Machine learning estimators tailored to bipartite datasets.
In a usual machine learning setting, one is interested in predicting a set of
outputs y from a given feature vector x representing an input instance.
There are tasks, however, that are sometimes better modeled as bipartite
networks, in which two domains of instances are present and only inter-domain
relationships are plausible between pairs of instances. The goal is then to
predict aspects (y) of such interaction between a sample from the first domain
and another from the second domain, respectively represented by feature vectors
x1 and x2. In other words, it is sometimes desirable to model a function
in the format (x1, x2) -> y rather than the usual x -> y format.
Examples of such tasks can be found in the realms of interaction prediction and recommendation systems, and the datasets corresponding to them can be presented
as a pair of design matrices (X1 and X2) together with an interaction
matrix Y that describes each relationship between the samples X1[i] and
X2[j] in the position Y[i, j].
This package provides:
- A collection of tools to adapt usual algorithms to bipartite data;
- Tree-based estimators designed specifically to such datasets, which yield expressive performance improvements over the naive adaptations of their monopartite counterparts.
A documentation for bipartite_learn, still in its infancy, can be found at
bipartite-learn.rtfd.io.
Please refer to the User Guide for more information
on how to use this package.
bipartite_learn is available on PyPI, and thus can be installed with pip:
$ pip install bipartite_learnInstallation from source can be done by cloning this repository and calling
pip install in the root folder.
$ git clone https://github.com/pedroilidio/bipartite_learn
$ cd bipartite_learn
$ pip install --editable .The optional --editable (or -e) flag links the installed package to the
local cloned repository, so that local changes in it will immediatly be active
without the need for reinstallation.