FastCan is a greedy search algorithm that supports:
- Feature selection
- Supervised
- Unsupervised
- Multioutput
- Term selection for time series regressors (e.g., NARX models)
- Data pruning (i.e., sample selection)
Key advantages:
- Extremely fast
- Redundancy-aware -- accounts for redundancy among features or samples to select the most informative subset
Check Home Page for more information.
Install FastCan via PyPi:
- Run
pip install fastcan
Or via conda-forge:
- Run
conda install -c conda-forge fastcan
>>> 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])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.
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.
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.
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},
}