NoLiES: The non-linear embeddings surveyor

This repository contains a Jupyter Notebook implementation of the method presented in "Attribute-based Explanation of Non-Linear Embeddings of High-Dimensional Data" by Jan-Tobias Sohns, Michaela Schmitt, Fabian Jirasek, Hans Hasse, and Heike Leitte, submitted to and presented at IEEE VIS 2021.

Requirements

• pandas
• bokeh
• pyviz::panel
• scikit-learn
• conda-forge::umap-learn
• shapely

Interactive Demos

Name	Link	Description	Size (data points x attributes)	Embedding
Iris		Iris dataset	150 x 5	MDS
Wine		UCI wine origin dataset	178 x 14	MDS
OECD Better Life		OECD better life dataset	41 x 11	MDS
Penguins		UMAP projection of the penguin dataset.	344 x 7	UMAP
Covertype		Covertype dataset with 3.5k data points. Updates take a few seconds.	3500 x 11	UMAP
Chemistry		Chemistry dataset of learned features describing activity coefficient compared to chemical class	240 x 4	MDS

Working with your own data

Download the repository and create an environment with the dependencies:

git clone https://github.com/Jan-To/nolies
conda env create -f nolies.yml
conda activate nolies

Make a copy of the template jupyter notebook:

cp template.ipynb my_data.ipynb

Update the notebook to load your data. Open the notebook with jupyter lab or jupyter notebook and edit the section Load data. Important parameters are grouped in the section Parameters and Preprocessing:

jupyter lab

Start the interactive web app:

panel serve --show my_data.ipynb

Citation

If you find this useful, please cite our paper:

@article{
title = {Attribute-based Explanation of Non-Linear Embeddings of High-Dimensional Data},
authors = {J.-T. Sohns and M. Schmitt and F. Jirasek and H. Hasse and H. Leitte},
journal = {IEEE Transactions on Visualization & Computer Graphics},
year = {2022},
volume = {28},
number = {01},
pages = {540-550},
doi = {10.1109/TVCG.2021.3114870},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = {jan}
}

Acknowledgements

This work was inspired and supported by:

• IRTG 2057
• NFDI DataPlant
• Dagstuhl workshop