An integrated Python toolbox for interpretable machine learning
pip install PiML
📢 V0.2.0 is released with high-code APIs.
PiML (or π-ML, /ˈpaɪ·ˈem·ˈel/) is a new Python toolbox for interpretable machine learning model development and validation. Through low-code interface and high-code APIs, PiML supports various machine learning models including
- Inherently interpretable models:
- EBM: Explainable Boosting Machine (Nori, et al. 2019; Lou, et al. 2013)
- GAMI-Net: Generalized Additive Model with Structured Interactions (Yang, Zhang and Sudjianto, 2021)
- ReLU-DNN: Deep ReLU Networks using Aletheia Unwrapper and Sparsification (Sudjianto, et al. 2020)
- Arbitrary machine learning models，e.g.
- Tree-ensembles: RF, GBM, XGBoost, LightGBM, ...
- DNNs: MLP, ResNet, CNN, Attention, ...
- Kernel methods: SVM, Gaussian Process, ...
Installation | Examples | Usage | Citations
pip install PiML Click the ipynb links to run examples in Google Colab:
- BikeSharing data:
ipynb - CaliforniaHousing data: ipynb
- TaiwanCredit data: ipynb
- Upload custom data in two ways: ipynb
Begin your own PiML journey with this demo notebook.
The same examples can also be run by high-code APIs:
from piml import Experiment
exp = Experiment()exp.data_loader()
exp.data_summary()
exp.data_prepare()
exp.eda()
exp.model_train()
exp.model_explain()
exp.model_interpret() 
exp.model_diagnose()
exp.model_compare()
For example, train a complex LightGBM with depth 7 and register it to the experiment:
from lightgbm import LGBMRegressor
pipeline = exp.make_pipeline(LGBMRegressor(max_depth=7))
pipeline.fit()
exp.register(pipeline=pipeline, name='LGBM')Then, compare it to inherently interpretable models (e.g. EBM and GAMI-Net):
exp.model_compare()
PiML, ReLU-DNN Aletheia and GAMI-Net
"PiML: A Python Toolbox for Interpretable Machine Learning Model Development and Validation" (A. Sudjianto, A. Zhang, Z. Yang, Y. Su, N. Zeng and V. Nair, 2022)
@article{sudjianto2022piml,
title={PiML: A Python Toolbox for Interpretable Machine Learning Model Development and Validation},
author={Sudjianto, Agus and Zhang, Aijun and Yang, Zebin and Su, Yu and Zeng, Ningzhou and Nair Vijay},
journal={To appear},
year={2022}
}"Designing Inherently Interpretable Machine Learning Models" (A. Sudjianto and A. Zhang, 2021) arXiv link
@article{sudjianto2021designing,
title={Designing Inherently Interpretable Machine Learning Models},
author={Sudjianto, Agus and Zhang, Aijun},
journal={arXiv preprint:2111.01743},
year={2021}
}"Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification" (A. Sudjianto, W. Knauth, R. Singh, Z. Yang and A. Zhang, 2020) arXiv link
@article{sudjianto2020unwrapping,
title={Unwrapping the black box of deep ReLU networks: interpretability, diagnostics, and simplification},
author={Sudjianto, Agus and Knauth, William and Singh, Rahul and Yang, Zebin and Zhang, Aijun},
journal={arXiv preprint:2011.04041},
year={2020}
}"GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions" (Z. Yang, A. Zhang, and A. Sudjianto, 2021) arXiv link
@article{yang2021gami,
title={GAMI-Net: An explainable neural network based on generalized additive models with structured interactions},
author={Yang, Zebin and Zhang, Aijun and Sudjianto, Agus},
journal={Pattern Recognition},
volume={120},
pages={108192},
year={2021}
}EBM and GA2M
"InterpretML: A Unified Framework for Machine Learning Interpretability" (H. Nori, S. Jenkins, P. Koch, and R. Caruana, 2019)
@article{nori2019interpretml,
title={InterpretML: A Unified Framework for Machine Learning Interpretability},
author={Nori, Harsha and Jenkins, Samuel and Koch, Paul and Caruana, Rich},
journal={arXiv preprint:1909.09223},
year={2019}
}"Accurate intelligible models with pairwise interactions" (Y. Lou, R. Caruana, J. Gehrke, and G. Hooker, 2013)
@inproceedings{lou2013accurate,
title={Accurate intelligible models with pairwise interactions},
author={Lou, Yin and Caruana, Rich and Gehrke, Johannes and Hooker, Giles},
booktitle={Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages={623--631},
year={2013},
organization={ACM}
} 
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