scikit-multimodallearn

scikit-multimodallearn is a Python package implementing algorithms multimodal data.

It is compatible with scikit-learn, a popular package for machine learning in Python.

Documentation

The documentation including installation instructions, API documentation and examples is available online.

Installation

Dependencies

scikit-multimodallearn works with Python 3.5 or later.

scikit-multimodallearn depends on scikit-learn (version >= 0.19).

Optionally, matplotlib is required to run the examples.

Installation using pip

scikit-multimodallearn is available on PyPI and can be installed using pip:

pip install scikit-multimodallearn

Development

The development of this package follows the guidelines provided by the scikit-learn community.

Refer to the Developer's Guide of the scikit-learn project for more details.

Source code

You can get the source code from the Git repository of the project:

git clone git@gitlab.lis-lab.fr:dev/multiconfusion.git

Testing

pytest and pytest-cov are required to run the test suite with:

cd multimodal
pytest

A code coverage report is displayed in the terminal when running the tests. An HTML version of the report is also stored in the directory htmlcov.

Generating the documentation

The generation of the documentation requires sphinx, sphinx-gallery, numpydoc and matplotlib and can be run with:

python setup.py build_sphinx

The resulting files are stored in the directory build/sphinx/html.

Credits

scikit-multimodallearn is developped by the development team of the LIS.

If you use scikit-multimodallearn in a scientific publication, please cite the following paper:

@InProceedings{Koco:2011:BAMCC,
 author={Ko\c{c}o, Sokol and Capponi, C{\'e}cile},
 editor={Gunopulos, Dimitrios and Hofmann, Thomas and Malerba, Donato
         and Vazirgiannis, Michalis},
 title={A Boosting Approach to Multiview Classification with Cooperation},
 booktitle={Proceedings of the 2011 European Conference on Machine Learning
            and Knowledge Discovery in Databases - Volume Part II},
 year={2011},
 location={Athens, Greece},
 publisher={Springer-Verlag},
 address={Berlin, Heidelberg},
 pages={209--228},
 numpages = {20},
 isbn={978-3-642-23783-6}
 url={https://link.springer.com/chapter/10.1007/978-3-642-23783-6_14},
 keywords={boosting, classification, multiview learning,
           supervised learning},
}

@InProceedings{Huu:2019:BAMCC,
 author={Huusari, Riika, Kadri Hachem and Capponi, C{\'e}cile},
 editor={},
 title={Multi-view Metric Learning in Vector-valued Kernel Spaces},
 booktitle={arXiv:1803.07821v1},
 year={2018},
 location={Athens, Greece},
 publisher={},
 address={},
 pages={209--228},
 numpages = {12}
 isbn={978-3-642-23783-6}
 url={https://link.springer.com/chapter/10.1007/978-3-642-23783-6_14},
 keywords={boosting, classification, multiview learning,
           merric learning, vector-valued, kernel spaces},
}

References

• Sokol Koço, Cécile Capponi, "Learning from Imbalanced Datasets with cross-view cooperation" Linking and mining heterogeneous an multi-view data, Unsupervised and semi-supervised learning Series Editor M. Emre Celeri, pp 161-182, Springer
• Sokol Koço, Cécile Capponi, "A boosting approach to multiview classification with cooperation", Proceedings of the 2011 European Conference on Machine Learning (ECML), Athens, Greece, pp.209-228, 2011, Springer-Verlag.
• Sokol Koço, "Tackling the uneven views problem with cooperation based ensemble learning methods", PhD Thesis, Aix-Marseille Université, 2013.
• Riikka Huusari, Hachem Kadri and Cécile Capponi, "Multi-View Metric Learning in Vector-Valued Kernel Spaces" in International Conference on Artificial Intelligence and Statistics (AISTATS) 2018

Copyright

Université d'Aix Marseille (AMU) - Centre National de la Recherche Scientifique (CNRS) - Université de Toulon (UTLN).

Copyright © 2017-2018 AMU, CNRS, UTLN

License

scikit-multimodallearn is free software: you can redistribute it and/or modify it under the terms of the New BSD License