PlotCoder: Hierarchical Decoding for Synthesizing Visualization Code in Programmatic Context

By Xinyun Chen, Linyuan Gong, Alvin Cheung, Dawn Song. 2021

25 citations recorded by the source corpus.

Creating effective visualization is an important part of data analytics. While there are many libraries for creating visualization, writing such code remains difficult given the myriad of parameters that users need to provide. In this paper, we propose the new task of synthesizing visualization programs from a combination of natural language utterances and code context. To tackle the learning problem, we introduce PlotCoder, a new hierarchical encoder-decoder architecture that models both the code context and the input utterance. We use PlotCoder to first determine the template of the visualization code, followed by predicting the data to be plotted. We use Jupyter notebooks containing visualization programs crawled from GitHub to train PlotCoder. On a comprehensive set of test samples from those notebooks, we show that PlotCoder correctly predicts the plot type of about 70% samples, and synthesizes the correct programs for 35% samples, performing 3-4.5% better than the baselines.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Code Generation & Program Synthesis

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Citations in the corpus

DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation (2022, methodology): “Fortunately, these barriers could potentially be reduced by pre-trained code generation models: for example, Codex (Chen et al., 2021a) can complete small Python snippets with non-trivial accuracy and AlphaCode (Li et al., 2022) competitive programming problems.”
Execution-based Evaluation for Data Science Code Generation Models (2022, background): “…productivity, there are increasing interests in building systems to solve a variety of DS tasks, including code synthesis (Agashe et al., 2019), code synthesis for visualization (Chen et al., 2021b) and data preparation (Yan and He, 2020), documentation (Liu et al., 2021; Wang et al., 2021), etc.”
Natural Language to Code Generation in Interactive Data Science Notebooks (2022, methodology): “LLMs for Code In recent years there has been a burgeoning of LMs trained on code (Chen et al., 2021a; Austin et al., 2021; Nijkamp et al., 2022; Fried et al., 2022; Li et al., 2022; Tunstall et al., 2022; Xu et al., 2022).”