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https://github.com/janjoswig/commonnnclustering
A Python package for common-nearest-neighbours clustering
https://github.com/janjoswig/commonnnclustering
clustering common-nearest-neighbours density-based-clustering markov-model molecular-dynamics scikit-learn
Last synced: 7 days ago
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A Python package for common-nearest-neighbours clustering
- Host: GitHub
- URL: https://github.com/janjoswig/commonnnclustering
- Owner: janjoswig
- License: mit
- Created: 2018-11-15T16:38:46.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-11-24T16:17:37.000Z (almost 2 years ago)
- Last Synced: 2024-04-26T21:03:10.213Z (5 months ago)
- Topics: clustering, common-nearest-neighbours, density-based-clustering, markov-model, molecular-dynamics, scikit-learn
- Language: Cython
- Homepage:
- Size: 204 MB
- Stars: 9
- Watchers: 2
- Forks: 0
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://pypi.org/project/cnnclustering/)
[](https://pypi.org/project/cnnclustering/)
[](https://pypi.org/project/cnnclustering/)
[](https://app.travis-ci.com/janjoswig/CommonNNClustering)
[](https://coveralls.io/github/janjoswig/CommonNNClustering)
Common-nearest-neighbour clustering
===================================
***
**NOTE**
*This project is now under further development at [https://github.com/bkellerlab/CommonNNClustering](https://github.com/bkellerlab/CommonNNClustering).*
*Please refer to this new version for updates.*
***
cnnclustering
-------------
The `cnnclustering` Python package provides a flexible interface to use the common-nearest-neighbours cluster algorithm. While the method can be applied to arbitrary data, this implementation was made before the background of processing trajectories from Molecular Dynamics simulations. In this context the cluster result can serve as a suitable basis for the construction of a core-set Markov-state (cs-MSM) model to capture the essential dynamics of the underlying molecular processes. For a tool for cs-MSM estimation, refer to this separate [project](https://github.com/janjoswig/cs-MSM).
The package provides a main module:
- `cluster`: User interface to (hierarchical) common-nearest-neighbour clustering
Further, it contains the modules:
- `plot`: Convenience functions to evaluate cluster results
- `_types`: Direct access to generic types representing needed cluster components
- `_fit`: Direct access to generic clustering procedures
Features:
- Flexible: Clustering can be done for data sets in different input formats. Easy interfacing with external methods.
- Convenient: Integration of functionality, handy in the context of Molecular Dynamics.
- Fast: Core functionalities implemented in Cython.
Please refer to the following papers for the scientific background (and consider citing if you find the method useful):
- B. Keller, X. Daura, W. F. van Gunsteren *J. Chem. Phys.*, __2010__, *132*, 074110.
- O. Lemke, B.G. Keller *J. Chem. Phys.*, __2016__, *145*, 164104.
- O. Lemke, B.G. Keller *Algorithms*, __2018__, *11*, 19.
Documentation
-------------
The package documentation (under developement) is available [here](https://janjoswig.github.io/CommonNNClustering/) online or under `docs/index.html`.
The sources for the documentation can be found under `docsrc/`.
Install
-------
Refer to the [documentation](https://janjoswig.github.io/CommonNNClustering/_source/install.html) for more details. Install from PyPi
```bash
$ pip install cnnclustering
```
or clone the development version and install from a local branch
```bash
$ git clone https://github.com/janjoswig/CommonNNClustering.git
$ cd CommonNNClustering
$ pip install .
```
Quickstart
----------
```python
>>> from cnnclustering import cluster
>>> # 2D data points (list of lists, 12 points in 2 dimensions)
>>> data_points = [ # point index
... [0, 0], # 0
... [1, 1], # 1
... [1, 0], # 2
... [0, -1], # 3
... [0.5, -0.5], # 4
... [2, 1.5], # 5
... [2.5, -0.5], # 6
... [4, 2], # 7
... [4.5, 2.5], # 8
... [5, -1], # 9
... [5.5, -0.5], # 10
... [5.5, -1.5], # 11
... ]
>>> clustering = cluster.Clustering(data_points)
>>> clustering.fit(radius_cutoff=1.5, cnn_cutoff=1, v=False)
>>> clustering.labels
array([1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2])
```
Alternative scikit-learn implementation
---------------------------------------
We provide an alternative approach to common-nearest-neighbours clustering in the spirit of the scikit-learn project within [scikit-learn-extra](https://github.com/scikit-learn-contrib/scikit-learn-extra).