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        "description": "<p align=\"center\">\n  <img src=\"doc/source/_static/Alibi_Logo.png\" alt=\"Alibi Logo\" width=\"50%\">\n</p>\n\n[![Build Status](https://travis-ci.com/SeldonIO/alibi.svg?branch=master)](https://travis-ci.com/SeldonIO/alibi)\n[![Documentation Status](https://readthedocs.org/projects/alibi/badge/?version=latest)](https://docs.seldon.io/projects/alibi/en/latest/?badge=latest)\n![Python version](https://img.shields.io/badge/python-3.5%20%7C%203.6%20%7C%203.7-blue.svg)\n[![PyPI version](https://badge.fury.io/py/alibi.svg)](https://badge.fury.io/py/alibi)\n![GitHub Licence](https://img.shields.io/github/license/seldonio/alibi.svg)\n[![Slack channel](https://img.shields.io/badge/chat-on%20slack-e51670.svg)](http://seldondev.slack.com/messages/alibi)\n---\n[Alibi](https://docs.seldon.io/projects/alibi) is an open source Python library aimed at machine learning model inspection and interpretation. The initial focus on the library is on black-box, instance based model explanations.\n\n*  [Documentation](https://docs.seldon.io/projects/alibi)\n\n## Goals\n* Provide high quality reference implementations of black-box ML model explanation algorithms\n* Define a consistent API for interpretable ML methods\n* Support multiple use cases (e.g. tabular, text and image data classification, regression)\n* Implement the latest model explanation, concept drift, algorithmic bias detection and other ML\n  model monitoring and interpretation methods\n\n## Installation\nAlibi can be installed from [PyPI](https://pypi.org/project/alibi):\n```bash\npip install alibi\n```\nThis will install `alibi` with all its dependencies:\n```bash\n  beautifulsoup4\n  numpy\n  Pillow\n  pandas\n  requests\n  scikit-learn\n  spacy\n  scikit-image\n  tensorflow\n```\n\nTo run all the example notebooks, you may additionally run `pip install alibi[examples]` which will\ninstall the following:\n```bash\n  seaborn\n  Keras\n```\n\n## Supported algorithms\n### Black-box model explanaton\n - Anchor explanations ([Ribeiro et al., 2018](https://homes.cs.washington.edu/~marcotcr/aaai18.pdf))\n   - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/Anchors.html)\n   - Examples:\n     [income prediction](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_tabular_adult.html),\n     [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_tabular_iris.html),\n     [movie sentiment classification](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_text_movie.html),\n     [ImageNet](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_image_imagenet.html),\n     [fashion MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/anchor_image_fashion_mnist.html)\n\n- Contrastive Explanation Method (CEM, [Dhurandhar et al., 2018](https://papers.nips.cc/paper/7340-explanations-based-on-the-missing-towards-contrastive-explanations-with-pertinent-negatives))\n  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/CEM.html)\n  - Examples: [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/cem_mnist.html),\n    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/cem_iris.html)\n\n- Counterfactual Explanations (extension of\n  [Wachter et al., 2017](https://arxiv.org/abs/1711.00399))\n  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/CF.html)\n  - Examples: \n    [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/cf_mnist.html)\n\n- Counterfactual Explanations Guided by Prototypes ([Van Looveren et al., 2019](https://arxiv.org/abs/1907.02584))\n  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/CFProto.html)\n  - Examples:\n    [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_mnist.html),\n    [Boston housing dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_housing.html),\n    [Adult income (one-hot)](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_cat_adult_ohe.html),\n    [Adult income (ordinal)](https://docs.seldon.io/projects/alibi/en/stable/examples/cfproto_cat_adult_ord.html)\n\n### Model confidence metrics\n- Trust Scores ([Jiang et al., 2018](https://arxiv.org/abs/1805.11783))\n  - [Documentation](https://docs.seldon.io/projects/alibi/en/stable/methods/TrustScores.html)\n  - Examples:\n    [MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/trustscore_mnist.html),\n    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/trustscore_mnist.html)\n- Linearity Measure\n  - Examples:\n    [Iris dataset](https://docs.seldon.io/projects/alibi/en/stable/examples/linearity_measure_iris.html),\n    [fashion MNIST](https://docs.seldon.io/projects/alibi/en/stable/examples/linearity_measure_fashion_mnist.html)\n\n## Example outputs\n\n[**Anchor method applied to the InceptionV3 model trained on ImageNet:**](examples/anchor_image_imagenet.ipynb)\n\nPrediction: Persian Cat             | Anchor explanation\n:-------------------------:|:------------------:\n![Persian Cat](doc/source/methods/persiancat.png)| ![Persian Cat Anchor](doc/source/methods/persiancatanchor.png)\n\n[**Contrastive Explanation method applied to a CNN trained on MNIST:**](examples/cem_mnist.ipynb)\n\nPrediction: 4             |  Pertinent Negative: 9               | Pertinent Positive: 4\n:-------------------------:|:-------------------:|:------------------:\n![mnist_orig](doc/source/methods/mnist_orig.png)  | ![mnsit_pn](doc/source/methods/mnist_pn.png) | ![mnist_pp](doc/source/methods/mnist_pp.png)\n\n[**Trust scores applied to a softmax classifier trained on MNIST:**](examples/trustscore_mnist.ipynb)\n\n![trust_mnist](doc/source/_static/trustscores.png)\n\n\n\n",
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