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 [email protected]: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
