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giotto-tda

giotto-tda is a high-performance topological machine learning toolbox in Python built on top of scikit-learn and is distributed under the GNU AGPLv3 license. It is part of the Giotto family of open-source projects.

Project genesis

giotto-tda is the result of a collaborative effort between L2F SA, the Laboratory for Topology and Neuroscience at EPFL, and the Institute of Reconfigurable & Embedded Digital Systems (REDS) of HEIG-VD.

License

giotto-tda is distributed under the AGPLv3 license. If you need a different distribution license, please contact the L2F team.

Documentation

Please visit and navigate to the version you are interested in.

Installation

Dependencies

The latest stable version of giotto-tda requires:

• Python (>= 3.7)
• NumPy (>= 1.19.1)
• SciPy (>= 1.5.0)
• joblib (>= 0.16.0)
• scikit-learn (>= 0.23.1)
• pyflagser (>= 0.4.3)
• python-igraph (>= 0.8.2)
• plotly (>= 4.8.2)
• ipywidgets (>= 7.5.1)

To run the examples, jupyter is required.

User installation

The simplest way to install giotto-tda is using pip :

python -m pip install -U giotto-tda

If necessary, this will also automatically install all the above dependencies. Note: we recommend upgrading pip to a recent version as the above may fail on very old versions.

Pre-release, experimental builds containing recently added features, and/or bug fixes can be installed by running :

python -m pip install -U giotto-tda-nightly

The main difference between giotto-tda-nightly and the developer installation (see the section on contributing, below) is that the former is shipped with pre-compiled wheels (similarly to the stable release) and hence does not require any C++ dependencies. As the main library module is called gtda in both the stable and nightly versions, giotto-tda and giotto-tda-nightly should not be installed in the same environment.

Developer installation

Please consult the dedicated page for detailed instructions on how to build giotto-tda from sources across different platforms.

Contributing

We welcome new contributors of all experience levels. The Giotto community goals are to be helpful, welcoming, and effective. To learn more about making a contribution to giotto-tda, please consult the relevant page.

Testing

After developer installation, you can launch the test suite from outside the source directory :

pytest gtda

Important links

• Official source code repo:
• Download releases:
• Issue tracker:

Citing giotto-tda

If you use giotto-tda in a scientific publication, we would appreciate citations to the following paper:

> giotto-tda: A Topological Data Analysis Toolkit for Machine Learning > and Data > Exploration, > Tauzin et al, J. Mach. Learn. Res. 22.39 (2021): 1-6.

You can use the following BibTeX entry:

@article{giotto-tda,
  author  = {Guillaume Tauzin and Umberto Lupo and Lewis Tunstall and Julian Burella P\'{e}rez and Matteo Caorsi and Anibal M. Medina-Mardones and Alberto Dassatti and Kathryn Hess},
  title   = {giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration},
  journal = {Journal of Machine Learning Research},
  year    = {2021},
  volume  = {22},
  number  = {39},
  pages   = {1-6},
  url     = {http://jmlr.org/papers/v22/20-325.html}
}

Community

giotto-ai Slack workspace:

Contacts