densired 1.0.0

A generator for density-based data

DENSIRED

This package provides a data generator for density-based data, described in the ECML PKDD 24 paper "Data with Density-Based Clusters: A Generator for Systematic Evaluation of Clustering Algorithms" by Philipp Jahn$^{1,2}$ , Christian M. M. Frey$^{3}$, Anna Beer$^{4}$, Collin Leiber$^{1,2}$, and Thomas Seidl$^{1,2,3}$.

(1 LMU Munich, Munich, Germany; 2 Munich Center for Machine Learning (MCML), Munich, Germany; 3 Fraunhofer IIS, Erlangen, Germany; 4 University of Vienna, Vienna, Austria.)

pip installation

The current stable version can be installed by the following command:

pip install densired

How to use

Use the following code to generate a skeleton. Parameters are listed at our Github Repository (https://github.com/PhilJahn/DENSIRED).

skeleton = datagen.densityDataGen()

Use the following code to obtain a dataset with n points from a skeleton. There are additional parameters, that are also listed below.

data = skeleton.generate_data(n)
datax = data[:,0:-1]
datay = data[:,-1]

To visualize the skeleton, call the following code. For higher-dimensionalities, either specify dcount to get all pairs of the dc most spread out dimensions or specify the desired dimensions directly with dims.

skeleton.display_cores(dims=[d1,d2,...], dcount=dc)

To visualize a dataset, call the following. Give the dataset as data. The flags show_radius and show_core decide whether to display the core radii and core centers, respectively. For higher-dimensionalities, as with dispaly_cores, either specify dcount to get all pairs of the dc most spread out dimensions or specify the desired dimensions directly with dims.

skeleton.display_data(data, show_radius=False, show_core=False, dims=[d1,d2,...], dcount=dc)

To initialize a stream, call the following function. The command-String controls the stream behavior. The default_duration is the default duration of a block of the stream. default_duration does not need to be specified, in which case it has a value of 1000. The command-String will be explained in more detail further below.

skeleton.init_stream(command=commandstring, default_duration = 1000)

In order to get an element from the stream, just use the skeleton as an iterator

x = skeleton.next()

To visualize a data stream, call the following.

skeleton.display_current_stream()

Alternatively, use this to set a stream command and display it in one command. Parameters are analogous to init_stream.

skeleton.display_stream(command=commandstring, default_duration = 1000)