Draco: Visualization Constraints Weight Learning for Visualization Recommendations 
Draco is a formal framework for representing design knowledge about effective visualization design as a collection of constraints. You can use draco to find effective visualization designs in Vega-Lite. Draco's constraints are implemented in based on Answer Set Programming (ASP) and solved with the clingo constraint solver. We also implemented a way to learn weights for the recommendation system directly from the results of graphical perception experiment.
Try Draco in the browser at https://uwdata.github.io/draco. Note that we use Emscripten to compile the constraint solver for the web.
Status
There Be Dragons! This project is in active development and we are working hard on cleaning up the repository and making it easier to use the recommendation model in Draco. If you want to use this right now, please talk to us. More documentation is forthcoming
Overview
This repository currently contains:
- The ASP programs with soft and hard constraints.
- A Python API that
- translates from Compassql and Vega-Lite to ASP
- translates the output from the clingo ASP solver to Vega-Lite
- Runs a learning to rank method on results of perception experiments
- UI tools to create annotated datasets of pairs of visualizations, look at the recommendations, and to explore large datasets of example visualizations.
- An online editor to use the Draco model in the browser. For this, we compiled clingo to WebAssembly.
- Notebooks to analyze the results
Installation
Install clingo.
You can install clingo with conda: conda install -c potassco clingo. On MacOS, you can alternatively run brew install clingo.
Install node dependencies
yarn or npm install
You might need to activate a Python 2.7 environment to compile the canvas module.
Python setup
pip install -r requirements.txt or conda install --file requirements.txt
Install draco in editable mode
pip install -e .
Now you can call the command line tool draco. For example draco --version or draco --help.
To run the notebook in a conda environment
conda install nb_conda_kernels nb_conda
Tests
You should also be able to run the tests (and coverage report)
python setup.py test
Run only ansunit tests
ansunit asp/tests.yaml
Run only python tests
pytest -v
Test types
mypy draco tests --ignore-missing-imports
Running Draco
Run the Editor
See https://github.com/uwdata/draco/blob/master/draco-editor/Readme.md
End to end example
To run draco on a partial spec.
sh run_pipeline.sh spec
The output would be a .vl.json file (for Vega-Lite spec) and a .png file to preview the visualization (by default, outputs would be in folder __tmp__).
Use CompassQL to generate examples
Run yarn build_cql_examples.
Run draco directly on a set of ASP constraints
You can use the helper file asp/_all.lp.
clingo asp/_all.lp test.lp
Alternatively, you can invoke draco with draco -m asp test.lp.
Run APT example
clingo asp/_apt.lp examples/example_apt.lp --opt-mode=optN --quiet=1 --project -c max_extra_encs=0
This only prints the relevant data and restricts the extra encodings that are being generated.
Resources
Related Repositories
Previous prototypes
Related software