API design for machine learning software: experiences from the scikit-learn project

scikit-learn is an increasingly popular machine learning library. 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...

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Bibliographic Details
Main Authors Buitinck, Lars, Louppe, Gilles, Blondel, Mathieu
Format Web Resource
LanguageEnglish
Published 23.09.2013
Subjects
api design
Computer science
data mining
Engineering, computing & technology
Ingénierie, informatique & technologie
machine learning
object-oriented programming
programming library
Sciences informatiques
scientific software
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Summary:scikit-learn is an increasingly popular machine learning library. 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.