Skip to content

Code repository for 'Type-Directed Synthesis of Visualizations from Natural Language Queries'

License

Notifications You must be signed in to change notification settings

utopia-group/graphy

Repository files navigation

Graphy

Code artifact for Graphy

This repository contains a snapshot of the source code for Graphy, a data visualization synthesis tool from natural language.

Installation

Requirement: Python3 (version 3.6), we suggest using the conda environment.

  1. first install pip: conda install pip
  2. run requirements pip install -r requirements.txt
  3. To run the NL4DV baseline, please install the stanford nlp packages including the files stanford-english-corenlp-2018-10-05 -models.jar, stanford-parser.jar
  4. To run draco-related baseline, please install clingo-cffi using the command python3 -m pip install --user --upgrade --extra-index-url https://test.pypi.org/simple/ clingo-cffi.
  5. To run preprocess.py, spacy english library is required using python -m spacy download en_core_web_trf (you need to first install spacy)
  6. To run the neural_parser, trained models are required. We provided all the models necessary to reproduce the evaluation here: https://drive.google.com/file/d/1UkbPoS-cueZ4J5MFMwAOaRje3woVNus_/view?usp=sharing. After downloading the zip file, just directly unzip the file and it should work.

If there are more installation necessary to run this package, please update README and requirements.txt.

Running evaluation

Command to run the evaluation script is:

python run_eval.py --eval_dataset $eval_dataset --top_k $k where $eval_dataset can be cars, movies or superstore. Setting the $k will get you top-k synthesized results.

The above command should be good to run the Graphy mode. To run the baselines, enable the following additional flags:

  • nl4dv (Sec 8.1): --nl4dv
  • draco-nl (Sec 8.1): --enum
  • base-only (Sec 8.2): --no_qualifier
  • prov-only (Sec 8.2): --no_table
  • table-only (Sec 8.2): --no_prov

Please contact the author or submit an issue if interested in the author's implementation of NcNet (Sec 8.1) and Bart-Vis (Sec 8.1).

Running user query

To utilize graphy to run additional queries on the supporting dataset, please look at run_example.py for reference.

Disclaimer

This is a prototype tool and would recommend caution when building on top of it :)

About

Code repository for 'Type-Directed Synthesis of Visualizations from Natural Language Queries'

Resources

License

Stars

Watchers

Forks