A low-code interpretable machine learning toolbox in Python

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

Run Low-code in Google Colab

Installation

Run the following piece of script to download and install PiML v0.1.0 to Google Colab:

!pip install wget
import wget
url = "https://github.com/SelfExplainML/PiML-Toolbox/releases/download/V0.1.0/PiML-0.1.0-cp37-cp37m-linux_x86_64.whl"
wget.download(url, 'PiML-0.1.0-cp37-cp37m-linux_x86_64.whl')
!pip install PiML-0.1.0-cp37-cp37m-linux_x86_64.whl

Low-code Usage on Google Colab

Stage 1: Initialize an experiment, Load and Prepare data

from piml import Experiment
exp = Experiment(platform="colab")

exp.data_loader()

exp.data_summary()

exp.data_prepare()

exp.eda()

Stage 2: Train intepretable models

exp.model_train()

Stage 3. Explain and Interpret

exp.model_explain()

exp.model_interpret() 

Stage 4. Diagnose and Compare

exp.model_diagnose()

exp.model_compare()

Arbitrary Black-Box Modeling

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()

Citations

"PiML: A Python Toolbox for Interpretable Machine Learning Model Development and Validation" (Sudjianto, Zhang*, Yang, Su, Zeng and 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}
}