This repository contains a snapshot of the source code for Graphy, a data visualization synthesis tool from natural language.
Requirement: Python3 (version 3.6), we suggest using the conda environment.
- first install pip:
conda install pip - run requirements
pip install -r requirements.txt - 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 - 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. - To run
preprocess.py, spacy english library is required usingpython -m spacy download en_core_web_trf(you need to first install spacy) - 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.
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).
To utilize graphy to run additional queries on the supporting dataset, please look at run_example.py for reference.
This is a prototype tool and would recommend caution when building on top of it :)