AI automation tools need machine-readable hyperparameter schemas to define their search spaces. At the same time, AI libraries often come with good human-readable documentation. While such documentation contains most of the necessary information, it is unfortunately not ready to consume by tools. This paper describes how to automatically mine Python docstrings in AI libraries to extract JSON Schemas for their hyperparameters. We evaluate our approach on 119 transformers and estimators from three different libraries and find that it is effective at extracting machine-readable schemas. Our vision is to reduce the burden to manually create and maintain such schemas for AI automation tools and broaden the reach of automation to larger libraries and richer schemas.

中文翻译：

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挖掘文档以提取超参数模式

AI 自动化工具需要机器可读的超参数模式来定义其搜索空间。同时，AI 库通常带有良好的人类可读文档。虽然此类文档包含大部分必要信息，但不幸的是，它尚未准备好被工具使用。本文介绍了如何自动挖掘 AI 库中的 Python 文档字符串，以为其超参数提取 JSON 模式。我们在来自三个不同库的 119 个转换器和估计器上评估我们的方法，发现它在提取机器可读模式方面是有效的。我们的愿景是减轻为 AI 自动化工具手动创建和维护此类模式的负担，并将自动化的范围扩大到更大的库和更丰富的模式。