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scikit-multimodallearn
======================
**scikit-multimodallearn** is a Python package implementing algorithms multimodal data.
It is compatible with `scikit-learn <http://scikit-learn.org/>`_, a popular
package for machine learning in Python.
Documentation
-------------
The **documentation** including installation instructions, API documentation
and examples is
`available online <http://dev.pages.lis-lab.fr/multimodal>`_.
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 <https://pypi.org/project/multiconfusion/>`_
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 <http://scikit-learn.org/stable/developers>`_
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::
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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 <https://developpement.lis-lab.fr/>`_ of the
`LIS <http://www.lis-lab.fr/>`_.
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},
}
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"
<https://link.springer.com/chapter/10.1007/978-3-642-23783-6_14>`_,
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" <http://www.theses.fr/en/2013AIXM4101>`_,
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**