giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration

Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella Perez, Matteo Caorsi, Wojciech Reise, Anibal Maximiliano Medina-Mardones, Alberto Dassatti, Kathryn Hess

Keywords: Topological Data Analysis, Machine Learning, Data Exploration, Persistent Homology, Mapper

TL;DR: We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations.

Abstract: We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations. The library's ability to handle various types of data is rooted in a wide range of preprocessing techniques, and its strong focus on data exploration and interpretability is aided by an intuitive plotting API. Source code, binaries, examples, and documentation can be found at https://github.com/giotto-ai/giotto-tda

Previous Submission: Yes

Community Implementations: [ 4 code implementations](https://www.catalyzex.com/paper/giotto-tda-a-topological-data-analysis/code)