This repository provides code and data of the paper Table2Charts: Recommending Charts by Learning Shared Table Representations
The core parts included in the folder Table2Charts. See Table2Charts/README.md for details.
In the paper Table2Charts is compared with the following four baselines:
- DeepEye: From the paper DeepEye: Towards Automatic Data Visualization with inference models at https://github.com/Thanksyy/DeepEye-APIs.
- Data2Vis: From the paper Data2Vis: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks with code at https://github.com/victordibia/data2vis.
- VizML: From the paper VizML: A Machine Learning Approach to Visualization Recommendation with code and data at https://github.com/mitmedialab/vizml.
- DracoLearn: From the paper Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco with inference models at https://github.com/uwdata/draco.
In the folder Baselines, we provide more details on how we train and evaluate those baselines.
In addition to our Excel chart corpus (which is under privacy reviews for publication), we use two more datasets for comparing with baselines in section 4.2:
- A public Plotly corpus used in VizML paper.
- 500 HTML tables (crawled from the public web) for human evaluation.
In the folder Data, we provide the way we get and process Plotly corpus, and the results about human evaluation.
We provide model in section 4.2.2 and human evaluation results in section 4.2.3.
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