MKLpy 0.5.1

A package for Multiple Kernel Learning scikit-compliant

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MKLpy is a framework for Multiple Kernel Learning (MKL) inspired by the [scikit-learn](http://scikit-learn.org/stable) project.

This package contains: * the implementation of some MKL algorithms, such as EasyMKL; * tools to operate on kernels, such as normalization, centering, summation, average…; * metrics, such as kernel_alignment, radius of Minimum Enclosing Ball, margin between classes, spectral ratio…; * kernel functions, including boolean kernels (disjunctive, conjunctive, DNF, CNF) and string kernels (spectrum, fixed length and all subsequences).

The documentation of MKLpy is available on [readthedocs.io](https://mklpy.readthedocs.io/en/latest/)!

Installation

MKLpy is also available on PyPI: `sh pip install MKLpy `

To work properly, MKLpy requires:

Resource | Website |

—— | ——- |

numpy | [https://www.numpy.org/](https://www.numpy.org/) |

PyTorch | [https://pytorch.org/](https://pytorch.org/) |

scikit-learn | [https://scikit-learn.org/stable/](https://scikit-learn.org/stable/) |

CVXOPT | [https://cvxopt.org/](https://cvxopt.org/) |

Examples

The folder examples contains several scripts and snippets of codes to show the potentialities of MKLpy. The examples show how to train a classifier, how to process data, and how to use kernel functions.

Additionally, you may read our [tutorials](https://mklpy.readthedocs.io/en/latest/)

Work in progress

MKLpy is under development! We are working to integrate several features, including: * further MKL algorithms, such as GRAM, MEMO, and SimpleMKL; * more kernels for structured data; * efficient optimization

Citing MKLpy

If you use MKLpy for a scientific purpose, please cite this library.