Metadata-Version: 2.1
Name: PiML
Version: 0.4.0
Summary: A low-code interpretable machine learning toolbox in Python.
Home-page: https://github.com/SelfExplainML/PiML-Toolbox
Author: Sudjianto, Agus and Zhang, Aijun and Yang, Zebin and Su, Yu and Zeng, Ningzhou and Nair, Vijay
License: Apache
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: Apache Software License
Description-Content-Type: text/markdown
License-File: LICENSE.md
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<div align="center">
  
**PiML: a low-code interpretable machine learning toolbox in Python** 
</div>

PiML (or π·ML, /ˈpaɪ·ˈem·ˈel/) is a new Python toolbox for Interpretable Machine Learning model development and validation. Through low-code automation and high-code programming, PiML supports various machine learning models in the following two categories:

- **Inherently interpretable models**: 
1. EBM: Explainable Boosting Machine (Nori, et al. 2019; Lou, et al. 2013)
2. GAMI-Net: Generalized Additive Model with Structured Interactions (Yang, Zhang and Sudjianto, 2021)
3. ReLU-DNN: Deep ReLU Networks using Aletheia Unwrapper (Sudjianto, et al. 2020)

- **Arbitrary black-box models**，e.g.
1. LightGBM or XGBoost of varying depth
2. RandomForest of varying depth
3. Residual Deep Neural Networks

## Low-code Examples<a name="Example"></a>   
Click the ipynb links to run examples in Google Colab:  
1. BikeSharing data: <a style="text-laign: 'center'" target="_blank" href="https://colab.research.google.com/github/SelfExplainML/PiML-Toolbox/blob/main/examples/Example_BikeSharing.ipynb">ipynb</a>  
2. CaliforniaHousing data: <a style="text-laign: 'center'" target="_blank" href="https://colab.research.google.com/github/SelfExplainML/PiML-Toolbox/blob/main/examples/Example_CaliforniaHousing.ipynb">ipynb</a>  
3. TaiwanCredit data: <a style="text-laign: 'center'" target="_blank" href="https://colab.research.google.com/github/SelfExplainML/PiML-Toolbox/blob/main/examples/Example_TaiwanCredit.ipynb">ipynb</a>   

Begin your own PiML journey with <a style="text-laign: 'center'" target="_blank" href="https://colab.research.google.com/github/SelfExplainML/PiML-Toolbox/blob/main/PiML%20Low-code%20Example%20Run.ipynb">this demo notebook</a>. 
