bipartite-learn 0.7.4

Machine learning estimators for bipartite data.

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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:

1. A collection of tools to adapt usual algorithms to bipartite data;
2. 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.

Installation

bipartite_learn is available on PyPI, and thus can be installed with pip:

$ pip install bipartite_learn

Installation 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.