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https://github.com/bhargavvader/pycobra

python library implementing ensemble methods for regression, classification and visualisation tools including Voronoi tesselations.
https://github.com/bhargavvader/pycobra

aggregation classification ensemble-learning machine-learning-algorithms machine-learning-library predictor python-library regression scikit-learn visualisation visualization voronoi-diagram

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python library implementing ensemble methods for regression, classification and visualisation tools including Voronoi tesselations.

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README 

        
|Travis Status| |Coverage Status| |Python27| |Python35|



pycobra

-------



Citation

~~~~~~~~~~~~~~~~~~~~~~~~~~



If you are using pycobra, please consider citing the following papers:



- Guedj and Srinivasa Desikan (2020), Kernel-based ensemble learning in Python. Information (`webpage `__)



- Guedj and Srinivasa Desikan (2018), Pycobra: A Python Toolbox for Ensemble Learning and Visualisation. Journal of Machine Learning Research (`webpage `__)



- Biau, Fischer, Guedj and Malley (2016), COBRA: A combined regression strategy. Journal of Multivariate Analysis (`webpage `__)



All these references are included in the file ``pycobra.bib``.



What is pycobra?

~~~~~~~~~~~~~~~~~~~~~~~~~~



pycobra is a python library for ensemble learning. It serves as a

toolkit for regression and classification using these ensembled

machines, and also for visualisation of the performance of the new

machine and constituent machines. Here, when we say machine, we mean any

predictor or machine learning object - it could be a LASSO regressor, or

even a Neural Network. It is scikit-learn compatible and fits into the

existing scikit-learn ecosystem.



pycobra offers a python implementation of the COBRA algorithm introduced

by Biau et al. (2016) for regression.



Another algorithm implemented is the EWA (Exponentially Weighted

Aggregate) aggregation technique (among several other references, you

can check the paper by Dalalyan and Tsybakov (2007).



Apart from these two regression aggregation algorithms, pycobra

implements a version of COBRA for classification. This procedure has

been introduced by Mojirsheibani (1999).



pycobra also offers various visualisation and diagnostic methods built

on top of matplotlib which lets the user analyse and compare different

regression machines with COBRA. The Visualisation class also lets you

use some of the tools (such as Voronoi Tesselations) on other

visualisation problems, such as clustering.



pycobra is described in the `paper `__ "Pycobra: A Python Toolbox for Ensemble Learning and Visualisation",

Journal of Machine Learning Research, vol. 18 (190), 1--5.



Documentation and Examples

~~~~~~~~~~~~~~~~~~~~~~~~~~



The

`notebooks `__

directory showcases the usage of pycobra, with examples and basic usage.

The `documentation `__ page further

covers how to use pycobra.



Installation

~~~~~~~~~~~~



Run ``pip install pycobra`` to download and install from PyPI.



Run ``python setup.py install`` for default installation.



Run ``python setup.py test`` to run all tests.



Run ``pip install .`` to install from source.



Dependencies

~~~~~~~~~~~~



-  Python 2.7+, 3.4+

-  numpy, scipy, scikit-learn, matplotlib, pandas, seaborn



References

~~~~~~~~~~



-  B. Guedj and B. Srinivasa Desikan (2018). Pycobra: A Python Toolbox for Ensemble Learning and Visualisation. 

   Journal of Machine Learning Research, vol. 18 (190), 1--5.

-  B. Guedj and B. Srinivasa Desikan (2020). Kernel-based ensemble learning in Python. 

   Information, vol. 11(2).

-  G. Biau, A. Fischer, B. Guedj and J. D. Malley (2016), COBRA: A

   combined regression strategy, Journal of Multivariate Analysis.

-  M. Mojirsheibani (1999), Combining Classifiers via Discretization,

   Journal of the American Statistical Association.

-  A. S. Dalalyan and A. B. Tsybakov (2007) Aggregation by exponential

   weighting and sharp oracle inequalities, Conference on Learning

   Theory.



.. |Travis Status| image:: https://travis-ci.org/bhargavvader/pycobra.svg?branch=master

   :target: https://travis-ci.org/bhargavvader/pycobra

.. |Coverage Status| image:: https://coveralls.io/repos/github/bhargavvader/pycobra/badge.svg?branch=master

   :target: https://coveralls.io/github/bhargavvader/pycobra?branch=master

.. |Python27| image:: https://img.shields.io/badge/python-2.7-blue.svg

   :target: https://pypi.python.org/pypi/pycobra

.. |Python35| image:: https://img.shields.io/badge/python-3.5-blue.svg

   :target: https://pypi.python.org/pypi/pycobra