用可调函数优化多目标问题，支持超参调优与探索-利用平衡。
Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions
- 基于可调函数构建多目标优化框架，适配多种优化场景。
- 提出扩展的Morris-Mitchell准则，提升搜索空间覆盖度。
- 提供可视化工具，辅助分析新采样点分布情况。
可调函数方法是优化多响应过程的常用手段。Kuhn（2016）在R语言中实现了desirability和desirability2包，但目前缺少对应的Python工具。本文介绍Python包spotdesirability，作为顺序参数优化框架的一部分，用于实现可调函数方法。首先简要介绍该方法，随后通过三个实例展示其在（i）经典优化、（ii）基于代理模型的优化、（iii）超参数调优中的应用。此外，提出一种扩展的Morris-Mitchell准则，用于计算搜索空间覆盖率，并在第四个例子中用于处理优化过程中的探索-利用权衡。最后，引入infill诊断图，以可视化新采样点相对于已有数据点的空间分布。
原文摘要 · Abstract (English)
The desirability-function approach is a widely adopted method for optimizing multiple-response processes. Kuhn (2016) implemented the packages desirability and desirability2 in the statistical programming language R, but no comparable packages exists for Python. The goal of this article is to provide an introduction to the desirability function approach using the Python package spotdesirability, which is available as part of the sequential parameter optimization framework. After a brief introduction to the desirability function approach, three examples are given that demonstrate how to use the desirability functions for (i) classical optimization, (ii) surrogate-model based optimization, and (iii) hyperparameter tuning. An extended Morris-Mitchell criterion, which allows the computation of the search-space coverage, is proposed and used in a fourth example to handle the exploration-exploitation trade-off in optimization. Finally, infill-diagnostic plots are introduced as a tool to visualize the locations of the infill points with respect to already existing points.
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