MKLpy

MKLpy is a framework for Multiple Kernel Learning (MKL) inspired by the scikit-learn project.

This package contains:

• the implementation of some MKL algorithms;
• 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 main MKL algorithms implemented in this library are

Name	Short description	Status	Source
AverageMKL	Computes the simple average of base kernels	Available	-
EasyMKL	Fast and memory efficient margin-based combination	Available	[1]
GRAM	Radius/margin ratio optimization	Available	[2]
R-MKL	Radius/margin ratio optimization	Available	[3]
MEMO	Margin maximization and complexity minimization	Available	[4]
PWMK	Heuristic based on individual kernels performance	Avaible	[5]
FHeuristic	Heuristic based on kernels alignment	Available	[6]
CKA	Centered kernel alignment optimization in closed form	Available	[7]
SimpleMKL	Alternate margin maximization	Work in progress	[5]

The documentation of MKLpy is available on readthedocs.io!

Installation

MKLpy is also available on PyPI:

pip install MKLpy

MKLpy leverages multiple scientific libraries, that are numpy, scikit-learn, PyTorch, and CVXOPT.

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

Work in progress

MKLpy is under development! We are working to integrate several features, including:

• additional MKL algorithms;
• more kernels for structured data;
• efficient optimization

Citing MKLpy

If you use MKLpy for a scientific purpose, please cite the following preprint.

@article{lauriola2020mklpy,
  title={MKLpy: a python-based framework for Multiple Kernel Learning},
  author={Lauriola, Ivano and Aiolli, Fabio},
  journal={arXiv preprint arXiv:2007.09982},
  year={2020}
}