Metadata-Version: 1.1
Name: pycobra
Version: 0.2.4
Summary: Python implementation of COBRA algorithm with regression analysis
Home-page: https://github.com/bhargavvader/pycobra
Author: ['Bhargav Srinivasa Desikan', 'Benjamin Guedj']
Author-email: ['bhargavvader@gmail.com', 'benjamin.guedj@inria.fr']
License: MIT
Description: |Travis Status| |Coverage Status| |Python27| |Python35|
        
        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 <http://jmlr.org/papers/v18/17-228.html>`__ "Pycobra: A Python Toolbox for Ensemble Learning and Visualisation",
        Journal of Machine Learning Research, vol. 18 (190), 1--5.
        
        
        Documentation and Examples
        ~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The
        `notebooks <https://github.com/bhargavvader/pycobra/tree/master/docs/notebooks>`__
        directory showcases the usage of pycobra, with examples and basic usage.
        The `documentation <https://modal.lille.inria.fr/pycobra/>`__ 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.
        -  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
        
Keywords: Aggregation of Predictors,Regression Analysis,Voronoi Tesselation,Statistical Aggregation
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
