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evolutionary-forest-0.2.2
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مشاهده بیشترتوضیحات
An open source python library for automated feature engineering based on Genetic Programming
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | evolutionary-forest-0.2.2 |
| نام | evolutionary-forest |
| نسخه کتابخانه | 0.2.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Hengzhe Zhang |
| ایمیل نویسنده | zhenlingcn@foxmail.com |
| آدرس صفحه اصلی | https://github.com/zhenlingcn/evolutionary_forest |
| آدرس اینترنتی | https://pypi.org/project/evolutionary-forest/ |
| مجوز | BSD license |
===================
Evolutionary Forest
===================
.. image:: https://img.shields.io/pypi/v/evolutionary_forest.svg
:target: https://pypi.python.org/pypi/evolutionary_forest
.. image:: https://img.shields.io/travis/com/zhenlingcn/evolutionaryforest.svg
:target: https://www.travis-ci.com/github/zhenlingcn/EvolutionaryForest
.. image:: https://readthedocs.org/projects/evolutionary-forest/badge/?version=latest
:target: https://evolutionary-forest.readthedocs.io/en/latest/?version=latest
:alt: Documentation Status
.. image:: https://pyup.io/repos/github/zhenlingcn/evolutionary_forest/shield.svg
:target: https://pyup.io/repos/github/zhenlingcn/evolutionary_forest/
: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
--------------------------------
.. code:: bash
pip install -U evolutionary_forest
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.
نیازمندی
| مقدار | نام |
|---|---|
| - | scipy |
| - | hdfe |
| - | numpy |
| - | seaborn |
| - | matplotlib |
| - | deap |
| - | sympy |
| - | pandas |
| - | scikit-learn |
| - | dill |
| - | lightgbm |
| - | smt |
| - | pytest |
| - | joblib |
| - | linear-tree |
| - | mlxtend |
| - | sklearn2pmml |
| - | tpot |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl evolutionary-forest-0.2.2:
pip install evolutionary-forest-0.2.2.whl
نصب پکیج tar.gz evolutionary-forest-0.2.2:
pip install evolutionary-forest-0.2.2.tar.gz