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#computational-topology #machine-learning #mapper #scikit-learn #tda #topological-data-analysis #topological-machine-learning #topology

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README

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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 =======
.. _L2F team: business@l2f.ch

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


Documentation =============

Please visit https://giotto-ai.github.io/gtda-docs _ 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-section:
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: https://github.com/giotto-ai/giotto-tda
Download releases: https://pypi.org/project/giotto-tda/
Issue tracker: https://github.com/giotto-ai/giotto-tda/issues

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:

.. code:: bibtex

@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: https://slack.giotto.ai/

Contacts ========

maintainers@giotto.ai

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