https://github.com/iamdecode/lemon-evaluation
https://github.com/iamdecode/lemon-evaluation
Last synced: 12 days ago
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- Host: GitHub
- URL: https://github.com/iamdecode/lemon-evaluation
- Owner: iamDecode
- License: bsd-2-clause
- Created: 2019-04-01T14:48:22.000Z (about 7 years ago)
- Default Branch: main
- Last Pushed: 2023-09-19T21:41:46.000Z (almost 3 years ago)
- Last Synced: 2026-01-17T11:21:19.286Z (5 months ago)
- Language: Jupyter Notebook
- Homepage: https://explaining.ml/lemon
- Size: 1.37 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
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README
🍋 LEMON evaluation
=================================
This repository hosts the code to run the evaluation of the [LEMON explainer technique](https://github.com/iamDecode/lemon).
## Setup
This assumes you have got the python virtualenv package installed (`pip install virtualenv`).
1. Create a virtual environment:
$ virtualenv venv
2. Activate the virtual environment:
$ source venv/bin/activate
Or in the fish shell:
$ source venv/bin/activate.fish
3. Install the dependencies:
$ pip install -r requirements.txt
4. Make sure jupyter uses the virtualenv:
$ python -m ipykernel install --user --name=venv
## Running
To execute the notebook, run:
$ jupyter lab
You may have to switch the kernel to the one we created in setup step 4.
## Content
- `01-samples` contains code to generate samples using arbitrary distance kernels (the basis of LEMON), and code to generate the figures used in the paper.
- `02-evaluation-synthetic` contains the full code for LEMON and for the synthetic data evaluation in the paper, and
- `03-evaluation-uci` the code for the UCI dataset evaluation in the paper (this may take a while to run).
## Citation
If you want to refer to our explanation technique, please cite our paper using the following BibTeX entry:
```bibtex
@inproceedings{collaris2023lemon,
title={{LEMON}: Alternative Sampling for More Faithful Explanation Through Local Surrogate Models},
author={Collaris, Dennis and Gajane, Pratik and Jorritsma, Joost and van Wijk, Jarke J and Pechenizkiy, Mykola},
booktitle={Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis (IDA 2023)},
pages={77--90},
year={2023},
organization={Springer}
}
```