{ "info": { "author": "G. Eli Jergensen", "author_email": "gelijergensen@ou.edu", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Topic :: Scientific/Engineering :: Information Analysis" ], "description": "# PermutationImportance\n\n[![Build Status](https://travis-ci.com/gelijergensen/PermutationImportance.svg?branch=master)](https://travis-ci.com/gelijergensen/PermutationImportance)\n[![Documentation Status](https://readthedocs.org/projects/permutationimportance/badge/?version=latest)](https://permutationimportance.readthedocs.io/en/latest/?badge=latest)\n\n![PermutationImportance Logo](https://github.com/gelijergensen/PermutationImportance/blob/master/docs/images/favicon.png)\n\nWelcome to the PermutationImportance library!\n\nPermutationImportance is a Python package for Python 2.7 and 3.5+ which provides\nseveral methods for computing data-based predictor importance. The methods\nimplemented are model-agnostic and can be used for any machine learning model in\nmany stages of development. The complete documentation can be found at our\n[Read The Docs](https://permutationimportance.readthedocs.io/en/latest/).\n\n## Version History\n\n- 1.2.1.5: Added documentation and examples and ensured compatibility with\n Python 3.5+\n- 1.2.1.4: Original scores are now also bootstrapped to match the other results\n- 1.2.1.3: Corrected an issue with multithreading deadlock when returned scores\n were too large\n- 1.2.1.1: Provided object to assist in constructing scoring strategies\n - Also added two new strategies with bootstrapping support\n- 1.2.1.0: Metrics can now accept kwargs and support bootstrapping\n- 1.2.0.0: Added support for Sequential Selection and completely revised backend\n for proper abstraction and extension\n - Return object now keeps track of `(context, result)` pairs\n - `abstract_variable_importance` enables implementation of custom variable\n importance methods\n - Backend is now correctly multithreaded (when specified) and is\n OS-independent\n- 1.1.0.0: 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