Scikit-dimension: a Python package for intrinsic dimension estimation

TL;DR

Scikit-dimension is an open-source Python package that provides a unified implementation of various intrinsic dimension estimators, facilitating easy evaluation of data complexity in machine learning applications.

Contribution

It introduces a comprehensive Python package for intrinsic dimension estimation, integrating multiple estimators and benchmarking tools in a user-friendly framework.

Findings

Benchmarking over 500 datasets demonstrates the package's effectiveness.

Unified implementation simplifies comparison of ID estimators.

The package supports both global and local intrinsic dimension evaluation.

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 \texttt{scikit-dimension}, an open-source Python package for intrinsic dimension estimation. \texttt{scikit-dimension} package provides a uniform implementation of most of the known ID estimators based on scikit-learn application programming interface to evaluate 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 in real-life and synthetic data. The source code is available from https://github.com/j-bac/scikit-dimension , the documentation is available from https://scikit-dimension.readthedocs.io .