API design for machine learning software: experiences from the scikit-learn project
Lars Buitinck(University of Amsterdam), Gilles Louppe(University of Liège), Mathieu Blondel(Kobe University), Fabián Pedregosa(Commissariat à l'Énergie Atomique et aux Énergies Alternatives), Andreas Mueller(University of Bonn), Olivier Grisel, Vlad Niculae(Romanian Academy), Peter Prettenhofer, Alexandre Gramfort(Commissariat à l'Énergie Atomique et aux Énergies Alternatives), Jaques Grobler(Commissariat à l'Énergie Atomique et aux Énergies Alternatives), Robert Layton, Jake Vanderplas(University of Washington), Arnaud Joly(University of Liège), Brian Holt(Samsung (United Kingdom)), Varoquaux, Ga\"el(Commissariat à l'Énergie Atomique et aux Énergies Alternatives)
arXiv (Cornell University)
September 1, 2013
Cited by 1,801Open Access
Abstract
Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library.
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