选择器:用于各种子集选择的一般Python库
Fanwang Meng1,2, Marco Martínez González2, Valerii Chuiko2
1Department of Chemistry, Queen's University, 90 Bader Lane, Kingston, Ontario, Canada, K7L 3N6.
bioRxiv : the preprint server for biology
|December 3, 2025
概括
选择器是一个免费的,开源的Python库,用于多样化的子集选择. 它提供各种采样算法,并与Scikit-Learn集成,用于数据分析和科学发现的广泛应用.
科学领域:
- 数据科学数据科学数据科学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 从大型数据集中选择多样化的子集对于高效的分析和发现至关重要.
- 现有的工具可能缺乏灵活性或全面的算法,用于多样化的子集选择.
研究的目的:
- 介绍Selector,一个免费的,开源的Python库,用于选择不同的子集.
- 提供具有多种采样算法和多样性指标的多功能工具.
- 为了促进与现有的数据分析工作流程的整合.
主要方法:
- 基于距离,相似性和空间分区的子集采样算法的实施.
- 使用实施的指标量化子集多样性的量化.
- 与Scikit-Learn集成,并开发一个可访问的Web界面.
主要成果:
- 选择器提供了一个灵活和可扩展的包,用于多样化的子集选择.
- 该库支持各种应用,包括计算化学和药物发现.
- 易于使用的教程和无代码的网页界面提高了可访问性.
结论:
- 选择器提供了一个强大的和可访问的解决方案,用于多样化的子集选择.
- 它的设计通过现代软件实践促进互操作性和可维护性.
- 该库使不同技能水平的用户能够利用先进的子集选择技术.
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