.. image:: https://raw.githubusercontent.com/giotto-ai/giotto-tda/master/doc/images/tda_logo.svg
:width: 850
|Version|_ |Azure-build|_ |Azure-cov|_ |Azure-test|_ |Twitter-follow|_ |Slack-join|_
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.. _Version:
.. |Azure-build| image:: https://dev.azure.com/maintainers/Giotto/_apis/build/status/giotto-ai.giotto-tda?branchName=master
.. _Azure-build: https://dev.azure.com/maintainers/Giotto/_build?definitionId=6&_a=summary&repositoryFilter=6&branchFilter=141&requestedForFilter=ae4334d8-48e3-4663-af95-cb6c654474ea
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.. _Azure-test:
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.. _Twitter-follow: https://twitter.com/intent/follow?screen_name=giotto_ai
.. |Slack-join| image:: https://img.shields.io/badge/Slack-Join-yellow
.. _Slack-join: https://slack.giotto.ai/
==========
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: business@l2f.ch
``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
========
maintainers@giotto.ai
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"description": ".. image:: https://raw.githubusercontent.com/giotto-ai/giotto-tda/master/doc/images/tda_logo.svg\n :width: 850\n\n|Version|_ |Azure-build|_ |Azure-cov|_ |Azure-test|_ |Twitter-follow|_ |Slack-join|_\n\n.. |Version| image:: https://img.shields.io/pypi/v/giotto-tda\n.. _Version:\n\n.. |Azure-build| image:: https://dev.azure.com/maintainers/Giotto/_apis/build/status/giotto-ai.giotto-tda?branchName=master\n.. _Azure-build: https://dev.azure.com/maintainers/Giotto/_build?definitionId=6&_a=summary&repositoryFilter=6&branchFilter=141&requestedForFilter=ae4334d8-48e3-4663-af95-cb6c654474ea\n\n.. |Azure-cov| image:: https://img.shields.io/azure-devops/coverage/maintainers/Giotto/6/master\n.. _Azure-cov:\n\n.. |Azure-test| image:: https://img.shields.io/azure-devops/tests/maintainers/Giotto/6/master\n.. _Azure-test:\n\n.. |Twitter-follow| image:: https://img.shields.io/twitter/follow/giotto_ai?label=Follow%20%40giotto_ai&style=social\n.. _Twitter-follow: https://twitter.com/intent/follow?screen_name=giotto_ai\n\n.. |Slack-join| image:: https://img.shields.io/badge/Slack-Join-yellow\n.. _Slack-join: https://slack.giotto.ai/\n\n==========\ngiotto-tda\n==========\n\n``giotto-tda`` is a high-performance topological machine learning toolbox in Python built on top of\n``scikit-learn`` and is distributed under the GNU AGPLv3 license. It is part of the `Giotto <https://github.com/giotto-ai>`_\nfamily of open-source projects.\n\nProject genesis\n===============\n\n``giotto-tda`` is the result of a collaborative effort between `L2F SA <https://www.l2f.ch/>`_,\nthe `Laboratory for Topology and Neuroscience <https://www.epfl.ch/labs/hessbellwald-lab/>`_ at EPFL,\nand the `Institute of Reconfigurable & Embedded Digital Systems (REDS) <https://heig-vd.ch/en/research/reds>`_ of HEIG-VD.\n\nLicense\n=======\n\n.. _L2F team: business@l2f.ch\n\n``giotto-tda`` is distributed under the AGPLv3 `license <https://github.com/giotto-ai/giotto-tda/blob/master/LICENSE>`_.\nIf you need a different distribution license, please contact the `L2F team`_.\n\nDocumentation\n=============\n\nPlease visit `https://giotto-ai.github.io/gtda-docs <https://giotto-ai.github.io/gtda-docs>`_ and navigate to the version you are interested in.\n\nInstallation\n============\n\nDependencies\n------------\n\nThe latest stable version of ``giotto-tda`` requires:\n\n- Python (>= 3.7)\n- NumPy (>= 1.19.1)\n- SciPy (>= 1.5.0)\n- joblib (>= 0.16.0)\n- scikit-learn (>= 0.23.1)\n- pyflagser (>= 0.4.3)\n- python-igraph (>= 0.8.2)\n- plotly (>= 4.8.2)\n- ipywidgets (>= 7.5.1)\n\nTo run the examples, jupyter is required.\n\nUser installation\n-----------------\n\nThe simplest way to install ``giotto-tda`` is using ``pip`` ::\n\n python -m pip install -U giotto-tda\n\nIf necessary, this will also automatically install all the above dependencies. 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As the main library module is called ``gtda`` in\nboth the stable and nightly versions, ``giotto-tda`` and ``giotto-tda-nightly`` should not be installed in\nthe same environment.\n\nDeveloper installation\n----------------------\n\nPlease consult the `dedicated page <https://giotto-ai.github.io/gtda-docs/latest/installation.html#developer-installation>`_\nfor detailed instructions on how to build ``giotto-tda`` from sources across different platforms.\n\n.. _contributing-section:\n\nContributing\n============\n\nWe welcome new contributors of all experience levels. The Giotto\ncommunity goals are to be helpful, welcoming, and effective. To learn more about\nmaking a contribution to ``giotto-tda``, please consult `the relevant page\n<https://giotto-ai.github.io/gtda-docs/latest/contributing/index.html>`_.\n\nTesting\n-------\n\nAfter developer installation, you can launch the test suite from outside the\nsource directory ::\n\n pytest gtda\n\nImportant links\n===============\n\n- Official source code repo: https://github.com/giotto-ai/giotto-tda\n- Download releases: https://pypi.org/project/giotto-tda/\n- Issue tracker: https://github.com/giotto-ai/giotto-tda/issues\n\n\nCiting giotto-tda\n=================\n\nIf you use ``giotto-tda`` in a scientific publication, we would appreciate citations to the following paper:\n\n `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. 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