SD4Py 0.1.6

Subgroup discovery and visualisation for python based on the VIKAMINE kernel

SD4Py

• License: LGPL (GNU Lesser General Public License v3.0)
• © 2021-2023 Dan Hudson, Martin Atzmueller - UOS, Semantic Information Systems Group

SD4Py is a package that makes it easy to perform subgroup discovery on tabular data. It is extremely simple to use. Call the sd4py.discover_subgroups() function on a Pandas dataframe and a collection of subgroups will be returned.

This package provides a Python interface for using the Java application VIKAMINE.

Before installing

SD4Py provides an interface to the Java application VIKAMINE. This means that Java and the Java Development Kit (JDK) must be installed before installing SD4Py.

Quick overview of subgroup discovery

Subgroup discovery is based on finding patterns within some (explanatory) columns of data that then help to explain another (target) column of data. The goal of the subgroup discovery process will be to understand in what circumstances the target is extreme. With a numeric target, this means finding circumstances in which the value is exceptionally high (or exceptionally low) on average. For a non-numeric target, this means looking for circumstances when a particular value is especially likely to occur. One of the key benefits of this approach is that the outputs are interpretable, being expressed as a readable combination of rules like (e.g.) "'Temperature'=high AND 'Pressure'=low".

One thing to note when performing the analysis is that subgroup discovery supports an iterative approach. This means that the subgroup discovery process can be run to obtain initial subgroups, which then might suggest changes like adding or removing variables or refining the search parameters, before re-running the process.

The outputs of the process are discovered subgroups that help to explain the target.

Important note on dependencies

There are two important dependencies for SD4Py. The first is JPype, which is used to run the JRE behind the scenes (although the user does not need to interact with JPype). In order to get JPype to work, you must first install Java and the Java Development Kit (JDK). The second is Pandas, which is used to store and manipulate tabular data. The data you want to use must be in a Pandas dataframe.

Basic usage example

Please see the basic usage example provided as a notebook on the SD4Py GitHub page https://github.com/cslab-hub/sd4py to see how to get started quickly.

Detailed documentation

Documentation can be accessed by using the `help' function within Python, or from the website https://cslab-hub.github.io/sd4py.