Skip to main content

pyFOCI - Feature Ordering by Conditional Independence

tests codecov doc

pyFOCI provides the feature selection algorithm "Feature Ordering by Conditional Independence" (FOCI), based on a nonlinear generalization of the partial R² statistic. So it can be especially useful in strongly nonlinear data scenarios.

It is based on

  • Mona Azadkia and Sourav Chatterjee. A simple measure of conditional dependence. The Annals of Statistics, 49(6):3070–3102, 2021. [DOI] [arXiv]

  • Sebastian Fuchs. Quantifying directed dependence via dimension reduction. Journal of Multivariate Analysis 201 (2024): 105266. [DOI] [arXiv]

The Package is scikit-learn compatible. It is available on PyPI.

Refer to the documentation (API and example code) at https://m3dm-jku.github.io/pyFOCI/ .


This work has been supported by the COMET-K2 Center of the Linz Center of Mechatronics (LCM), funded by the Austrian federal government and the federal state of Upper Austria.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyfoci-0.7.1.tar.gz (175.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pyfoci-0.7.1-py3-none-any.whl (25.0 kB view details)

Uploaded Python 3

File details

Details for the file pyfoci-0.7.1.tar.gz.

File metadata

  • Download URL: pyfoci-0.7.1.tar.gz
  • Upload date:
  • Size: 175.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for pyfoci-0.7.1.tar.gz
Algorithm Hash digest
SHA256 61caaa34cd6daa59d8247888485bcfc6c4736370ba9e0970b8bacc763cca93bb
MD5 5c7e0961c201c58a0cfeb57ee058d83d
BLAKE2b-256 825851324db4052f794506179afd37b2b04a0f500e69cd2247091f343616104d

See more details on using hashes here.

Provenance

The following attestation bundles were made for pyfoci-0.7.1.tar.gz:

Publisher: release.yml on m3dm-jku/pyFOCI

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file pyfoci-0.7.1-py3-none-any.whl.

File metadata

  • Download URL: pyfoci-0.7.1-py3-none-any.whl
  • Upload date:
  • Size: 25.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for pyfoci-0.7.1-py3-none-any.whl
Algorithm Hash digest
SHA256 7b8d96f979b33d15c46a77b8cae2e76e1a7b975bc7c9e16c5a7bf746b126ef12
MD5 bf82deffcd7709a85194b47ff2310461
BLAKE2b-256 285c71ce26059ece8b0955051e60aa1452f4280d49cf774160a4305aa2f5a88c

See more details on using hashes here.

Provenance

The following attestation bundles were made for pyfoci-0.7.1-py3-none-any.whl:

Publisher: release.yml on m3dm-jku/pyFOCI

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.8.0

2 files

This release

0.7.1 This release

2 files

0.7.0

2 files

0.6.0

2 files

0.5.0

2 files

0.4.0

2 files

0.3.2

2 files

0.3.1

2 files

0.3.0

2 files

0.2.3

2 files

0.2.2

2 files

0.2.1

2 files

0.2.0

2 files

0.1.2

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page