Metadata-Version: 2.1
Name: evolutionary-forest
Version: 0.1.5
Summary: An open source python library for automated feature engineering based on Genetic Programming
Home-page: https://github.com/zhenlingcn/evolutionary_forest
Author: Hengzhe Zhang
Author-email: zhenlingcn@foxmail.com
License: BSD license
Keywords: evolutionary_forest
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6

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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
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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
----------------

.. code:: bash

    pip install -U evolutionary_forest

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=8, normalize=True, select='AutomaticLexicase',
                                    gene_num=10, boost_size=100, n_gen=100,
                                    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

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


=======
History
=======

0.1.0 (2021-05-22)
------------------

* First release on PyPI.


