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Conference papers
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
4
PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
Lars Buitinck 1 Site :
Author
Gilles Louppe 2
Author
Mathieu Blondel 3
Author
Fabian Pedregosa 4
Mail :
Author
Alexandre Gramfort 4, 9
Mail :
Author IdHAL : agramfort
Jaques Grobler 4
Author
Robert Layton 10
Author
Jake Vanderplas 11
Author
Arnaud Joly 2
Author
Brian Holt 12
Author
Gaël Varoquaux 4
Mail : Site :
Author IdHAL : gael-varoquaux ORCID : https://orcid.org/0000-0003-1076-5122
1
ILPS - Information and Language Processing Systems (ISLA, University of Amsterdam, Science Park 904, 1098 XH Amsterdam - Netherlands)
- UvA - University of Amsterdam [Amsterdam] (Spui 21 1012 WX Amsterdam - Netherlands)
2
Systems and Modeling Research Unit (Institute Montefiore (B28, P32) Grande Traverse, 10 Sart-Tilman B-4000 Liège, Belgium. - Belgium)
- Université de Liège (Place du 20-Août, 7 4000 Liège - Belgium)
- Institute Montefiore (France)
3
Kobe University (Japan)
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)
- Inria Saclay - Ile de France (1 rue Honoré d'Estienne d'Orves
Bâtiment Alan Turing
Campus de l'École Polytechnique
91120 Palaiseau - France)
- Inria - Institut National de Recherche en Informatique et en Automatique (Domaine de Voluceau Rocquencourt - BP 105 78153 Le Chesnay Cedex - France)
- NEUROSPIN - Service NEUROSPIN (France)
- DRF (CEA) - Direction de Recherche Fondamentale (CEA) : DRF/JOLIOT (Direction de Recherche Fondamentale
Fundamental Research Division
CEA Saclay
91191 Gif-sur-Yvette
- France)
- CEA - Commissariat à l'énergie atomique et aux énergies alternatives (Centre de Saclay Centre de Grenoble Centre de Cadarache etc - France)
- Université Paris-Saclay (Espace Technologique, Bat. Discovery - RD 128 - 2e ét., 91190 Saint-Aubin - France)
- DRF (CEA) - Direction de Recherche Fondamentale (CEA) : DRF/JOLIOT (Direction de Recherche Fondamentale
Fundamental Research Division
CEA Saclay
91191 Gif-sur-Yvette
- France)
5
Autonomous Intelligent Systems Group (Rheinische Friedrich-Wilhelms-Universität Bonn Institut für Informatik VI Friedrich-Ebert-Allee 144 53113 Bonn - Germany)
- Rheinische Friedrich-Wilhelms-Universität Bonn (Regina-Pacis-Weg 3, 53113 Bonn - Germany)
6
Chercheur indépendant (France)
7
Computational Linguistics (Romania)
- University of Bucharest (36-46 Bd. M. Kogalniceanu, 5th District, 050107, Bucharest - Romania)
9
LTCI - Laboratoire Traitement et Communication de l'Information (46 rue Barrault F-75634 Paris Cedex 13 - France)
- Télécom ParisTech (46 rue Barrault 75634 Paris Cedex 13 - France)
- IMT - Institut Mines-Télécom [Paris] (37-39 Rue Dareau, 75014 Paris - France)
- CNRS - Centre National de la Recherche Scientifique : UMR5141 (France)
10
Science Information Technology and Engineering (Suite 9, Greenhill Enterprise Centre Ballarat 3350 - Australia)
- FedUni - University of Ballarat [Australie] (Australia)
11
University of Washington [Seattle] (Seattle, Washington 98105 - United States)
12
Samsung Electronics Research Institute (Communications House, South Street Staines, Middlesex TW18 4QE - United Kingdom)
- Samsung (France)
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.
Document type :
Conference papers
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Complete list of metadatas
https://hal.inria.fr/hal-00856511
Contributor : Gaël Varoquaux <>
Submitted on : Sunday, September 1, 2013 - 5:31:15 PM
Last modification on : Wednesday, June 24, 2020 - 4:18:59 PM
Document(s) archivé(s) le : Monday, December 2, 2013 - 8:55:29 AM
Contributor : Gaël Varoquaux <>
Submitted on : Sunday, September 1, 2013 - 5:31:15 PM
Last modification on : Wednesday, June 24, 2020 - 4:18:59 PM
Document(s) archivé(s) le : Monday, December 2, 2013 - 8:55:29 AM
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Identifiers
- HAL Id : hal-00856511, version 1
- ARXIV : 1309.0238
Citation
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⟩
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