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 visual 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.
Read our introductory blog post about Draco and our research paper for more details. Try Draco in the browser at https://uwdata.github.io/draco-editor.
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.
This repository currently contains:
- draco (pypi) The ASP programs with soft and hard constraints, a python API for running Draco, the CLI, and the python wrapper for the draco-core API. Additionally includes some helper functions that may prove useful.
- draco-core (npm) Holds a Typescript / Javascript friendly copy of the ASP programs, and additionally, a Typescript /Javascript API for all the translation logic of Draco, as described below.
Various functionality and extensions are in the following repositories
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- A web-friendly Draco! Including a bundled Webassembly module of Draco's solver, Clingo.
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- Runs a learning-to-rank method on results of perception experiments.
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- UI tools to create annotated datasets of pairs of visualizations, look at the recommendations, and to explore large datasets of example visualizations.
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- Notebooks to analyze the results.
In addition to a wrapper of the Draco-Core API describe below, the python API contains the following functions.
object Result <>
The result of a Draco run, a solution to a draco_query. User
result.as_vl()to convert this solution into a Vega-Lite specification.
run (draco_query: List[str] [,constants, files, relax_hard, silence_warnings, debug, clear_cache]) -> Result: <>
Runs a
draco_query, defined as a list of Draco ASP facts (strings), against givenfileasp programs (defaults to base Draco set). Returns aResultif the query is satisfiable. Ifrelax_hardis set toTrue, hard constraints (hard.lp) will not be strictly enforced, and instead will incur an infinite cost when violated.
is_valid (draco_query: List[str] [,debug]) -> bool: <>
Runs a
draco_query, defined as a list of Draco ASP facts (strings), against Draco's hard constraints. Returns true if the visualization defined by the query is a valid one (does not violate hard constraints), and false otherwise. Hard constraints can be found inhard.lp.
data_to_asp (data: List) -> List[str]: <>
Reads an array of
dataand returns the ASP declaration of it (a list of facts).
read_data_to_asp (file: str) -> List[str]: <>
Reads a
fileof data (either.jsonor.csv) and returns the ASP declaration of it (a list of facts).
vl2asp (spec: TopLevelUnitSpec): string[] <>
Translates a Vega-Lite specification into a list of ASP Draco facts.
cql2asp (spec: any): string[] <>
Translates a CompassQL specification into a list of ASP Draco constraints.
asp2vl (facts: string[]): TopLevelUnitSpec <>
Interprets a list of ASP Draco facts as a Vega-Lite specification.
data2schema (data: any[]): Schema <>
Reads a list of rows and generates a data schema for the dataset.
datashould be given as a list of dictionaries.
schema2asp (schema: Schema): string[] <>
Translates a data schema into an ASP declaration of the data it describes.
constraints2json (constraintsAsp: string, weightsAsp?: string): Constraint[] <>
Translates the given ASP constraints and matching weights (i.e. for soft constraints) into JSON format.
json2constraints (constraints: Constraint[]): ConstraintAsp <>
Translates the given JSON format ASP constraints into ASP strings for definitions and weights (if applicable, i.e. for soft constraints).
You can install Clingo with conda: conda install -c potassco clingo. On MacOS, you can alternatively run brew install clingo.
pip install draco
STOP! If you wish to run Draco in a web browser, consider using draco-vis, which bundles the Clingo solver as a WebAssembly module. The Draco-Core API does not include this functionality by itself. It merely handles the logic of translating between the various interface languages.
yarn add draco-core or npm install draco-core
You can install Clingo with conda: conda install -c potassco clingo. On MacOS, you can alternatively run brew install clingo.
yarn or npm install
You might need to activate a Python 2.7 environment to compile the canvas module.
yarn build. We are currently using typescript version 3.2.1 and greater.
pip install -r requirements.txt or conda install --file requirements.txt
Install Draco in editable mode. We expect Python 3.
pip install -e .
Now you can call the command line tool draco. For example draco --version or draco --help.
You should also be able to run the tests (and coverage report)
python setup.py test
ansunit asp/tests.yaml
pytest -v
mypy draco tests --ignore-missing-imports
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__).
Run yarn build_cql_examples.
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.
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.
- Make sure everything works!
- Update
__version__indraco/__init__.pyand use the right version below. git commit -m "bump version to 0.0.1"- Tag the last commit
git tag -a v0.0.1. git pushandgit push --tags- Run
python setup.py sdist upload.
Previous prototypes
Related software