clusteval 0.1.1

clusteval is a python package that provides various methods for unsupervised cluster validation.

clusteval

• clusteval is Python package for unsupervised cluster evaluation. Five methods are implemented that can be used to evalute clusterings; silhouette, dbindex, derivative, dbscan and hdbscan.

Methods

# X is your data
out = clusteval.fit(X)
clusteval.plot(out, X)

Contents

• Installation
• Requirements
• Quick Start
• Contribute
• Citation
• Maintainers
• License

Installation

• Install clusteval from PyPI (recommended). clusteval is compatible with Python 3.6+ and runs on Linux, MacOS X and Windows.
• It is distributed under the MIT license.

Requirements

• It is advisable to create a new environment.

conda create -n env_clusteval python=3.6
conda activate env_clusteval
pip install matplotlib numpy pandas tqdm seaborn hdbscan sklearn

Quick Start

pip install clusteval

• Alternatively, install clusteval from the GitHub source:

git clone https://github.com/erdogant/clusteval.git
cd clusteval
python setup.py install

Import clusteval package

import clusteval as clusteval

Create example data set

# Generate some random data
from sklearn.datasets import make_blobs
[X,_] = make_blobs(n_samples=750, centers=4, n_features=2, cluster_std=0.5)

Cluster validation using Silhouette score

# Determine the optimal number of clusters
out = clusteval.fit(X, method='silhouette')
fig = clusteval.plot(out, X)

Cluster validation using davies-boulin index

# Determine the optimal number of clusters
out = clusteval.fit(X, method='dbindex')
fig = clusteval.plot(out, X)

Cluster validation using derivative method

# Determine the optimal number of clusters
out = clusteval.fit(X, method='derivative')
fig = clusteval.plot(out)

Cluster validation using hdbscan

# Determine the optimal number of clusters
out = clusteval.fit(X, method='hdbscan')
fig = clusteval.plot(out)

Cluster validation using dbscan

# Determine the optimal number of clusters
out = clusteval.fit(X, method='dbscan')
fig = clusteval.plot(out, X)

Citation

Please cite clusteval in your publications if this is useful for your research. Here is an example BibTeX entry:

@misc{erdogant2019clusteval,
  title={clusteval},
  author={Erdogan Taskesen},
  year={2019},
  howpublished={\url{https://github.com/erdogant/clusteval}},
}

TODO

• Use ARI when the ground truth clustering has large equal sized clusters
• Usa AMI when the ground truth clustering is unbalanced and there exist small clusters https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html https://scikit-learn.org/stable/auto_examples/cluster/plot_adjusted_for_chance_measures.html#sphx-glr-auto-examples-cluster-plot-adjusted-for-chance-measures-py

Maintainers

• Erdogan Taskesen, github: erdogant

Contribute

• Contributions are welcome.

Licence

See LICENSE for details.

Donation

• This package is created and maintained in my free time. If this package is usefull, you can show your gratitude :) Thanks!