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This reporsitory contains the source for the original Draco project. We develop a much improved Draco 2 athttps://github.com/cmudig/draco2.

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 introductoryblog post about Draco and ourresearch paper for more details. Try Draco in the browser athttps://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 forrunning Draco, theCLI, and thepython wrapper for thedraco-core API. Additionally includes somehelper 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

• draco-vis

  • A web-friendly Draco! Including a bundled Webassembly module of Draco's solver, Clingo.

• draco-learn

  • Runs a learning-to-rank method on results of perception experiments.

• draco-tools

  • UI tools to create annotated datasets of pairs of visualizations, look at the recommendations, and to explore large datasets of example visualizations.

• draco-analysis

  • Notebooks to analyze the results.

In addition to a wrapper of the Draco-Core API describe below, the python API contains the following functions.

objectResult<>

The result of a Draco run, a solution to a draco_query. Userresult.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 adraco_query, defined as a list of Draco ASP facts (strings), against givenfile asp programs (defaults to base Draco set). Returns aResult if the query is satisfiable. Ifrelax_hard is 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 adraco_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 ofdata and returns the ASP declaration of it (a list of facts).

read_data_to_asp(file: str) -> List[str]:<>

Reads afile of data (either.json or.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.data should 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 runbrew install clingo.

pip install draco

STOP! If you wish torun Draco in aweb browser, consider usingdraco-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 ornpm install draco-core

You can install Clingo with conda:conda install -c potassco clingo. On MacOS, you can alternatively runbrew install clingo.

yarn ornpm 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 orconda install --file requirements.txt

Install Draco in editable mode. We expect Python 3.

pip install -e .

Now you can call the command line tooldraco. For exampledraco --version ordraco --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__).

Runyarn build_cql_examples.

You can use the helper fileasp/_all.lp.

clingo asp/_all.lp test.lp

Alternatively, you can invoke Draco withdraco -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__.py and use the right version below.
• git commit -m "bump version to 0.0.1"
• Tag the last commitgit tag -a v0.0.1.
• git push andgit push --tags
• Runpython setup.py sdist upload.

Previous prototypes

• https://github.com/domoritz/vis-csp
• https://github.com/domoritz/vis-constraints

For a bit of historical perspective, after buildingCompass andCompassQL with @kanitw, @domoritz worked with colleagues at UW on a first constraint-based prototypevis-csp in ASP, then tried Z3 invis-constraints before going back to ASP for Draco.

Related software

• https://github.com/uwdata/draco-vis
• https://github.com/vega/compassql
• https://github.com/potassco/clingo

• https://github.com/potassco/guide/releases/