Metadata-Version: 2.3
Name: xwhy
Version: 0.0.1
Summary: Explain Why (XWhy) with Statistical Model-agnostic Interpretability with Local Explanations (SMILE)
Keywords: explainable-ai,xwhy,interpretability,machine-learning,deep-learning
Author: Mojgan Hashemian, Koorosh Aslansefat, Hamed Daneshvar, Sara Dehghani, Mohammad Naveed Akram, Ioannis Sorokos, Martin Walker, Yiannis Papadopoulos
Author-email: Koorosh Aslansefat <koo.ec2008@gmail.com>
License: MIT License
         
         Copyright (c) 2021 Koorosh Aslansefat
         
         Permission is hereby granted, free of charge, to any person obtaining a copy
         of this software and associated documentation files (the "Software"), to deal
         in the Software without restriction, including without limitation the rights
         to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
         copies of the Software, and to permit persons to whom the Software is
         furnished to do so, subject to the following conditions:
         
         The above copyright notice and this permission notice shall be included in all
         copies or substantial portions of the Software.
         
         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
         IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
         FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
         AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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         OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
         SOFTWARE.
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: openai>=2.43.0
Requires-Dist: numpy>=2.0.2
Requires-Dist: matplotlib==3.10.0
Requires-Dist: scikit-learn~=1.6.1
Requires-Dist: gensim>=4.3.2
Requires-Dist: pot>=0.9.6.post1
Requires-Dist: python-dotenv>=1.2.2
Requires-Dist: requests>=2.32.4
Requires-Dist: pydantic-settings>=2.14.2
Requires-Dist: xgboost>=3.3.0
Requires-Dist: google-genai>=2.10.0
Requires-Dist: anthropic>=0.116.0
Requires-Dist: huggingface-hub>=1.20.1
Requires-Dist: shap>=0.52.0
Requires-Dist: gdown>=5.2.2
Requires-Dist: xwhy[vertex] ; extra == 'all'
Requires-Dist: xwhy[aws] ; extra == 'all'
Requires-Dist: xwhy[bedrock] ; extra == 'all'
Requires-Dist: boto3>=1.43.42 ; extra == 'aws'
Requires-Dist: botocore>=1.43.42 ; extra == 'aws'
Requires-Dist: boto3>=1.43.42 ; extra == 'bedrock'
Requires-Dist: botocore>=1.43.42 ; extra == 'bedrock'
Requires-Dist: google-auth[requests]>=2,<3 ; extra == 'vertex'
Requires-Dist: google-cloud-aiplatform>=1,<2 ; extra == 'vertex'
Maintainer: Koorosh Aslansefat, Hamed Daneshvar
Maintainer-email: Koorosh Aslansefat <koo.ec2008@gmail.com>
Requires-Python: >=3.12
Project-URL: Homepage, https://github.com/Dependable-Intelligent-Systems-Lab/xwhy
Project-URL: Repository, https://github.com/Dependable-Intelligent-Systems-Lab/xwhy.git
Project-URL: Issues, https://github.com/Dependable-Intelligent-Systems-Lab/xwhy/issues
Provides-Extra: all
Provides-Extra: aws
Provides-Extra: bedrock
Provides-Extra: vertex
Description-Content-Type: text/markdown

<p align="left"> </p>

 <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT">
 <a href="https://standardjs.com"><img src="https://img.shields.io/badge/code_style-standard-brightgreen.svg" alt="Standard - \Python Style Guide"></a> 
 
# X-Why
XWhy: eXplain Why with SMILE -- Statistical Model-agnostic Interpretability with Local Explanations
 
<p align="center">
 <img src="https://github.com/koo-ec/xwhy/blob/main/docs/graphics/XWhy_Logo_v1.png" alt="XWhy, SMILE, Explainability, Interpretability, XAI, machine learning explainability, responsible ai"> </p>



## Installation
```
pip install xwhy
```

## Simple Example
```
import xwhy
import xgboost

# train an XGBoost model
X, y = xwhy.datasets.boston()
model = xgboost.XGBRegressor().fit(X, y)

# explain the model's predictions using xwhy
# (same syntax works for LightGBM, CatBoost, scikit-learn, transformers, Spark, etc.)
explainer = xwhy.Explainer(model)
xwhy_values = explainer(X)

# visualize the first prediction's explanation
xwhy.plots.waterfall(xwhy_values[0])

```
 
## Citations
It would be appreciated a citation to our paper as follows if you use X-Why for your research:
```
@article{Aslansefat2021Xwhy,
   author  = {{Aslansefat}, Koorosh and {Hashemian}, Mojgan and {Martin}, Walker and {Papadopoulos}, Yiannis},
   title   = "{SMILE: Statistical Model-agnostic Interpretability with Local Explanations}",
   journal = {arXiv e-prints},
   year    = {2021},
   url     = {https://arxiv.org/abs/...},
   eprint  = {},
}
```
 
## Acknowledgment
This project is supported by the [Secure and Safe Multi-Robot Systems (SESAME)](https://www.sesame-project.org) H2020 Project under Grant Agreement 101017258.

## Contribution 
If you are interested in contributing to this project, please check the [contribution guidelines](https://github.com/koo-ec/xwhy/blob/main/docs/contribute/contributing.md).
