Institute for Translational Psychiatry, University of Münster, Münster, Germany.
Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany.
PLoS One. 2021 Jul 21;16(7):e0254062. doi: 10.1371/journal.pone.0254062. eCollection 2021.
PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support the iterative model development process and automates the repetitive training, hyperparameter optimization and evaluation tasks. Importantly, the workflow ensures unbiased performance estimates while still allowing the user to fully customize the machine learning analysis. PHOTONAI extends existing solutions with a novel pipeline implementation supporting more complex data streams, feature combinations, and algorithm selection. Metrics and results can be conveniently visualized using the PHOTONAI Explorer and predictive models are shareable in a standardized format for further external validation or application. A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. Source code is publicly available on Github, while examples and documentation can be found at www.photon-ai.com.
PHOTONAI 是一个高级 Python API，旨在简化和加速机器学习模型开发。它作为一个统一的框架，允许用户轻松访问和组合来自不同工具箱的算法，形成自定义算法序列。它特别设计用于支持迭代模型开发过程，并自动化重复的训练、超参数优化和评估任务。重要的是，该工作流程在允许用户完全自定义机器学习分析的同时，确保了无偏的性能估计。PHOTONAI 通过一种新颖的管道实现扩展了现有解决方案，支持更复杂的数据流、特征组合和算法选择。使用 PHOTONAI Explorer 可以方便地可视化指标和结果，并且可以以标准化格式共享预测模型，以便进行进一步的外部验证或应用。不断增长的附加组件生态系统允许研究人员向社区提供特定于数据模态的算法，并增强生命科学领域的机器学习。它在一个典型的医学机器学习问题上展示了其实用性，仅用几行代码就实现了最先进的解决方案。源代码在 Github 上公开，示例和文档可在 www.photon-ai.com 上找到。
