py)
        * [PyTorch Lightning](https://github.com/optuna/optuna-examples/tree/main/pytorch/pytorch_lightning_simple.py)
        * [TensorFlow](https://github.com/optuna/optuna-examples/tree/main/tensorflow/tensorflow_estimator_integration.py)
        * [tf.keras](https://github.com/optuna/optuna-examples/tree/main/tfkeras/tfkeras_integration.py)
        * [XGBoost](https://github.com/optuna/optuna-examples/tree/main/xgboost/xgboost_integration.py)
        
        
        ## Web Dashboard (experimental)
        
        The new Web dashboard is under the development at [optuna-dashboard](https://github.com/optuna/optuna-dashboard).
        It is still experimental, but much better in many regards.
        Feature requests and bug reports welcome!
        
        | Manage studies | Visualize with interactive graphs |
        | -------------- | --------------------------------- |
        | ![manage-studies](https://user-images.githubusercontent.com/5564044/97099702-4107be80-16cf-11eb-9d97-f5ceec98ce52.gif) | ![optuna-realtime-graph](https://user-images.githubusercontent.com/5564044/97099797-66e19300-16d0-11eb-826c-6977e3941fb0.gif) |
        
        Install `optuna-dashboard` via pip:
        
        ```
        $ pip install optuna-dashboard
        $ optuna-dashboard sqlite:///db.sqlite3
        ...
        Listening on http://localhost:8080/
        Hit Ctrl-C to quit.
        ```
        
        ## Installation
        
        Optuna is available at [the Python Package Index](https://pypi.org/project/optuna/) and on [Anaconda Cloud](https://anaconda.org/conda-forge/optuna).
        
        ```bash
        # PyPI
        $ pip install optuna
        ```
        
        ```bash
        # Anaconda Cloud
        $ conda install -c conda-forge optuna
        ```
        
        Optuna supports Python 3.6 or newer.
        
        Also, we also provide Optuna docker images on [DockerHub](https://hub.docker.com/r/optuna/optuna).
        
        ## Communication
        
        - [GitHub Discussions] for questions.
        - [GitHub Issues] for bug reports and feature requests.
        - [Gitter] for interactive chat with developers.
        - [Stack Overflow] for questions.
        
        [GitHub Discussions]: https://github.com/optuna/optuna/discussions
        [GitHub issues]: https://github.com/optuna/optuna/issues
        [Gitter]: https://gitter.im/optuna/optuna
        [Stack Overflow]: https://stackoverflow.com/questions/tagged/optuna
        
        
        ## Contribution
        
        Any contributions to Optuna are more than welcome!
        
        If you are new to Optuna, please check the [good first issues](https://github.com/optuna/optuna/labels/good%20first%20issue). They are relatively simple, well-defined and are 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](https://github.com/optuna/optuna/labels/contribution-welcome).
        
        For general guidelines how to contribute to the project, take a look at [CONTRIBUTING.md](./CONTRIBUTING.md).
        
        
        ## Reference
        
        Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019.
        Optuna: A Next-generation Hyperparameter Optimization Framework. In KDD ([arXiv](https://arxiv.org/abs/1907.10902)).
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: benchmark
Provides-Extra: checking
Provides-Extra: document
Provides-Extra: integration
Provides-Extra: optional
Provides-Extra: test
