{ "info": { "author": "", "author_email": "", "bugtrack_url": null, "classifiers": [], "description": ".. -*- mode: rst -*-\n\n============= ================================================================\nSoftware |Licence|_ |GitHubRelease|_ |PyPi|_ |Python35|_\nDocs |Homepage|_\nCI |Travis|_ |Codecov|_\nTry it |Binder|_\nContact |MailingList|_ |Gitter|_\nCite |BibTeX|_ |DOI|_\n============= ================================================================\n\n.. |Licence| image:: https://img.shields.io/github/license/fat-forensics/fat-forensics.svg\n.. _Licence: https://github.com/fat-forensics/fat-forensics/blob/dev/LICENCE\n\n.. |GitHubRelease| image:: https://img.shields.io/github/release/fat-forensics/fat-forensics.svg\n.. _GitHubRelease: https://github.com/fat-forensics/fat-forensics/releases\n\n.. |PyPi| image:: https://img.shields.io/pypi/v/fat-forensics.svg\n.. _PyPi: https://pypi.org/project/fat-forensics/\n\n.. |Python35| image:: https://img.shields.io/badge/python-3.5-blue.svg\n.. _Python35: https://badge.fury.io/py/fat-forensics\n\n.. .. |ReadTheDocs| image:: https://readthedocs.org/projects/fat-forensics/badge/?version=latest&style=flat\n.. .. _ReadTheDocs: https://fat-forensics.readthedocs.io/en/latest/\n\n.. |Homepage| image:: https://img.shields.io/badge/homepage-read-green.svg\n.. _Homepage: https://fat-forensics.org\n.. What about wiki?\n\n.. |Travis| image:: https://travis-ci.com/fat-forensics/fat-forensics.svg\n.. _Travis: https://travis-ci.com/fat-forensics/fat-forensics\n\n.. .. |CircleCI| image:: https://circleci.com/gh/fat-forensics/fat-forensics/tree/dev.svg?style=shield\n.. .. _CircleCI: https://circleci.com/gh/fat-forensics/fat-forensics/tree/dev\n\n.. |Codecov| image:: https://codecov.io/gh/fat-forensics/fat-forensics/branch/dev/graph/badge.svg\n.. _Codecov: https://codecov.io/gh/fat-forensics/fat-forensics\n\n.. https://codeclimate.com/\n\n.. https://requires.io/\n\n.. |Binder| image:: https://mybinder.org/badge_logo.svg\n.. _Binder: https://mybinder.org/v2/gh/fat-forensics/fat-forensics-doc/master?filepath=notebooks\n\n.. |Docker| image:: https://images.microbadger.com/badges/image/anthropocentricai/ai-python.svg\n.. _Docker: https://hub.docker.com/r/anthropocentricai/ai-python\n\n.. |MailingList| image:: https://img.shields.io/badge/mailing%20list-Google%20Groups-green.svg\n.. _MailingList: https://groups.google.com/forum/#!forum/fat-forensics\n\n.. |Gitter| image:: https://img.shields.io/gitter/room/fat-forensics/FAT-Forensics.svg\n.. _Gitter: https://gitter.im/fat-forensics/fat-forensics\n\n.. |BibTeX| image:: https://img.shields.io/badge/cite-BibTeX-blue.svg\n.. _BibTeX: https://fat-forensics.org/getting_started/cite.html\n\n.. |DOI| image:: https://zenodo.org/badge/DOI/xx.xxxx/zenodo.xxxxxxx.svg\n.. _DOI: https://doi.org/xx.xxxx/zenodo.xxxxxxx\n\n============================================================================\nFAT Forensics: Algorithmic Fairness, Accountability and Transparency Toolbox\n============================================================================\n\nFAT-Forensics (``fatf``) is a Python toolbox for evaluating fairness,\naccountability and transparency of predictive systems. It is built on top of\nSciPy_ and NumPy_, and is distributed under the 3-Clause BSD license (new BSD).\n\nFAT Forensics implements the state of the art *fairness*, *accountability* and\n*transparency* (FAT) algorithms for the three main components of any data\nmodelling pipeline: *data* (raw data and features), predictive *models* and\nmodel *predictions*. We envisage two main use cases for the package, each\nsupported by distinct features implemented to support it: an interactive\n*research mode* aimed at researchers who may want to use it for an exploratory\nanalysis and a *deployment mode* aimed at practitioners who may want to use it\nfor monitoring FAT aspects of a predictive system.\n\nPlease visit the project's web site `https://fat-forensics.org`_ for more\ndetails.\n\nInstallation\n============\n\nDependencies\n------------\n\nFAT Forensics requires **Python 3.5** or higher and the following dependencies:\n\n+------------+------------+\n| Package | Version |\n+============+============+\n| NumPy_ | >=1.10.0 |\n+------------+------------+\n| SciPy_ | >=0.13.3 |\n+------------+------------+\n\nIn addition, some of the modules require *optional* dependencies:\n\n+----------------------------------------+------------------+------------+\n| ``fatf`` module | Package | Version |\n+========================================+==================+============+\n| ``fatf.transparency.lime`` | | |\n+----------------------------------------+ | |\n| ``fatf.transparency.models.lime`` | LIME_ | >=0.0.0.0 |\n+----------------------------------------+ | |\n| ``fatf.transparency.predictions.lime`` | | |\n+----------------------------------------+------------------+------------+\n| ``fatf.transparency.sklearn`` | `scikit-learn`_ | >=0.19.2 |\n+----------------------------------------+------------------+------------+\n| ``fatf.vis`` | matplotlib_ | >=3.0.0 |\n+----------------------------------------+------------------+------------+\n\nUser Installation\n-----------------\n\nThe easies way to install FAT Forensics is via ``pip``::\n\n pip install fat-forensics\n\nwhich will only installed the required dependencies. If you want to install the\npackage together with all the auxiliary dependencies please consider using the\n``[all]`` option::\n\n pip install fat-forensics[all]\n\nThe documentation provides more detailed `installation instructions `_.\n\nChangelog\n=========\n\nSee the changelog_ for a development history and project milestones.\n\nDevelopment\n===========\n\nWe welcome new contributors of all experience levels. The\n`Development Guide `_ has detailed information about contributing\ncode, documentation, tests and more. Some basic development instructions are\nincluded below.\n\nImportant Links\n---------------\n\n* Project's web site and documentation: `https://fat-forensics.org`_.\n* Official source code repository:\n `https://github.com/fat-forensics/fat-forensics`_.\n* FAT Forensics releases: `https://pypi.org/project/fat-forensics`_.\n* Issue tracker: `https://github.com/fat-forensics/fat-forensics/issues`_.\n\nSource Code\n-----------\n\nYou can check out the latest FAT Forensics source code via git with the\ncommand::\n\n git clone https://github.com/fat-forensics/fat-forensics.git\n\nContributing\n------------\n\nTo learn more about contributing to FAT Forensics, please see our\n`Contributing Guide `_.\n\nTesting\n-------\n\nYou can launch the test suite from the root directory of this repository with::\n\n make test-with-code-coverage\n\nTo run the tests you will need to have version 3.9.1 of ``pytest`` installed.\nThis package, together with other development dependencies, can be also\ninstalled with::\n\n pip install -r requirements-dev.txt\n\nor with::\n\n pip install fat-forensics[dev]\n\nSee the *Testing* section of the `Development Guide `_ page for\nmore information.\n\n Please note that the ``make test-with-code-coverage`` command will test the\n version of the package in the local ``fatf`` directory and not the one\n installed since the pytest command is preceded by ``PYTHONPATH=./``. If\n you want to test the installed version, consider using the command from the\n ``Makefile`` without the ``PYTHONPATH`` variable.\n\n To control the randomness during the tests the ``Makefile`` sets the random\n seed to ``42`` by preceding each test command with ``FATF_SEED=42``, which\n sets the environment variable responsible for that. More information about\n the setup of the *Testing Environment* is available on the\n `development `_ web page in the documentation.\n\nSubmitting a Pull Request\n-------------------------\n\nBefore opening a Pull Request, please have a look at the whole content of the\n`Contributing page `_ to make sure that your code complies with\nour guidelines.\n\nHelp and Support\n================\n\nFor help please have a look at our\n`documentation web page `_, especially the\n`Getting Started `_ page.\n\nCommunication\n-------------\n\nYou can reach out to us at:\n\n* our gitter_ channel for code-related development discussion; and\n* our `mailing list`_ for discussion about the project's future and the\n direction of the development.\n\nMore information about the communication can be found in our documentation\non the `main page `_ and\non the\n`develop page `_.\n\nCitation\n--------\n\nIf you use FAT Forensics in a scientific publication, we would appreciate\ncitations! Information on how to cite use is available on the\n`citation `_ web page in\nour documentation.\n\nAcknowledgements\n================\nThis project is the result of a collaborative research agreement between Thales\nand the University of Bristol with the initial funding provided by Thales.\n\n.. _SciPy: https://www.scipy.org/\n.. _NumPy: https://www.numpy.org/\n.. _LIME: https://github.com/marcotcr/lime\n.. _scikit-learn: https://scikit-learn.org/stable/\n.. _matplotlib: https://matplotlib.org/\n.. _`https://fat-forensics.org`: https://fat-forensics.org\n.. _inst: https://fat-forensics.org/getting_started/install_deps_os.html#installation-instructions\n.. _changelog: https://fat-forensics.org/getting_started/changelog.html\n.. _dev_guide: https://fat-forensics.org/development.html\n.. _`https://github.com/fat-forensics/fat-forensics`: https://github.com/fat-forensics/fat-forensics\n.. _`https://pypi.org/project/fat-forensics`: https://pypi.org/project/fat-forensics\n.. _`https://github.com/fat-forensics/fat-forensics/issues`: https://github.com/fat-forensics/fat-forensics/issues\n.. _contrib_guide: https://fat-forensics.org/development.html#contributing-code\n.. _dev_testing: https://fat-forensics.org/development.html#testing\n.. _dev_testing_env: https://fat-forensics.org/development.html#testing-environment\n.. _getting_started: https://fat-forensics.org/getting_started/index.html\n.. _gitter: https://gitter.im/fat-forensics/fat-forensics\n.. _`mailing list`: https://groups.google.com/forum/#!forum/fat-forensics\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "https://pypi.org/project/FAT-Forensics/#files", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://anthropocentricai.github.io/FAT-Forensics", "keywords": "", "license": "new BSD", "maintainer": "Kacper Sokol", "maintainer_email": "k.sokol@bristol.ac.uk", "name": "FAT-Forensics", "package_url": "https://pypi.org/project/FAT-Forensics/", "platform": "", "project_url": "https://pypi.org/project/FAT-Forensics/", "project_urls": { "Download": "https://pypi.org/project/FAT-Forensics/#files", "Homepage": "https://anthropocentricai.github.io/FAT-Forensics" }, "release_url": "https://pypi.org/project/FAT-Forensics/0.0.1/", "requires_dist": [ "numpy (>=1.10.0)", "scipy (>=0.13.3)", "lime ; 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