让复杂解释更简洁：自动精简反事实说明。
xai-cola: A Python library for sparsifying counterfactual explanations
- 通过可插拔策略自动减少反事实解释中的冗余特征变动。
- 实验显示特征修改数量最多降低50%。
- 适合需要清晰、简洁解释的机器学习应用者。
反事实解释（CE）是事后可解释性的重要领域，但大多数生成器产生的解释存在高度冗余。本文介绍开源 Python 库 xai-cola，提供端到端流程，对任意生成器输出的反事实解释进行稀疏化处理，在保持解释有效性的同时减少不必要的特征变化。该库支持输入原始表格数据（pandas DataFrame）、预处理对象（用于标准化和编码），以及训练好的 scikit-learn 或 PyTorch 模型。用户可使用内置或外部导入的 CE 生成器。库中实现多种稀疏化策略，并包含可视化工具以分析和比较稀疏化后的反事实结果。xai-cola 采用 MIT 许可证，可通过 PyPI 安装。实验证明，该工具在多个 CE 生成器上均能生成更稀疏的解释，本设置下特征修改数最多减少50%。源码位于 https://github.com/understanding-ml/COLA。
原文摘要 · Abstract (English)
Counterfactual explanation (CE) is an important domain within post-hoc explainability. However, the explanations generated by most CE generators are often highly redundant. This work introduces an open-source Python library xai-cola, which provides an end-to-end pipeline for sparsifying CEs produced by arbitrary generators, reducing superfluous feature changes while preserving their validity. It offers a documented API that takes as input raw tabular data in pandas DataFrame form, a preprocessing object (for standardization and encoding), and a trained scikit-learn or PyTorch model. On this basis, users can either employ the built-in or externally imported CE generators. The library also implements several sparsification policies and includes visualization routines for analysing and comparing sparsified counterfactuals. xai-cola is released under the MIT license and can be installed from PyPI. Empirical experiments indicate that xai-cola produces sparser counterfactuals across several CE generators, reducing the number of modified features by up to 50% in our setting. The source code is available at https://github.com/understanding-ml/COLA.
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