cnnclustering 0.3.11

A Python package for common-nearest neighbour (CNN) clustering

Common-nearest-neighbours (CNN) clustering and core-set Markov-state model estimation

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NOTE

This project is currently under development in the alpha state. The implementation may change in the future. Check the examples and the documentation for up-to-date information.

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cnnclustering

The cnnclustering Python package provides a flexible interface to use the common-nearest-neighbours cluster algorithm. While the method can be applied to abitrary 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 (csMSM) model to capture the essential dynamics of the underlying molecular processes.

The package provides two modules:

• cnn: (Hierarchical) CNN clustering and analysis
• cmsm: csMSM estimation and analysis

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 use 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.

Install

Refer to the documentation for more details. Install from PyPi

$ pip install cnnclustering

or clone the development version and install from a local branch

$ git clone https://github.com/janjoswig/CNN.git
$ cd CNN
$ pip install .

Quickstart

from cnnclustering import cnn
import matplotlib.pyplot as plt

# 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 = cnn.CNN(points=data_points)
clustering.fit(radius_cutoff=1.5, cnn_cutoff=1)
clustering.labels
# Labels([1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2])

fig, ax = plt.subplots(1, 2)
ax[0].set_title("original")
clustering.evaluate(ax=ax[0], original=True)

ax[1].set_title("clustered")
clustering.evaluate(ax=ax[1])

Alternative scikit-learn implementation

We provide an alternative approach to common-nearest-neighbours clustering in the spirit of the scikit-learn project over this fork of scikit-learn-contrib/scikit-learn-extra.