===================
Evolutionary Forest
===================
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:alt: Updates
An open source python library for automated feature engineering based on Genetic Programming
* Free software: BSD license
* Documentation: https://evolutionary-forest.readthedocs.io.
Introduction
----------------
Feature engineering is a long-standing issue that has plagued machine learning practitioners for many years. Deep learning techniques have significantly reduced the need for manual feature engineering in recent years. However, a critical issue is that the features discovered by deep learning methods are difficult to interpret.
In the domain of interpretable machine learning, genetic programming has demonstrated to be a promising method for automated feature construction, as it can improve the performance of traditional machine learning systems while maintaining similar interpretability. Nonetheless, such a potent method is rarely mentioned by practitioners. We believe that the main reason for this phenomenon is that there is still a lack of a mature package that can automatically build features based on the genetic programming algorithm. As a result, we propose this package with the goal of providing a powerful feature construction tool for enhancing existing state-of-the-art machine learning algorithms, particularly decision-tree based algorithms.
Features
----------------
* A powerful feature construction tool for generating interpretable machine learning features.
* A reliable machine learning model has powerful performance on the small dataset.
Installation
--------------------------------
From PyPI:
.. code:: bash
pip install -U evolutionary_forest
From GitHub (Latest Code):
.. code:: bash
pip install git+https://github.com/hengzhe-zhang/EvolutionaryForest.git
Supported Algorithms
--------------------------------
* `Evolutionary Forest (TEVC 2021) <https://github.com/hengzhe-zhang/EvolutionaryForest/blob/master/experiment/methods/EF.py>`_
* `SR-Forest (TEVC 2023) <https://github.com/hengzhe-zhang/EvolutionaryForest/blob/master/experiment/methods/SRForest.py>`_
Example
----------------
An example of usage:
.. code:: Python
X, y = load_diabetes(return_X_y=True)
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
r = EvolutionaryForestRegressor(max_height=3, normalize=True, select='AutomaticLexicase',
gene_num=10, boost_size=100, n_gen=20, n_pop=200, cross_pb=1,
base_learner='Random-DT', verbose=True)
r.fit(x_train, y_train)
print(r2_score(y_test, r.predict(x_test)))
An example of improvements brought about by constructed features:
.. image:: https://raw.githubusercontent.com/zhenlingcn/EvolutionaryForest/master/docs/constructed_features.png
Tutorials
----------------
Here are some nodebook examples of using Evolutionary Forest:
* `Regression on Diabetes Dataset`_
.. _Regression on Diabetes Dataset: https://github.com/hengzhe-zhang/EvolutionaryForest/blob/master/tutorial/diabetes_regression.ipynb
Documentation
----------------
Tutorial: `English Version`_ | `中文版本`_
.. _English Version: https://github.com/zhenlingcn/EvolutionaryForest/blob/master/tutorial/diabetes_regression.ipynb
.. _中文版本: https://github.com/zhenlingcn/EvolutionaryForest/blob/master/tutorial/diabetes_regression-CN.md
Credits
---------------
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
Citation
---------------
Please cite our paper if you find it helpful :)
.. code::
@article{zhang2021evolutionary,
title={An Evolutionary Forest for Regression},
author={Zhang, Hengzhe and Zhou, Aimin and Zhang, Hu},
journal={IEEE Transactions on Evolutionary Computation},
volume={26},
number={4},
pages={735--749},
year={2021},
publisher={IEEE}
}
@article{zhang2023sr,
title={SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method},
author={Zhang, Hengzhe and Zhou, Aimin and Chen, Qi and Xue, Bing and Zhang, Mengjie},
journal={IEEE Transactions on Evolutionary Computation},
year={2023},
publisher={IEEE}
}
=======
History
=======
0.1.0 (2021-05-22)
------------------
* First release on PyPI.
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"description": "===================\r\nEvolutionary Forest\r\n===================\r\n\r\n\r\n.. image:: https://img.shields.io/pypi/v/evolutionary_forest.svg\r\n :target: https://pypi.python.org/pypi/evolutionary_forest\r\n\r\n.. image:: https://img.shields.io/travis/com/zhenlingcn/evolutionaryforest.svg\r\n :target: https://www.travis-ci.com/github/zhenlingcn/EvolutionaryForest\r\n\r\n.. image:: https://readthedocs.org/projects/evolutionary-forest/badge/?version=latest\r\n :target: https://evolutionary-forest.readthedocs.io/en/latest/?version=latest\r\n :alt: Documentation Status\r\n\r\n\r\n.. image:: https://pyup.io/repos/github/zhenlingcn/evolutionary_forest/shield.svg\r\n :target: https://pyup.io/repos/github/zhenlingcn/evolutionary_forest/\r\n :alt: Updates\r\n\r\n\r\n\r\nAn open source python library for automated feature engineering based on Genetic Programming\r\n\r\n\r\n* Free software: BSD license\r\n* Documentation: https://evolutionary-forest.readthedocs.io.\r\n\r\n\r\nIntroduction\r\n----------------\r\n\r\nFeature engineering is a long-standing issue that has plagued machine learning practitioners for many years. Deep learning techniques have significantly reduced the need for manual feature engineering in recent years. However, a critical issue is that the features discovered by deep learning methods are difficult to interpret.\r\n\r\nIn the domain of interpretable machine learning, genetic programming has demonstrated to be a promising method for automated feature construction, as it can improve the performance of traditional machine learning systems while maintaining similar interpretability. Nonetheless, such a potent method is rarely mentioned by practitioners. We believe that the main reason for this phenomenon is that there is still a lack of a mature package that can automatically build features based on the genetic programming algorithm. As a result, we propose this package with the goal of providing a powerful feature construction tool for enhancing existing state-of-the-art machine learning algorithms, particularly decision-tree based algorithms.\r\n\r\nFeatures\r\n----------------\r\n\r\n* A powerful feature construction tool for generating interpretable machine learning features.\r\n* A reliable machine learning model has powerful performance on the small dataset.\r\n\r\nInstallation\r\n--------------------------------\r\nFrom PyPI:\r\n\r\n.. code:: bash\r\n\r\n pip install -U evolutionary_forest\r\n\r\nFrom GitHub (Latest Code):\r\n\r\n.. code:: bash\r\n\r\n pip install git+https://github.com/hengzhe-zhang/EvolutionaryForest.git\r\n\r\nSupported Algorithms\r\n--------------------------------\r\n* `Evolutionary Forest (TEVC 2021) <https://github.com/hengzhe-zhang/EvolutionaryForest/blob/master/experiment/methods/EF.py>`_\r\n* `SR-Forest (TEVC 2023) <https://github.com/hengzhe-zhang/EvolutionaryForest/blob/master/experiment/methods/SRForest.py>`_\r\n\r\n\r\nExample\r\n----------------\r\nAn example of usage:\r\n\r\n.. code:: Python\r\n\r\n X, y = load_diabetes(return_X_y=True)\r\n x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\r\n r = EvolutionaryForestRegressor(max_height=3, normalize=True, select='AutomaticLexicase',\r\n gene_num=10, boost_size=100, n_gen=20, n_pop=200, cross_pb=1,\r\n base_learner='Random-DT', verbose=True)\r\n r.fit(x_train, y_train)\r\n print(r2_score(y_test, r.predict(x_test)))\r\n\r\nAn example of improvements brought about by constructed features:\r\n\r\n.. image:: https://raw.githubusercontent.com/zhenlingcn/EvolutionaryForest/master/docs/constructed_features.png\r\n\r\nTutorials\r\n----------------\r\nHere are some nodebook examples of using Evolutionary Forest:\r\n\r\n* `Regression on Diabetes Dataset`_\r\n\r\n.. _Regression on Diabetes Dataset: https://github.com/hengzhe-zhang/EvolutionaryForest/blob/master/tutorial/diabetes_regression.ipynb\r\n\r\nDocumentation\r\n----------------\r\nTutorial: `English Version`_ | `\u4e2d\u6587\u7248\u672c`_\r\n\r\n.. _English Version: https://github.com/zhenlingcn/EvolutionaryForest/blob/master/tutorial/diabetes_regression.ipynb\r\n.. _\u4e2d\u6587\u7248\u672c: https://github.com/zhenlingcn/EvolutionaryForest/blob/master/tutorial/diabetes_regression-CN.md\r\n\r\nCredits\r\n---------------\r\n\r\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\r\n\r\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\r\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\r\n\r\nCitation\r\n---------------\r\n\r\nPlease cite our paper if you find it helpful :)\r\n\r\n.. code::\r\n\r\n @article{zhang2021evolutionary,\r\n title={An Evolutionary Forest for Regression},\r\n author={Zhang, Hengzhe and Zhou, Aimin and Zhang, Hu},\r\n journal={IEEE Transactions on Evolutionary Computation},\r\n volume={26},\r\n number={4},\r\n pages={735--749},\r\n year={2021},\r\n publisher={IEEE}\r\n }\r\n\r\n @article{zhang2023sr,\r\n title={SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method},\r\n author={Zhang, Hengzhe and Zhou, Aimin and Chen, Qi and Xue, Bing and Zhang, Mengjie},\r\n journal={IEEE Transactions on Evolutionary Computation},\r\n year={2023},\r\n publisher={IEEE}\r\n }\r\n\r\n\r\n=======\r\nHistory\r\n=======\r\n\r\n0.1.0 (2021-05-22)\r\n------------------\r\n\r\n* First release on PyPI.\r\n",
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