Metadata-Version: 2.1
Name: LORE-ext
Version: 0.2
Summary: LORE (LOcal Rule-based Explanations) is a model-agnostic explanator for tabular data
Home-page: https://www.ai4europe.eu/research/ai-catalog/lore
Author: rinziv
Author-email: rinzivillo@isti.cnr.it
License: MIT
Project-URL: Documentation, http://lore-ext.readthedocs.io/
Project-URL: Source, https://github.com/rinziv/LORE_Ext
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Description-Content-Type: text/x-rst; charset=UTF-8
License-File: LICENSE.txt
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: deap (>=1.3.1)
Requires-Dist: pillow
Requires-Dist: scipy
Requires-Dist: sklearn
Requires-Dist: bitarray (>=2.5.1)
Requires-Dist: importlib-metadata ; python_version < "3.8"
Provides-Extra: testing
Requires-Dist: setuptools ; extra == 'testing'
Requires-Dist: pytest ; extra == 'testing'
Requires-Dist: pytest-cov ; extra == 'testing'

==============
LORE Explainer
==============

LORE (LOcal Rule-based Explanations) is a model-agnostic explanator capable of producing rules to provide insight on the motivation a AI-based black box provides a specific outcome for an input instance.


The method of LORE does not make any assumption on the classifier that is used for labeling. The approach used by LORE exploits the exploration of a neighborhood of the input instance, based on a genetic algorithm to generate synthetic instances, to learn a local transparent model, which can be interpreted locally by the analyst.


.. _pyscaffold-notes:

Note
====

This project has been set up using PyScaffold 4.2.1. For details and usage
information on PyScaffold see https://pyscaffold.org/.