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

Lars Buitinck; Gilles Louppe; Mathieu Blondel; Fabian Pedregosa; Andreas Mueller; Olivier Grisel; Vlad Niculae; Peter Prettenhofer; Alexandre Gramfort; Jaques Grobler; Robert Layton; Jake Vanderplas; Arnaud Joly; Brian Holt; Gaël Varoquaux

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

NEUROSPIN - Service NEUROSPIN, 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

Document type :

Conference papers

Domain :

Computer Science [cs] / Machine Learning [cs.LG]

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https://hal.inria.fr/hal-00856511
Contributor : Gaël Varoquaux <>
Submitted on : Sunday, September 1, 2013 - 5:31:15 PM
Last modification on : Thursday, March 7, 2019 - 3:34:14 PM
Long-term archiving on : Monday, December 2, 2013 - 8:55:29 AM

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