Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation
Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been suggested for the purpose of estimating ID, but no standard package to easily apply them one by one or all at once has be...
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| Published in: | Entropy (Basel, Switzerland) Vol. 23; no. 10; p. 1368 |
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| Abstract | Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been suggested for the purpose of estimating ID, but no standard package to easily apply them one by one or all at once has been implemented in Python. This technical note introduces scikit-dimension, an open-source Python package for intrinsic dimension estimation. The scikit-dimension package provides a uniform implementation of most of the known ID estimators based on the scikit-learn application programming interface to evaluate the global and local intrinsic dimension, as well as generators of synthetic toy and benchmark datasets widespread in the literature. The package is developed with tools assessing the code quality, coverage, unit testing and continuous integration. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation for real-life and synthetic data. |
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| AbstractList | Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been suggested for the purpose of estimating ID, but no standard package to easily apply them one by one or all at once has been implemented in Python. This technical note introduces scikit-dimension, an open-source Python package for intrinsic dimension estimation. The scikit-dimension package provides a uniform implementation of most of the known ID estimators based on the scikit-learn application programming interface to evaluate the global and local intrinsic dimension, as well as generators of synthetic toy and benchmark datasets widespread in the literature. The package is developed with tools assessing the code quality, coverage, unit testing and continuous integration. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation for real-life and synthetic data. Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been suggested for the purpose of estimating ID, but no standard package to easily apply them one by one or all at once has been implemented in Python. This technical note introduces scikit-dimension, an open-source Python package for intrinsic dimension estimation. The scikit-dimension package provides a uniform implementation of most of the known ID estimators based on the scikit-learn application programming interface to evaluate the global and local intrinsic dimension, as well as generators of synthetic toy and benchmark datasets widespread in the literature. The package is developed with tools assessing the code quality, coverage, unit testing and continuous integration. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation for real-life and synthetic data.Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been suggested for the purpose of estimating ID, but no standard package to easily apply them one by one or all at once has been implemented in Python. This technical note introduces scikit-dimension, an open-source Python package for intrinsic dimension estimation. The scikit-dimension package provides a uniform implementation of most of the known ID estimators based on the scikit-learn application programming interface to evaluate the global and local intrinsic dimension, as well as generators of synthetic toy and benchmark datasets widespread in the literature. The package is developed with tools assessing the code quality, coverage, unit testing and continuous integration. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation for real-life and synthetic data. |
| Author | Zinovyev, Andrei Bac, Jonathan Gorban, Alexander N. Mirkes, Evgeny M. Tyukin, Ivan |
| AuthorAffiliation | 2 INSERM, U900, 75248 Paris, France 3 CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, 75272 Paris, France 5 Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky University, 603105 Nizhniy Novgorod, Russia 4 Department of Mathematics, University of Leicester, Leicester LE1 7RH, UK; em322@leicester.ac.uk (E.M.M.); a.n.gorban@leicester.ac.uk (A.N.G.); i.tyukin@leicester.ac.uk (I.T.) 1 Institut Curie, PSL Research University, 75248 Paris, France |
| AuthorAffiliation_xml | – name: 5 Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky University, 603105 Nizhniy Novgorod, Russia – name: 3 CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, 75272 Paris, France – name: 1 Institut Curie, PSL Research University, 75248 Paris, France – name: 4 Department of Mathematics, University of Leicester, Leicester LE1 7RH, UK; em322@leicester.ac.uk (E.M.M.); a.n.gorban@leicester.ac.uk (A.N.G.); i.tyukin@leicester.ac.uk (I.T.) – name: 2 INSERM, U900, 75248 Paris, France |
| Author_xml | – sequence: 1 givenname: Jonathan surname: Bac fullname: Bac, Jonathan – sequence: 2 givenname: Evgeny M. orcidid: 0000-0003-1474-1734 surname: Mirkes fullname: Mirkes, Evgeny M. – sequence: 3 givenname: Alexander N. orcidid: 0000-0001-6224-1430 surname: Gorban fullname: Gorban, Alexander N. – sequence: 4 givenname: Ivan orcidid: 0000-0002-7359-7966 surname: Tyukin fullname: Tyukin, Ivan – sequence: 5 givenname: Andrei orcidid: 0000-0002-9517-7284 surname: Zinovyev fullname: Zinovyev, Andrei |
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