PiML (read $\pi$-ML, /`pai.`em.`el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics. It is designed with machine learning workflows in both low-code and high-code modes, including data pipeline, model training, model interpretation and explanation, and model diagnostics and comparison. The toolbox supports a growing list of interpretable models (e.g. GAM, GAMI-Net, XGB2) with inherent local and/or global interpretability. It also supports model-agnostic explainability tools (e.g. PFI, PDP, LIME, SHAP) and a powerful suite of model-agnostic diagnostics (e.g. weakness, uncertainty, robustness, fairness). Integration of PiML models and tests to existing MLOps platforms for quality assurance are enabled by flexible high-code APIs. Furthermore, PiML toolbox comes with a comprehensive user guide and hands-on examples, including the applications for model development and validation in banking. The project is available at https://github.com/SelfExplainML/PiML-Toolbox.

翻译：PiML（读作$\pi$-ML，/`pai.`em.`el/）是一个集成的开源Python工具箱，专为可解释机器学习模型开发与模型诊断而设计。该工具箱支持低代码与高代码两种机器学习工作流模式，涵盖数据管道、模型训练、模型解释与说明、模型诊断与比较等模块。该工具箱支持不断扩展的可解释模型（如GAM、GAMI-Net、XGB2），这些模型具备固有的局部和/或全局可解释性。此外，它还支持模型无关的可解释性工具（如PFI、PDP、LIME、SHAP）以及一套强大的模型无关诊断工具（如弱点分析、不确定性分析、鲁棒性分析、公平性分析）。通过灵活的高代码API，可将PiML模型与测试集成到现有的MLOps平台中进行质量保证。PiML工具箱还附有全面的用户指南与实操案例，涵盖银行业模型开发与验证的应用场景。项目访问地址：https://github.com/SelfExplainML/PiML-Toolbox。