Metadata-Version: 2.1
Name: gradientcobra
Version: 1.0.3
Summary: Python implementation for Gradient COBRA: A kernel-based consensual aggregation for regression by S. Has (2023).
Home-page: https://github.com/hassothea/gradientcobra/
Author: ['Sothea Has']
Author-email: sothea.has@lpsm.paris
Keywords: Consensual aggregation,Kernel,Regression,Statistical Aggregation
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Operating System :: Microsoft :: Windows
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
License-File: LICENSE
Requires-Dist: numpy>=1.20.1
Requires-Dist: pandas>=2.1.0
Requires-Dist: scipy>=1.10.1
Requires-Dist: scikit-learn>=1.2
Requires-Dist: matplotlib
Requires-Dist: seaborn

gradientcobra
=============

|Travis Status| |Coverage Status| |Python39|

Introduction
------------

``gradientcobra`` is the ``python`` package implementation of `S. Has (2023) <https://jdssv.org/index.php/jdssv/article/view/70>`__, which is a Kernel-based consensual aggregation method for regression problems. 
Is is a regular kernel-based version of `COBRA` method of `Biau et al. (2016) <https://www.sciencedirect.com/science/article/pii/S0047259X15000950>`__. 
We have theoretically shown that the consistency inheritance property also holds for this kernel-based configuration, and the same convergence rate as classical COBRA is achieved.
Moreoever, gradient descent algorithm is applied to efficiently estimate the bandwidth parameter of the method. This efficiency is illustrated in several numerical experiments on simulated and real datasets.

Installation
------------

In your terminal, run the following command to download and install from PyPI:

 `pip install gradientcobra`


Citation
--------

If you find ``gradientcobra`` helpful, please consider citing the following papaers:

- S., Has (2023), `Gradient COBRA: A kernel-based consensual aggregation for regression <https://jdssv.org/index.php/jdssv/article/view/70>`__.

- Biau, Fischer, Guedj and Malley (2016), `COBRA: A combined regression strategy <https://doi.org/10.1016/j.jmva.2015.04.007>`__.


Documentation and Examples
--------------------------

For more information and how to use the package, read `gradientcobra documentation <https://hassothea.github.io/files/CodesPhD/gradientcobra_doc.html>`__.

Dependencies
------------

-  Python 3.9+
-  numpy, scipy, scikit-learn, matplotlib, pandas, seaborn

References
----------

-  HAS, S. (2023). A Gradient COBRA: A kernel-based consensual aggregation for regression. 
   Journal of Data Science, Statistics, and Visualisation, 3(2). 
   Retrieved from `<https://jdssv.org/index.php/jdssv/article/view/70>`__.
-  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.

.. |Travis Status| image:: https://img.shields.io/travis/hassothea/gradientcobra.svg?branch=master
   :target: https://travis-ci.org/hassothea/gradientcobra

.. |Python39| image:: https://img.shields.io/badge/python-3.9-green.svg
   :target: https://pypi.python.org/pypi/gradientcobra

.. |Coverage Status| image:: https://img.shields.io/codecov/c/github/hassothea/gradientcobra.svg
   :target: https://codecov.io/gh/hassothea/gradientcobra
