machine learning; data mining; api design; object-oriented programming; programming library; scientific software
Abstract :
[en] 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.
Disciplines :
Computer science
Author, co-author :
Buitinck, Lars
Louppe, Gilles ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Blondel, Mathieu
Other collaborator :
Pedregosa, Fabian
Müller, Andreas
Grisel, Olivier
Niculae, Vlad
Prettenhofer, Peter
Gramfort, Alexandre
Grobler, Jaques
Layton, Robert
Vanderplas, Jake
Joly, Arnaud ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Holt, Brian
Varoquaux, Gaël
Language :
English
Title :
API design for machine learning software: experiences from the scikit-learn project
Publication date :
23 September 2013
Number of pages :
15
Event name :
ECML/PKDD 2013 Workshop: Languages for Data Mining and Machine Learning
