https://github.com/djsutherland/skl-groups

scikit-learn addon to operate on set/"group"-based features
https://github.com/djsutherland/skl-groups

Last synced: 1 day ago
JSON representation

scikit-learn addon to operate on set/"group"-based features

• Host: GitHub
• URL: https://github.com/djsutherland/skl-groups
• Owner: djsutherland
• License: bsd-3-clause
• Created: 2014-06-10T22:36:35.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2016-08-08T22:38:51.000Z (over 8 years ago)
• Last Synced: 2024-11-07T18:53:59.570Z (11 days ago)
• Language: Python
• Homepage:
• Size: 2.16 MB
• Stars: 41
• Watchers: 6
• Forks: 7
• Open Issues: 25
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE.txt

Awesome Lists containing this project

README

        
|Travis|_

.. |Travis| image:: https://api.travis-ci.org/dougalsutherland/skl-groups.png?branch=master

.. _Travis: https://travis-ci.org/dougalsutherland/skl-groups

skl-groups

==========

skl-groups is a package to perform machine learning on sets (or "groups") of

features in Python. It extends the `scikit-learn `_

library with support for either transforming sets into feature vectors that

can be operated on with standard scikit-learn constructs or obtaining

pairwise similarity/etc matrices that can be turned into kernels for use in

scikit-learn.

For an introduction to the package, why you might want to use it, and how to

do so, check out

`the documentation `_.

skl-groups is still in fairly early development.

The precursor package, `py-sdm `_,

is still somewhat easier to use for some tasks (though it has less functionality

and less documentation); skl-groups will hopefully match it in the next few weeks.

Feel free to get in touch ([email protected]) if you're interested.

Installation

------------

Full instructions are

`in the documentation `_,

but the short version is to do::

    $ conda install -c dougal -c r skl-groups

if you use conda, or::

    $ pip install skl-groups

if not. If you pip install and want to use the kNN divergence estimator,

you'll need to install either

`cyflann `_

or the regular pyflann bindings to FLANN,

and you'll want a version of FLANN with OpenMP support.

A much faster version of the kNN estimator is enabled by the

skl-groups-accel package, which you can get via::

    $ pip install skl-groups-accel

It requires cyflann and a working C compiler with OpenMP support

(i.e. gcc, not clang).