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pudu

pudu: A Python library for agnostic feature selection and explainability of Machine Learning spectroscopic problems - Published in JOSS (2023)

https://github.com/pudu-py/pudu

Science Score: 93.0%

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    CITATION.cff file
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    Found 1 DOI reference(s) in JOSS metadata
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    Links to: joss.theoj.org
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    JOSS paper metadata
    Published in Journal of Open Source Software
Last synced: 6 months ago · JSON representation

Repository

A Python library for explainability of machine learinng algorithms in an agnostic, deterministic, and simple way.

Basic Info
  • Host: GitHub
  • Owner: pudu-py
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 31.6 MB
Statistics
  • Stars: 4
  • Watchers: 1
  • Forks: 1
  • Open Issues: 0
  • Releases: 11
Created over 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme Contributing License

README.md

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A Python library for explainability of machine learning algorithms for spectroscopic data in an agnostic, deterministic, and simple way.

  • GitHub repo: https://github.com/pudu-py/pudu
  • Documentation: https://pudu-py.github.io/pudu
  • PyPI: https://pypi.python.org/pypi/pudu
  • Conda-forge: https://anaconda.org/conda-forge/pudu
  • Free software: MIT license

Introduction

pudu is a Python package that helps interpret and explore the results of machine learning algorithms with spectroscopic data. It does this by quantifying the change in the prediction according to the change in the features. This library works with classification and regression problems with both 1-d and 2-d problems. actiIn order to see the exact procedure and format needed, please refer to the examples in the docs.

Features

The following is a list of the main features that pudu package enables.

  • Importance: measures the change in prediction according to perturbations in the features.
  • Speed: calculates how fast a prediction changes according to different perturbation levels.
  • Synergy: tests the synergy between features and the change in classification probability.
  • Re-activations: Evaluates how activations maps from CNN’s change according to perturbations in the data.
  • Easy plotting with ample personalization options for all the cases above.

Quickstart

  1. Install this library using pip::

    pip install pudu

  2. Install this library using conda-forge::

    conda install -c conda-forge pudu

  3. Test it by running one of the examples in the docs.

  4. If you find this library useful, please consider a reference or citation as::

    @misc{Grau-Luque2023Pudu,
author = {E. Grau-Luque, I. Becerril-Romero, A. Perez-Rodriguez, M. Guc, V. Izquierdo-Roca},
title = {pudu},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/pudu-py/pudu}},
}

  5. Stay up-to-date by updating the library using::

    conda update pudu pip install --update pudu

  6. If you encounter problems when updating, try uninstalling and then re-installing::

    pip uninstall pudu
conda remove pudu

Credits

This package was created with Cookiecutter and the giswqs/pypackage project template.

JOSS Publication

pudu: A Python library for agnostic feature selection and explainability of Machine Learning spectroscopic problems
Published
December 12, 2023
DOI
Volume 8, Issue 92, Page 5873
Authors
Enric Grau-Luque ORCID
Catalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre 1, 08930 Sant Adrià de Besòs, Spain.
Ignacio Becerril-Romero ORCID
Catalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre 1, 08930 Sant Adrià de Besòs, Spain.
Alejandro Perez-Rodriguez ORCID
Catalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre 1, 08930 Sant Adrià de Besòs, Spain., Departament d'Enginyeria Electrònica i Biomèdica, IN2UB, Universitat de Barcelona, C/ Martí i Franqués 1, 08028 Barcelona, Spain.
Maxim Guc ORCID
Catalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre 1, 08930 Sant Adrià de Besòs, Spain.
Victor Izquierdo-Roca ORCID
Catalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre 1, 08930 Sant Adrià de Besòs, Spain.
Editor
Arfon Smith ORCID
Tags
Spectroscopy Machine Learning Explainability and intepretability Classification and regression
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GitHub Events

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Committers

Last synced: 7 months ago

All Time
  • Total Commits: 110
  • Total Committers: 4
  • Avg Commits per committer: 27.5
  • Development Distribution Score (DDS): 0.473
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  • Committers: 1
  • Avg Commits per committer: 4.0
  • Development Distribution Score (DDS): 0.0
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enricgrau p****c@g****m 58
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Committer Domains (Top 20 + Academic)
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Last synced: 6 months ago

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  • Total issues: 4
  • Total pull requests: 1
  • Average time to close issues: 23 days
  • Average time to close pull requests: N/A
  • Total issue authors: 3
  • Total pull request authors: 1
  • Average comments per issue: 2.75
  • Average comments per pull request: 3.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
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  • Pull requests: 1
  • Average time to close issues: N/A
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  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 3.0
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  • hbaniecki (2)
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  • jongari7 (3)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 25 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 10
  • Total maintainers: 1
pypi.org: pudu

A Python library for explainability of machine learinng algorithms in an agnostic, deterministic, and simple way.

  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 25 Last month
Rankings
Dependent packages count: 6.6%
Average: 24.4%
Downloads: 26.0%
Stargazers count: 28.2%
Forks count: 30.5%
Dependent repos count: 30.6%
Maintainers (1)
GrauJack
Last synced: 6 months ago

Dependencies

requirements.txt pypi
  • matplotlib *
  • numpy *
  • pandas *
  • spectrapepper *
requirements_dev.txt pypi
  • Sphinx * development
  • bump2version * development
  • coverage * development
  • flake8 * development
  • grip * development
  • pip * development
  • tox * development
  • twine * development
  • watchdog * development
  • wheel * development
setup.py pypi
  • x.strip *
.github/workflows/build.yml actions
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  • actions/setup-python v4.3.0 composite
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  • github/codeql-action/init v2 composite
.github/workflows/docs.yml actions
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.github/workflows/pypi.yml actions
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.github/workflows/updater.yml actions
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examples/examples_requirements.txt pypi
  • lime *
  • localreg *
  • pickle *
  • spectrapepper *
pyproject.toml pypi