Visualization Constraints and Weight Learning
Clone or download
Latest commit 7f082b8 Jul 17, 2018

README.md

Draco: Visualization Constraints Weight Learning for Visualization Recommendations Build Status Coverage Status

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-editor. The code for the editor is at https://github.com/uwdata/draco-editor.

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.
  • 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

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

Guides