lrp-pf-auc 0.1.6
Explain Neural Networks using Layer-Wise Relevance Propagation and evaluate the explanations using Pixel-Flipping and Area Under the Curve.
License: MIT
Requires Python: >=3.9

# Layer-Wise Relevance Propagation

[![DOI](https://zenodo.org/badge/432443018.svg)](https://zenodo.org/badge/latestdoi/432443018)
[![PyPI Version](https://img.shields.io/pypi/v/lrp-pf-auc)](https://pypi.org/project/lrp-pf-auc/)
[![License](https://img.shields.io/badge/license-MIT-blue)](license)

PyTorch implementation of Layer-wise Relevance Propagation (LRP) algorithm together with quantitative evaluation metrics to compare heatmap explanations objectively.

Special thanks to Dr. Grégoire Montavon for his insights, which shaped the development of this project.

## Features

Explainability:

- Layer-wise Relevance Propagation (LRP)

Quantitative Evaluation:

- Pixel Flipping (PF), also known as Region Perturbation (RP)
  - Perturbation modes:
    - inpainting
    - random
  - Sort objectives:
    - most relevant first (MoRF), also known as activation curve
    - least relevant first (LRF), also known as pruning curve
    - random
- AUC (Area Under the Curve), also known as AUAC (Area Under the Activation Curve) or AU-MSE (Area Under the Mean Squared Error Curve) depending on the sort objective

### Showcase

**LRP** ([lrp-tutorial](https://git.tu-berlin.de/gmontavon/lrp-tutorial) composite) and **PF** with **inpainting** perturbation mode and sort objective **MoRF**.

<p align="center" width="100%">
    <img width="65%" src="https://github.com/rodrigobdz/lrp/blob/main/docs/images/castle-lrp-pf-auc-inpainting.png?raw=true">
</p>

Classification scores of castle image with **inpainting** perturbation mode

<p align="center" width="100%">
    <img width="65%" src="https://github.com/rodrigobdz/lrp/blob/main/docs/images/castle-pf-auc-inpainting.png?raw=true">
</p>

Number of simultaneous flips per perturbation step during Pixel-Flipping.

<p align="center" width="100%">
    <img width="65%" src="https://github.com/rodrigobdz/lrp/blob/main/docs/images/castle-pf-perturbation-steps.png?raw=true">
</p>

---

<details>
<summary>PF perturbation mode random</summary>
<p>

**LRP** ([lrp-tutorial](https://git.tu-berlin.de/gmontavon/lrp-tutorial) composite) and **PF** with **random** perturbation mode and sort objective **MoRF**.

<p align="center" width="100%">
    <img width="65%" src="https://github.com/rodrigobdz/lrp/blob/main/docs/images/castle-lrp-pf-auc-random.png?raw=true">
</p>

Classification scores of castle image with random perturbation mode

<p align="center" width="100%">
    <img width="65%" src="https://github.com/rodrigobdz/lrp/blob/main/docs/images/castle-pf-auc-random.png?raw=true">
</p>

</p>
</details>

## Requirements

- `python3` >= 3.9

## Installation

```sh
python3 -m pip install lrp-pf-auc
```

The PyPI distribution `lrp-pf-auc` provides the following two packages: `lrp` and `pf`.

The name `lrp-pf-auc` stands for Layer-wise Relevance Propagation (LRP), Pixel Flipping (PF), and Area Under the Curve (AUC) respectively.

## Usage

Refer to [demo.ipynb](https://github.com/rodrigobdz/lrp/blob/main/demo.ipynb) for an example of Layer-wise Relevance Propagation (LRP), Pixel-Flipping (PF) and Area under the Curve (AUC).

Feel free to check out the Jupyter notebooks under [experiments/notebooks](https://github.com/rodrigobdz/lrp/tree/main/experiments/notebooks) for a chronological overview of the project.

## Related Projects

- Sequential LRP implementation: [gmontavon/lrp-tutorial](https://git.tu-berlin.de/gmontavon/lrp-tutorial)
  > Tutorial on how to implement LRP
- Updated version of `gmontavon/lrp-tutorial`: [rodrigobdz/lrp-tutorial](https://git.tu-berlin.de/rodrigobdz/lrp-tutorial)
- Forward-hook LRP implementation: [chr5tphr/zennit](https://github.com/chr5tphr/zennit)
  > Implementation of LRP-based methods in PyTorch
- [`innvestigate`](https://github.com/albermax/innvestigate)-based LRP implementation: [moboehle/Pytorch-LRP](https://github.com/moboehle/Pytorch-LRP)
- Caffe-based LRP implementation: [`lrp_toolbox`](https://github.com/sebastian-lapuschkin/lrp_toolbox)
- Pixel-Flipping and Region Perturbation implementation: [understandable-machine-intelligence-lab/Quantus](https://github.com/understandable-machine-intelligence-lab/Quantus)

## Citation

Cite as:

- Plaintext:

  > Rodrigo Bermúdez Schettino. (2022). rodrigobdz/lrp: v0.1.6 (v0.1.6). Zenodo. https://doi.org/10.5281/zenodo.6821295

- BibTeX:

  ```text
  @software{rodrigo_bermudez_schettino_2022_6821295,
    author       = {Rodrigo Bermúdez Schettino},
    title        = {rodrigobdz/lrp: v0.1.6},
    month        = jul,
    year         = 2022,
    publisher    = {Zenodo},
    version      = {v0.1.6},
    doi          = {10.5281/zenodo.6821295},
    url          = {https://doi.org/10.5281/zenodo.6821295}
  }
  ```

## Credits

- The structure of this readme is based on [minimal-readme](https://github.com/rodrigobdz/minimal-readme)

- Scripts follow [Personal Shell Style Guide](https://github.com/rodrigobdz/styleguide-sh)

- The `lrp` package uses [two customized files](https://github.com/rodrigobdz/lrp/tree/main/lrp/zennit) originally from [chr5tphr/zennit](https://github.com/chr5tphr/zennit).

- The syntax for defining custom composites is inspired by [this discussion](https://github.com/chr5tphr/zennit/issues/76) on `zennit`'s repo.

This implementation is based on insights from:

- LRP overview paper

  > G. Montavon, A. Binder, S. Lapuschkin, W. Samek, K.-R. Müller
  > [Layer-wise Relevance Propagation: An Overview](https://doi.org/10.1007/978-3-030-28954-6_10)
  > in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer LNCS, vol. 11700, 2019

- Original LRP paper

  > S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, W. Samek
  > [On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation](https://doi.org/10.1371/journal.pone.0130140)
  > PloS ONE 10 (7), e0130140, 2015

- [ECML/PKDD 2020 Tutorial: Explainable AI for Deep Networks: Basics and Extensions (Part 3)](http://heatmapping.org/slides/2020_ECML_3.pdf)

## License

[MIT](LICENSE) © [rodrigobdz](https://github.com/rodrigobdz/)