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.. image:: https://raw.githubusercontent.com/giotto-ai/giotto-tda/master/doc/images/tda_logo.svg :width: 850
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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.
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.
.. _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
.
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.
The latest stable version of giotto-tda
requires:
To run the examples, jupyter is required.
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.
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:
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>
_.
After developer installation, you can launch the test suite from outside the source directory ::
pytest gtda
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}
}
giotto-ai Slack workspace: https://slack.giotto.ai/
FAQs
Toolbox for Machine Learning using Topological Data Analysis.
We found that giotto-tda demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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