Kepler Mapper

Nature uses as little as possible of anything.

—Johannes Kepler

This is a library implementing the Mapper algorithm in Python. KeplerMapper can be used for visualization of high-dimensional data and 3D point cloud data. KeplerMapper can make use of Scikit-Learn API compatible cluster and scaling algorithms. You can find the source code on github at scikit-tda/kepler-mapper.

KeplerMapper employs approaches based on the MAPPER algorithm (Singh et al.) as first described in the paper “Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition”.

User's Guide

• Background
  • Topological Data Analysis
  • Mapper
• Getting Started
  • Setup
  • Usage
• Examples
  • More Examples
• Applications
  • Anomaly Detection
  • Natural Language Processing
  • Uses & Mentions
• Tutorials
  • HTML Visualization Features
  • Visualization Adapters
  • Demo of Plotlyviz features
  • Choosing a Lens
  • Using Kepler Mapper in Jupyter Notebook
• Case studies
  • KeplerMapper & NLP examples
  • Detecting Encrypted TOR Traffic with Boosting and Topological Data Analysis
  • Confidence Graphs: Representing Model Uncertainty in Deep Learning
  • Self-guessing mapper

API Reference

• API
  • Kepler Mapper
  • Visuals
  • Adapters

Citations

To credit KeplerMapper in your work, please cite both the JOSS paper and the Zenodo archive. The former provides a high level description of the package, and the latter points to a permanent record of all KeplerMapper versions (we encourage you to cite the specific version you used).

Example citations (for KeplerMapper 1.4.1):

van Veen et al., (2019). Kepler Mapper: A flexible Python implementation of the Mapper algorithm. Journal of Open Source Software, 4(42), 1315, https://doi.org/10.21105/joss.01315

Hendrik Jacob van Veen, Nathaniel Saul, David Eargle, & Sam W. Mangham. (2019, October 14). Kepler Mapper: A flexible Python implementation of the Mapper algorithm (Version 1.4.1). Zenodo. http://doi.org/10.5281/zenodo.4077395

Bibtex entry for JOSS article:

@article{KeplerMapper_JOSS,
    doi           = {10.21105/joss.01315},
    url           = {https://doi.org/10.21105/joss.01315},
    year          = {2019},
    publisher     = {The Open Journal},
    volume        = {4},
    number        = {42},
    pages         = {1315},
    author        = {Hendrik Jacob van Veen and Nathaniel Saul and David Eargle and Sam W. Mangham},
    title         = {Kepler Mapper: A flexible Python implementation of the Mapper algorithm.},
    journal       = {Journal of Open Source Software}
    }

Bibtex entry for the Zenodo archive, version 1.4.1:

@software{KeplerMapper_v1.4.1-Zenodo,
    author       = {Hendrik Jacob van Veen and
                    Nathaniel Saul and
                    Eargle, David and
                    Sam W. Mangham},
    title        = {{Kepler Mapper: A flexible Python implementation of
                     the Mapper algorithm}},
    month        = oct,
    year         = 2020,
    publisher    = {Zenodo},
    version      = {1.4.1},
    doi          = {10.5281/zenodo.4077395},
    url          = {https://doi.org/10.5281/zenodo.4077395}
}

Contributions

We welcome contributions of all shapes and sizes. There are lots of opportunities for potential projects, so please get in touch if you would like to help out. Everything from an implementation of your favorite distance, notebooks, examples, and documentation are all equally valuable so please don’t feel you can’t contribute.

See the contributing page in the code repository for more details.