TALE is a Tool for Annotation of Low-dimensional Embeddings. It offers functionality to assess, interprete and rate low-dimensional projections, such as those generated by e.g. t-SNE or UMAP. See todo - add paper link for a more complete description. It is written in Python (backend) and Javascript (frontend). This repository contains the dataset with projection features and user ratings discussed in the paper.
Head over to https://github.com/rmitsch/TALE-backend and https://github.com/rmitsch/TALE-frontend for the actual source code.
TALE allows to explore the parameter space of low-dimensional projections in the global view:
Individual projections can be inspected, evaluated and rated in the local view:
- Pull source code:
git clone --recurse-submodules git@github.com:rmitsch/TALE.git - Build the Docker image:
docker build -t tale -f Dockerfile . - Alternatively pull the image from Dockerhub:
docker pull rmitsch/tale
docker run -v [host data directory]:/data tale python /TALE-backend/source/generate_data.py [dataset name] [DR kernel name] /data
[dataset name] can be either "happiness" for the UN world happiness study or "movie" for the IMDB movie dataset.
[DR kernel name] can be "UMAP", "TSNE" or "SVD".
docker run -p 2484:2484 -v [host data directory]:/data tale python /TALE-backend/source/app.py /TALE-frontend /data [experiment name] [Dropbox OAuth Token]
[experiment name] and [Dropbox OAuth Token] are optional and only necessary if you want to hook up TALE to a Dropbox account to automatically store the resulting user ratings in the cloud.
Access in your browser via localhost:2484.
Note: By default, TALE attempts to load t-SNE projections for the world happiness dataset, i. e. assumes that projections have been generated with
docker run -v [host data directory]:/data tale python /TALE-backend/source/generate_data.py happiness TSNE /data. If you want to look at another configuration, select it in the dataset and DR kernel dropdowns to the top right and click the load button to their right.