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

Lars Buitinck 1 Gilles Louppe 2 Mathieu Blondel 3 Fabian Pedregosa 4 Andreas Mueller 5 Olivier Grisel 6 Vlad Niculae 7 Peter Prettenhofer 8 Alexandre Gramfort 4, 9 Jaques Grobler 4 Robert Layton 10 Jake Vanderplas 11 Arnaud Joly 2 Brian Holt 12 Gaël Varoquaux 4
1 ILPS - Information and Language Processing Systems
2 Systems and Modeling Research Unit
3 Kobe University
4 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France
5 Autonomous Intelligent Systems Group
6 Chercheur indépendant
7 Computational Linguistics
8 Ciuvo GMBH
9 LTCI - Laboratoire Traitement et Communication de l'Information
10 Science Information Technology and Engineering
11 University of Washington [Seattle]
12 Samsung Electronics Research Institute
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.
Keywords : Machine learning language design software engineering Python
Type de document :
Communication dans un congrès
European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases, Sep 2013, Prague, Czech Republic. 2013
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Contributeur : Gaël Varoquaux <>
Soumis le : dimanche 1 septembre 2013 - 17:31:15
Dernière modification le : lundi 9 avril 2018 - 12:20:04
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Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, et al.. API design for machine learning software: experiences from the scikit-learn project. European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases, Sep 2013, Prague, Czech Republic. 2013. 〈hal-00856511〉

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