ens_clust

ens_clust provides the following consensus functions which work on an ensemble of clusterings.

Consensus Function	File
MM	mixture_model.py
Cumulative Voting: A-CV	cvs.py
Cumulative Voting: A-BV	cvs.py
Iterative Voting Consensus	ivc.py
QMI	adjusted_ba_kmeans.py

ens_clust contains multiple, simple ensemble generation strategies.

Installation

Install build-essential and miniconda

1. sudo apt-get install build-essential
2. Follow https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html to install Miniconda. We will create a Python 3.6 environment but you can install conda with a higher Python version (tested with Python 3.9).

Get the code and dependencies

git clone https://github.com/moemode/ensemble_clustering
cd ensemble_clustering
# Create and activate conda environment with needed dependencies
conda env create -f environment.yml
conda activate ensemble_clustering

Add ens_clust to environment

To install ens_clust, we create a file in the site packages folder of the ensemble_clustering environment. This has the advantage that you can change the code without reinstalling the package. Edit the file ~/miniconda3/envs/ensemble_clustering/lib/python3.6/site-packages/conda.pth
Write into it the absolute path to the ensemble_clustering folder. These must be absolute paths (do not use ~), for example

/home/#username/ensemble_clustering

Dependency: Install ib_base

ib_base is unfortunately neither on PyPi nor available as conda package. We need to download it manually to a separate folder.

git clone https://collaborating.tuhh.de/cip3725/ib_base.git
cd ib_base
python setup.py install
cd ..

Tests

Run the following command in the ensemble_clustering folder to run all tests
python -m unittest discover