Skip to content
Foundational library for Kernel methods in pattern analysis and machine learning
Python Jupyter Notebook Makefile
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.github
datasets simple comparison script Aug 28, 2019
demo_tutorials propagating docs and code changes from gh-pages Aug 13, 2019
docs
kernelmethods finalized and comprehensive test suite Aug 28, 2019
.coveragerc
.coveralls.yml
.editorconfig
.gitattributes setting up versioneer auto version Dec 26, 2018
.gitignore ignore editor config Jun 19, 2019
.travis.yml tigher cfg Aug 16, 2019
CODE_OF_CONDUCT.md Create CODE_OF_CONDUCT.md Jun 20, 2019
CONTRIBUTING.rst first commit Dec 26, 2018
HISTORY.rst
LICENSE
MANIFEST.in
Makefile first commit Dec 26, 2018
README.rst
cmd2pkg
pytest.ini
requirements.txt
requirements_dev.txt
setup.cfg
setup.py
tox.ini
versioneer.py

README.rst

Kernel methods and classes

https://coveralls.io/repos/github/raamana/kernelmethods/badge.svg?branch=master

Docs: https://raamana.github.io/kernelmethods/

Demo notebooks:

kernelmethods

docs/flyer.png

kernelmethods is a pure python library defining modular classes that provides basic kernel methods, such as computing various kernel functions on a given sample (N points of dimension p) as well as provides an intuitive interface for advanced functionality such as composite and hyper kernels. This library fills an important void in the ever-growing python-based machine learning ecosystem, where users can only use predefined kernels and are not able to customize or extend them for their own applications, that demand great flexibility owing to their diversity and need for better performing kernel. This library defines the KernelMatrix class that is central to all the kernel methods and machines. As the KernelMatrix class is a key bridge between input data and the various kernel learning algorithms, it is designed to be highly usable and extensible to different applications and data types. Besides being able to apply basic kernels on a given sample (to produce a KernelMatrix), this library provides various kernel operations, such as normalization, centering, product, alignment evaluation, linear combination and ranking (by various performance metrics) of kernel matrices.

In addition, we provide several convenient classes, such as KernelSet and KernelBucket for easy management of a large collection of kernels. Dealing with a diverse configuration of kernels is necessary for automatic kernel selection and optimization in applications such as Multiple Kernel Learning (MKL) and the like.

In addition to the common numerical kernels such as the Gaussian and Polynomial kernels, we designed this library to make it easy to develop categorical, string and graph kernels, with the same attractive properties of intuitive and highly-testable API. In addition to providing native implementation of non-numerical kernels, we aim to provide a deeply and easily extensible framework for arbitrary input data types, such as sequences, trees and graphs etc, via data structures such as pyradigm.

Moreover, drop-in Estimator classes are provided, called KernelMachine, offering the power of SVM for seamless usage in the scikit-learn ecosystem. Another useful class is called OptimalKernelSVR which finds the most optimal kernel func for a given sample, and trains the SVM using the optimal kernel.

Docs

https://raamana.github.io/kernelmethods/

Demo notebooks:

Note

The software is beta. All types of contributions are greatly welcome.

You can’t perform that action at this time.