Skip to main content

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

Project description

image Anaconda-Server Badge image CodeQL image codecov Downloads image image status

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

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.

Project details


Download files

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

Source Distribution

pudu-0.3.4.tar.gz (20.7 kB view details)

Uploaded Source

Built Distribution

pudu-0.3.4-py2.py3-none-any.whl (18.1 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file pudu-0.3.4.tar.gz.

File metadata

  • Download URL: pudu-0.3.4.tar.gz
  • Upload date:
  • Size: 20.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for pudu-0.3.4.tar.gz
Algorithm Hash digest
SHA256 7eb0c21a966b0e960e6bc277783c03439a5065280095409b2ad52be695c9811b
MD5 8e61b24038cd60e8dcc5255078ca59b6
BLAKE2b-256 ee0a3eaf9b31891b7599b90f8b00369746b357373cc68d92af3a221e843c776e

See more details on using hashes here.

File details

Details for the file pudu-0.3.4-py2.py3-none-any.whl.

File metadata

  • Download URL: pudu-0.3.4-py2.py3-none-any.whl
  • Upload date:
  • Size: 18.1 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for pudu-0.3.4-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 858832dd4ffd5e3ced7df87340ea4f28861a8476f68a3954a239a30247bd586b
MD5 b4bb5ea10827edc6d4b9e9ee4dae36ab
BLAKE2b-256 0db6bbeed8d36a0445e8bdbc648283f87ec13555c5bd4fb6457d51cfe3922c53

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page