stoclust is a package of modularized methods for stochastic and ensemble clustering techniques.

By modular, I mean that there are few methods in this package which act as a single pipeline for clustering a dataset–––rather, the methods each form a unit of what might be a larger clustering routine.

These modular units are designed to be compatible with general clustering methods from other packages, like scipy.clustering or sklearn.cluster. However, we also provide specific methods for implementing clustering algorithms whose underlying mathematics is rooted in stochastic analysis and dynamics. Additionally, one can add a stochastic twist to any clustering method by using ensemble clustering, which uses randomness to probe the stability and robustness of clustering results.

The core of our package is currently:

1. The two classes Aggregation and Hierarchy, which respectively formalize a single clustering or partition of a set, and a hierarchical clustering of a set, each in a manner that is amicable to numpy and pandas indexing, and allows cross-referencing between subsets and supersets;

2. The ensemble module, which can be used to generate noisy ensembles from a base dataset and to apply clustering methods to already-generated ensembles

3. The clustering module, which contains functions implementing selected stochastic clustering techniques;

4. The simulation and regulators modules, which currently allows the generation of regulated Markov random walks.

In addition to these are several auxiliary modules such as distance, which contains methods for calculating simple distance metrics from data; visualization, which contains methods for easily generating Plotly visualizations of data and clusters; and utils, which contains useful miscellaneous functions.

Check out our Documentation for further info!

Installation

To download the package, you can either download the zip or tarball from the sidebar, or clone the GitHub repository via

>>> git clone https://github.com/samlikesphysics/stoclust.git

The present installation consists of two console steps, run in the the same folder as setup.py:

>>> python setup.py build
>>> python -m pip install .

Dependencies

stoclust depends on the following packages:

Package	Recommended version
numpy	1.15.0
scipy	1.1.0
plotly	4.12.0
pandas	0.25.0
tqdm	4.41.1