==================
PS-Tree
==================
.. image:: https://img.shields.io/pypi/v/pstree.svg
:target: https://pypi.python.org/pypi/pstree
.. image:: https://img.shields.io/travis/hengzhe-zhang/pstree.svg
:target: https://travis-ci.com/hengzhe-zhang/pstree
.. image:: https://readthedocs.org/projects/pstree/badge/?version=latest
:target: https://pstree.readthedocs.io/en/latest/?version=latest
:alt: Documentation Status
An open source python library for non-linear piecewise symbolic regression based on Genetic Programming
* Free software: MIT license
* Documentation: https://pstree.readthedocs.io.
Introduction
----------------
Piece-wise non-linear regression is a long-standing problem in the machine learning domain that has long plagued machine learning researchers. It is extremely difficult for users to determine the correct partition scheme and non-linear model when there is no prior information. To address this issue, we proposed piece-wise non-linear regression tree (PS-Tree), an automated piece-wise non-linear regression method based on decision tree and genetic programming techniques. Based on such an algorithm framework, our method can produce an explainable model with high accuracy in a short period of time.
Installation
----------------
.. code:: bash
pip install -U pstree
Features
----------------
* A fully automated piece-wise non-linear regression tool
* A fast genetic programming based symbolic regression tool
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 = PSTreeRegressor(regr_class=GPRegressor, tree_class=DecisionTreeRegressor,
height_limit=6, n_pop=25, n_gen=100,
basic_primitive='optimal', size_objective=True)
r.fit(x_train, y_train)
print(r2_score(y_test, r.predict(x_test)))
Experimental results on SRBench:
.. image:: https://raw.githubusercontent.com/hengzhe-zhang/PS-Tree/master/docs/R2-result.png
Citation
----------------
.. code:: bibtex
@article{zhang2022ps,
title={PS-Tree: A piecewise symbolic regression tree},
author={Zhang, Hengzhe and Zhou, Aimin and Qian, Hong and Zhang, Hu},
journal={Swarm and Evolutionary Computation},
volume={71},
pages={101061},
year={2022},
publisher={Elsevier}
}
* By the way, I would like to express my gratitude to Qi-Hao Huang from Guangzhou University for pointing out that the "minimize" in formula (4) of the paper should be "maximize", corresponding to the code. (https://github.com/hengzhe-zhang/PS-Tree/blob/master/pstree/cluster_gp_sklearn.py#L320-L346)
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 (2021-06-28)
------------------
* First release on PyPI.
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