https://github.com/scikit-learn-contrib/fastcan
A Fast Canonical-Correlation-Based Feature Selection Algorithm
https://github.com/scikit-learn-contrib/fastcan
Last synced: 20 days ago
JSON representation
A Fast Canonical-Correlation-Based Feature Selection Algorithm
- Host: GitHub
- URL: https://github.com/scikit-learn-contrib/fastcan
- Owner: scikit-learn-contrib
- License: mit
- Created: 2024-05-27T05:17:33.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-04-09T08:52:13.000Z (21 days ago)
- Last Synced: 2025-04-09T09:35:49.849Z (21 days ago)
- Language: Python
- Homepage:
- Size: 1.17 MB
- Stars: 8
- Watchers: 1
- Forks: 1
- Open Issues: 7
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
Awesome Lists containing this project
README
FastCan: A Fast Canonical-Correlation-Based Feature Selection Algorithm
=======================================================================
|conda| |Codecov| |CI| |Doc| |PythonVersion| |PyPi| |Black| |ruff| |pixi|
.. |conda| image:: https://img.shields.io/conda/vn/conda-forge/fastcan.svg
:target: https://anaconda.org/conda-forge/fastcan
.. |Codecov| image:: https://codecov.io/gh/scikit-learn-contrib/fastcan/branch/main/graph/badge.svg
:target: https://codecov.io/gh/scikit-learn-contrib/fastcan
.. |CI| image:: https://github.com/scikit-learn-contrib/fastcan/actions/workflows/ci.yml/badge.svg
:target: https://github.com/scikit-learn-contrib/fastcan/actions
.. |Doc| image:: https://readthedocs.org/projects/fastcan/badge/?version=latest
:target: https://fastcan.readthedocs.io/en/latest/?badge=latest
.. |PythonVersion| image:: https://img.shields.io/pypi/pyversions/fastcan.svg
:target: https://pypi.org/project/fastcan/
.. |PyPi| image:: https://img.shields.io/pypi/v/fastcan
:target: https://pypi.org/project/fastcan
.. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
:target: https://github.com/psf/black
.. |ruff| image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json
:target: https://github.com/astral-sh/ruff
.. |pixi| image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/prefix-dev/pixi/main/assets/badge/v0.json&style=flat-square
:target: https://pixi.sh
FastCan is a feature selection method, which has following advantages:
#. Extremely **fast**.
#. Support unsupervised feature selection.
#. Support multioutput feature selection.
#. Skip redundant features.
#. Evaluate relative usefulness of features.
Check `Home Page `_ for more information.
Installation
------------
Install **FastCan** via PyPi:
* Run ``pip install fastcan``
Or via conda-forge:
* Run ``conda install -c conda-forge fastcan``
Getting Started
---------------
>>> from fastcan import FastCan
>>> X = [[ 0.87, -1.34, 0.31 ],
... [-2.79, -0.02, -0.85 ],
... [-1.34, -0.48, -2.55 ],
... [ 1.92, 1.48, 0.65 ]]
>>> y = [[0, 0], [1, 1], [0, 0], [1, 0]] # Multioutput feature selection
>>> selector = FastCan(n_features_to_select=2, verbose=0).fit(X, y)
>>> selector.get_support()
array([ True, True, False])
>>> selector.get_support(indices=True) # Sorted indices
array([0, 1])
>>> selector.indices_ # Indices in selection order
array([1, 0], dtype=int32)
>>> selector.scores_ # Scores for selected features in selection order
array([0.91162413, 0.71089547])
>>> # Here Feature 2 must be included
>>> selector = FastCan(n_features_to_select=2, indices_include=[2], verbose=0).fit(X, y)
>>> # We can find the feature which is useful when working with Feature 2
>>> selector.indices_
array([2, 0], dtype=int32)
>>> selector.scores_
array([0.34617598, 0.95815008])
NARX Time Series Modelling
--------------------------
FastCan can be used for system identification.
In particular, we provide a submodule `fastcan.narx` to build Nonlinear AutoRegressive eXogenous (NARX) models.
For more information, check our `Home Page `_.
Support Free-Threaded Wheels
----------------------------
FastCan has support for free-threaded (also known as nogil) CPython 3.13.
For more information about free-threaded CPython, check `how to install a free-threaded CPython `_.
Citation
--------
FastCan is a Python implementation of the following papers.
If you use the `h-correlation` method in your work please cite the following reference:
.. code:: bibtex
@article{ZHANG2022108419,
title = {Orthogonal least squares based fast feature selection for linear classification},
journal = {Pattern Recognition},
volume = {123},
pages = {108419},
year = {2022},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2021.108419},
url = {https://www.sciencedirect.com/science/article/pii/S0031320321005951},
author = {Sikai Zhang and Zi-Qiang Lang},
keywords = {Feature selection, Orthogonal least squares, Canonical correlation analysis, Linear discriminant analysis, Multi-label, Multivariate time series, Feature interaction},
}
If you use the `eta-cosine` method in your work please cite the following reference:
.. code:: bibtex
@article{ZHANG2025111895,
title = {Canonical-correlation-based fast feature selection for structural health monitoring},
journal = {Mechanical Systems and Signal Processing},
volume = {223},
pages = {111895},
year = {2025},
issn = {0888-3270},
doi = {https://doi.org/10.1016/j.ymssp.2024.111895},
url = {https://www.sciencedirect.com/science/article/pii/S0888327024007933},
author = {Sikai Zhang and Tingna Wang and Keith Worden and Limin Sun and Elizabeth J. Cross},
keywords = {Multivariate feature selection, Filter method, Canonical correlation analysis, Feature interaction, Feature redundancy, Structural health monitoring},
}