==================== Introduction to pudu ==================== .. image:: https://img.shields.io/pypi/v/pudu.svg :target: https://pypi.python.org/pypi/pudu .. image:: https://img.shields.io/conda/vn/conda-forge/pudu.svg :target: https://anaconda.org/conda-forge/pudu .. image:: https://img.shields.io/badge/License-MIT-yellow.svg :target: https://opensource.org/licenses/MIT .. image:: https://github.com/pudu-py/pudu/actions/workflows/codeql.yml/badge.svg :target: https://github.com/pudu-py/pudu/actions/workflows/codeql.yml .. image:: https://github.com/pudu-py/pudu/workflows/docs/badge.svg :target: https://pudu-py.github.io/pudu .. image:: https://codecov.io/gh/spectrapepper/pudu/branch/main/graph/badge.svg?token=DC0QIwuYel :target: https://codecov.io/gh/spectrapepper/spectrapepper .. image:: https://img.shields.io/conda/dn/conda-forge/pudu.svg?color=blue&label=conda%20downloads :target: https://pepy.tech/project/pudu .. image:: https://static.pepy.tech/personalized-badge/pudu?period=total&units=international_system&left_color=grey&left_text=pypi%20downloads&right_color=blue :target: https://pepy.tech/project/pudu .. image:: https://img.shields.io/badge/stackoverflow-Ask%20a%20question-brown :target: https://stackoverflow.com/questions/tagged/pudu **A Python library for explainability of machine learinng algorithms 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 **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. Features -------- The following is a list of the main procedures that **pudu** package enables. 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 CNNs 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.