From c6b7ea91ab6d7d921dc327c4e48fbc1573378f06 Mon Sep 17 00:00:00 2001 From: alejandro Date: Sun, 13 Jan 2019 21:22:31 +0000 Subject: [PATCH] Added data --- MANIFEST.in | 1 + README.md | 7 +- dist/xai-0.0.3-py3.6.egg | Bin 23519 -> 361752 bytes examples/XAI Example Usage.ipynb | 332 +++++-------------------------- setup.py | 2 +- 5 files changed, 51 insertions(+), 291 deletions(-) diff --git a/MANIFEST.in b/MANIFEST.in index 0aec855..9e778a6 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -2,5 +2,6 @@ include README.md include requirements.txt recursive-include xai *.pickle recursive-exclude * __pycache__ +recursive-include xai *.csv recursive-exclude * *.py[co] recursive-include docs *.md *.jpg *.png *.gif diff --git a/README.md b/README.md index d15678d..89a66c8 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ ![GitHub](https://img.shields.io/badge/Release-ALPHA-yellow.svg) -![GitHub](https://img.shields.io/badge/Version-0.0.3_ALPHA-lightgrey.svg) +![GitHub](https://img.shields.io/badge/Version-0.0.4_ALPHA-lightgrey.svg) ![GitHub](https://img.shields.io/badge/Python-3.5_|_3.6-blue.svg) ![GitHub](https://img.shields.io/badge/License-MIT-lightgrey.svg) [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity) @@ -10,7 +10,7 @@ XAI is a Machine Learning library that is designed with AI explainability in its You can find the documentation at [https://ethicalml.github.io/xai/index.html](https://ethicalml.github.io/xai/index.html). You can also check out our [talk at Tensorflow London](https://www.youtube.com/watch?v=GZpfBhQJ0H4) where the idea was first conceived - the talk also contains an insight on the definitions and principles in this library. -# 0.0.3 - ALPHA Version +# 0.0.4 - ALPHA Version This library is currently in early stage developments and hence it will be quite unstable due to the fast updates. It is important to bare this in mind if using it in production. @@ -47,7 +47,8 @@ You can find example usage in the examples folder. With XAI you can identify imbalances in the data. 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