Speaker: Umberto Lupo, EPFL

Title: giotto-tda tutorial: machine learning pipelines with persistent homology and Mapper

Abstract: giotto-tda is 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.

Its large selection of preprocessing techniques, of persistent homology algorithms, and of featurization methods for persistence diagrams, allows for the flexible creation and tuning of end-to-end topological machine learning pipelines for various types of data (e.g. point clouds, graphs, images, and time series).

The Mapper algorithm is implemented in giotto-tda as a scikit-learn pipeline with a parallelized clustering step. Furthermore, an interactive plotting API allows one to tune Mapper’s hyperparameters and observe how the resulting graph changes in real time.

In this tutorial, we will illustrate some of giotto-tda‘s functionalities by a) showing how to create pipelines for time series classification using the time-delay embedding technique, and b) showcasing the library’s generic and extensible Mapper implementation. Source code: https://github.com/giotto-ai/giotto-tda.  Tutorial material: https://github.com/ulupo/giotto-tda_demo.