multipers: Multiparameter Persistence for Machine Learning

International audience ; multipers is a Python library for Topological Data Analysis, focused on Multiparameter Persistence computation and visualizations for Machine Learning. It features several efficient computational and visualization tools, with integrated, easy to use, auto-differentiable Machine Learning pipelines, that can be seamlessly interfaced with scikit-learn (Pedregosa et al., 2011) and PyTorch (Paszke et al., 2019). This library is meant to be usable for non-experts in Topological or Geometrical Machine Learning. Performance-critical functions are implemented in C++ or in Cython (Behnel et al., 2011-03/2011-04), are parallelizable with TBB (Robison, 2011), and have Python bindings and interface. It can handle a very diverse range of datasets that can be framed into a (finite) multi-filtered simplicial or cell complex, including, e.g., point clouds, graphs, time series, images, etc.