PHOTONAI -- A Python API for Rapid Machine Learning Model Development
Institute for Translational Psychiatry, University of Münster（明斯特大学转化精神病学研究所）；Faculty of Mathematics and Computer Science, University of Münster（明斯特大学数学与计算机科学学院）
Ramona Leenings, Nils Ralf Winter, Lucas Plagwitz, Vincent Holstein, Jan Ernsting, Jakob Steenweg, Julian Gebker, Kelvin Sarink, Daniel Emden, Dominik Grotegerd… 展开作者
Ramona Leenings, Nils Ralf Winter, Lucas Plagwitz, Vincent Holstein, Jan Ernsting, Jakob Steenweg, Julian Gebker, Kelvin Sarink, Daniel Emden, Dominik Grotegerd, Nils Opel, Benjamin Risse, Xiaoyi Jiang, Udo Dannlowski, Tim Hahn
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.
OpenML-Python: an extensible Python API for OpenML
University of Freiburg（弗莱堡大学）；Leiden University（莱顿大学）；Eindhoven University of Technology（埃因霍温理工大学）；Microsoft（微软）；Bosch Center for Artificial Intelligence（博世人工智能中心）
Manufacture Française des pneumatiques Michelin（法国米其林轮胎制造公司）；Centre Borelli（博雷利中心）；Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；ENS Paris-Saclay（巴黎萨克雷高等师范学院）
API design for machine learning software: experiences from the scikit-learn project
University of Amsterdam（阿姆斯特丹大学）；University of Liège（列日大学）；Kobe University（神户大学）；INRIA Saclay（法国国家信息与自动化研究所萨克莱分所）；University of Bonn（波恩大学）；University of Bucharest（布加勒斯特大学）；Ciuvo GmbH（Ciuvo有限公司）；Institut Mines-Telecom（矿业电信研究所）；Telecom ParisTech（巴黎高等电信学院）；CNRS LTCI（法国国家科学研究中心信息处理与通信实验室）；University of Ballarat（巴拉瑞特大学）；University of Washington（华盛顿大学）；Samsung Electronics Research Institute（三星电子研究院）
