fastcan 0.4.1

A fast canonical-correlation-based feature selection method

fastcan is a greedy search algorithm that supports:

1. Feature selection

   • Supervised

   • Unsupervised

   • Multioutput

2. Term selection for time series regressors (e.g., NARX models)

3. Data pruning (i.e., sample selection)

Key advantages:

1. Extremely fast – Designed for high performance, even with large datasets

2. Redundancy-aware – Effectively handles feature or sample redundancy to select the most informative subset

3. Multioutput – Natively supports matrix-valued targets for multioutput tasks

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 ]]
>>> # Multioutput feature selection
>>> y = [[0, 0], [1, 1], [0, 0], [1, 0]]
>>> selector = FastCan(
...     n_features_to_select=2, verbose=0
... ).fit(X, y)
>>> selector.get_support()
array([ True,  True, False])
>>> # Sorted indices
>>> selector.get_support(indices=True)
array([0, 1])
>>> # Indices in selection order
>>> selector.indices_
array([1, 0], dtype=int32)
>>> # Scores for selected features in selection order
>>> selector.scores_
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)
>>> # 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 this NARX model example.

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.

Support WASM Wheels

fastcan is compiled to WebAssembly (WASM) wheels using pyodide, and they are available on the assets of GitHub releases. You can try it in a REPL directly in a browser. The WASM wheels of fastcan can be installed by

>>> import micropip # doctest: +SKIP
>>> await micropip.install('URL of the wasm wheel (end with _wasm32.whl)') # doctest: +SKIP

📝 Note: Due to the Cross-Origin Resource Sharing (CORS) block in web browsers, you may need Allow CORS: Access-Control-Allow-Origin Chrome extension.

📝 Note: The nightly wasm wheel of fastcan’s dependency (i.e. scikit-learn) can be found in Scientific Python Nightly Wheels.

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:

@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:

@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},
   }