API design for machine learning software: experiences from the scikit-learn project - Inria - Institut national de recherche en sciences et technologies du numérique
[go: up one dir, main page]
Communication Dans Un Congrès Année : 2013
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 (ISLA, University of Amsterdam, Science Park 904, 1098 XH Amsterdam - Pays-Bas)
"> ILPS - Information and Language Processing Systems
2 Systems and Modeling Research Unit (Institute Montefiore (B28, P32) Grande Traverse, 10 Sart-Tilman B-4000 Liège, Belgium. - Belgique)
"> Systems and Modeling Research Unit
3 Kobe University (Japon)
"> Kobe University
4 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data (Neurospin, CEA Saclay, Bâtiment 145, 91191 Gif-sur-Yvette Cedex - France)
"> PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
5 Autonomous Intelligent Systems Group (Rheinische Friedrich-Wilhelms-Universität Bonn Institut für Informatik VI Friedrich-Ebert-Allee 144 53113 Bonn - Allemagne) "> Autonomous Intelligent Systems Group
6 Chercheur indépendant (France) "> Chercheur indépendant
7 Computational Linguistics (Roumanie) "> Computational Linguistics
8 Ciuvo GMBH (Autriche) "> Ciuvo GMBH
9 LTCI - Laboratoire Traitement et Communication de l'Information (46 rue Barrault F-75634 Paris Cedex 13 - France) "> LTCI - Laboratoire Traitement et Communication de l'Information
10 Science Information Technology and Engineering (Suite 9, Greenhill Enterprise Centre Ballarat 3350 - Australie) "> Science Information Technology and Engineering
11 University of Washington [Seattle] (Seattle, Washington 98105 - États-Unis) "> University of Washington [Seattle]
12 Samsung Electronics Research Institute (Communications House, South Street Staines, Middlesex TW18 4QE - Royaume-Uni) "> Samsung Electronics Research Institute

Résumé

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.

Domaines

Apprentissage [cs.LG]
Fichier principal
Vignette du fichier
paper.pdf (162.8 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Gaël Varoquaux  : Connectez-vous pour contacter le contributeur

https://inria.hal.science/hal-00856511

Soumis le : dimanche 1 septembre 2013-17:31:15

Dernière modification le : lundi 4 novembre 2024-16:36:05

Archivage à long terme le : lundi 2 décembre 2013-08:55:29

Télécharger pour visualiser

Dates et versions

hal-00856511 , version 1 (01-09-2013)
Identifiants

Citer

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. ⟨hal-00856511⟩

Exporter

BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite

Collections

CEA CNRS INRIA PARISTECH INRIA2 JOLIOT CEA-DRF NEUROSPIN LTCI INSTITUT-MINES-TELECOM
2379 Consultations
3159 Téléchargements

Altmetric

Partager

More