giotto-learn is a high performance topological toolbox for machine learning in Python built on top of scikit-learn and is distributed under the Apache 2.0 license. It is part of the Giotto open-source project.
The project was started jointly by Learn To Forecast - L2F, EPFL Laboratory for topology and neuroscience and the Reconfigurable and Embedded Digital Systems at heig-vd.
The code is under active development and is maintained and developed by members of those three institutions. See the GOVERNANCE.rst file for a list of the Giotto team members.
Website: http://www.giotto.ai
giotto-learn requires:
- Python (>= 3.5)
- scikit-learn (>= 0.21.3)
- NumPy (>= 1.11.0)
- SciPy (>= 0.17.0)
- joblib (>= 0.11)
For running the examples jupyter, matplotlib and plotly are required.
If you already have a working installation of numpy and scipy,
the easiest way to install giotto-learn is using pip
pip install -U giotto-learn
- HTML documentation (stable release): http://www.giotto.ai/docs/
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-learn, please see the CONTRIBUTING.rst file.
- C++14 compatible compiler
- CMake >= 3.9
- Boost >= 1.56
You can check the latest sources with the command:
git clone https://github.com/giotto-learn/giotto-learn.git
cd giotto-learn
pip install -e .From there any change in the library files will be immediately available on your machine.
After installation, you can launch the test suite from outside the source directory:
pytest giotto
See the RELEASE.rst file for a history of notable changes to giotto-learn.
- Official source code repo: https://github.com/giotto-learn/giotto-learn
- Download releases: https://pypi.org/project/giotto-learn/
- Issue tracker: https://github.com/giotto-learn/giotto-learn/issues
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