https://github.com/escherba/clustering-metrics
Python implementations of various clustering metrics
https://github.com/escherba/clustering-metrics
Last synced: 6 months ago
JSON representation
Python implementations of various clustering metrics
- Host: GitHub
- URL: https://github.com/escherba/clustering-metrics
- Owner: escherba
- License: bsd-3-clause
- Created: 2016-09-13T19:27:30.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2025-10-04T16:55:32.000Z (10 months ago)
- Last Synced: 2025-10-04T18:28:40.811Z (10 months ago)
- Language: Python
- Size: 1.33 MB
- Stars: 9
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
Awesome Lists containing this project
README
Clustering Metrics
==================
A Python implementation of various metrics (primarily external) used for clustering evaluation. The documentation is `available online here `_.
Motivation
----------
After creating an in-memory representation of a clustering or a partition, many common metrics can be calculated
very cheaply. The efficiency of the computation depends primarily on the in-memory representation of clustering.
Sparse representations are pefect for this purpose and allow us to calculate many metrics more efficiently than
packages like Scikit-Learn.
Installation
------------
At the moment, the package is not on PyPI. To install it, use ``pip`` like so:
.. code-block:: bash
pip install git+https://github.com/escherba/pymaptools#egg=pymaptools-0.2.31
pip install git+https://github.com/escherba/clustering-metrics#egg=clustering_metrics-0.0.2
Usage
-----
Clusters can be represented in different ways. One way is to enumerate all items in the cluster with integer labels:
.. code-block:: python
>>> ground_truth = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2]
>>> predicted = [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 2, 2, 1, 2, 2, 2]
Note that ``ground_truth`` and ``predicted`` must have the same length. We can then produce various metrics
as follows:
.. code-block:: python
>>> from clustering_metrics.metrics import ClusteringMetrics
>>> cm = ClusteringMetrics.from_labels(ground_truth, predicted)
>>> cm.adjusted_rand_index()
0.242914979757085
Another way to represent clusters is using partition-style encoding. Here, each clustering is represented
as a set of partitions:
.. code-block:: python
>>> ground_truth = [{1, 2, 3, 4}, {5, 6, 7, 8, 9, 10}, {11, 12, 13, 14, 15, 16}]
>>> predicted = [{1, 2, 3, 4}, {5, 6, 7, 8, 9, 10, 11, 12}, {13, 14, 15, 16}]
>>> cm = ClusteringMetrics.from_partitions(ground_truth, predicted)
>>> cm.split_join_distance(normalize=False)
4
Development
-----------
For development and testing, this package sets up a Python virtualenv under ``./env/``
relative to the source tree root.
.. code-block:: bash
git clone https://github.com/escherba/clustering-metrics.git
cd clustering-metrics
make test
The above should finish without interruptions and all tests should pass. To generate documentation:
.. code-block:: bash
make doc-sources
make doc-html
make doc-publish
License
-------
This package is under a BSD 3-clause license.