---

Alibi is a Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models.

• Documentation

Interpreting medical image data with Alibi: Using Counterfactual RL in Kaggle Diabetic Retinopathy Dataset.

Revised the code at the link below from Tensorflow version to Pytorch version (+ additional revision w/r/t MNISTEncoder, MNISTDecoder so that AE applies to RGB image)

• Original Code Link

Run "xai.ipynb" file for model training.

Citations

BibTeX entry:

@article{JMLR:v22:21-0017,
  author  = {Janis Klaise and Arnaud Van Looveren and Giovanni Vacanti and Alexandru Coca},
  title   = {Alibi Explain: Algorithms for Explaining Machine Learning Models},
  journal = {Journal of Machine Learning Research},
  year    = {2021},
  volume  = {22},
  number  = {181},
  pages   = {1-7},
  url     = {http://jmlr.org/papers/v22/21-0017.html}
}