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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 <https://github.com/giotto-ai>`_
family of open-source projects.

Project genesis
===============

``giotto-tda`` is the result of a collaborative effort between `L2F SA <https://www.l2f.ch/>`_,
the `Laboratory for Topology and Neuroscience <https://www.epfl.ch/labs/hessbellwald-lab/>`_ at EPFL,
and the `Institute of Reconfigurable & Embedded Digital Systems (REDS) <https://heig-vd.ch/en/research/reds>`_ of HEIG-VD.

License
=======

.. _L2F team: [email protected]

``giotto-tda`` is distributed under the AGPLv3 `license <https://github.com/giotto-ai/giotto-tda/blob/master/LICENSE>`_.
If you need a different distribution license, please contact the `L2F team`_.

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

Please visit `https://giotto-ai.github.io/gtda-docs <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 <https://giotto-ai.github.io/gtda-docs/latest/installation.html#developer-installation>`_
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
<https://giotto-ai.github.io/gtda-docs/latest/contributing/index.html>`_.

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 <https://www.jmlr.org/papers/volume22/20-325/20-325.pdf>`_, 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
========

[email protected]