Code License:	New BSD License
Labels:	scipy, numpy, linkage, dendrogram, matlab, python, hierarchicalclustering, pdist, agglomorativeclustering, wardsalgorithm

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	damian.eads

News: all of the few remaining calls to scipy have been replaced with calls to numpy. Versions 0.1.8 and above do not require scipy as a dependency.

Introduction

This library provides Python functions for agglomerative clustering. Its features include

generating hierarchical clusters from distance matrices
computing distance matrices from observation vectors
computing statistics on clusters
cutting linkages to generate flat clusters
and visualizing clusters with dendrograms.

The interface is very similar to MATLAB's Statistics Toolbox API to make code easier to port from MATLAB to Python/Numpy. The core implementation of this library is in C for efficiency.

Setup and Installation

Windows

Install dependencies

Install Numpy by downloading the installer and running it. Make sure to run the installer for your version of Python (only Python versions 2.4 or 2.5 are supported).

If you use hcluster for plotting dendrograms, you will need matplotlib. Again, download the matplotlib installer for your version of Python. Scipy is optional.

Note: The few remaining calls to scipy have been replaced with numpy calls. Scipy is no longer required for hcluster.

Install hcluster

Note: If you previously installed hcluster, remove it by going to Control Panel::Add/Remove Programs.

Download the installer that corresponds to your Python version. Run it.

Optional

Install the IPython and pyreadline libraries for a more user-friendly console interface to Numpy, Scipy, and Matplotlib. Ctypes is required for Python 2.4.

Pypi

hcluster is available in the pypi index.

Linux

Debian

hcluster is available as a Debian package. Type

apt-get install python-hcluster

to install python-hcluster and its dependencies.

Thanks to Michael Hanke for packaging.

FreeBSD

hcluster is available as a FreeBSD package. Type

cd /usr/ports/science/py-hcluster/ && make install clean

to install python-hcluster as a port. Otherwise, type

pkg_add -r py25-hcluster

to add as a package.

Thanks to Wen Heping for packaging.

Ubuntu

Required Install numpy (required) by typing the following shell command as root:

apt-get install python-numpy

Optional Install optional packages by typing the following shell commands as root:

apt-get install python-matplotlib # needed for dendrograms
apt-get install ipython
apt-get install python-scipy

Then follow the instructions for building from source on UNIX.

Fedora and Red Hat Enterprise

Required Install numpy (required) by typing the following shell command as root:

yum install numpy

Optional Install optional packages by typing the following shell commands as root:

# The following are optional
yum install matplotlib # needed for dendrograms
yum install ipython
yum install scipy

to install Numpy, Scipy, and matplotlib.

Then follow the instructions for building from source on UNIX.

Build from source on UNIX

Download the source tar ball, unpack it, and go into the source directory.

gzip -cd hcluster-XXX.tar.gz | tar xvf -
cd hcluster-XXX

Build the package by running the setup.py script with build as the build command.

python setup.py build

Install the package to a prefix of your choice (e.g. /afs/qp/lib/python2.X/site-packages) with install as the build command.

python setup.py install --prefix=/afs/qp

The --prefix option is optional and defaults to /usr/local on UNIX.

hcluster Functions

The hcluster Python library has an interface that is very similar to MATLAB's suite of hierarchical clustering functions found in the Statistics Toolbox. Some of the functions should be familiar to users of MATLAB (e.g. linkage, pdist, squareform, cophenet, inconsistent, and dendrogram). The fcluster and fclusterdata are equivalent to MATLAB's cluster and cluseterdata functions. All of the functions in this library reside in the hcluster package, which must be imported prior to using its functions.

Python Help

If you are unfamiliar with python, the Python Tutorial is a good start. If you are looking for a good reference book, I highly recommend David Beazley's Python Essential Reference. It is by far the most comprehensive book I've come across, covering most of python's functionality with a very complete index.

A Quick Example

This script imports the pdist, linkage, and dendrogram functions. It then generates 10 random 100-dimensional observation vectors (with pdist), hierarchically clusters them (with linkage), and visualizes the result (with dendrogram).

from hcluster import pdist, linkage, dendrogram
import numpy
from numpy.random import rand

X = rand(10,100)
X[0:5,:] *= 2
Y = pdist(X)
Z = linkage(Y)
dendrogram(Z)

Function Listing

Flat cluster formation

fcluster: forms flat clusters from hierarchical clusters.
fclusterdata: forms flat clusters directly from data.
leaders: singleton root nodes for flat cluster.

Agglomerative cluster formation

linkage: agglomeratively clusters original observations.
single: the single/min/nearest algorithm. (alias)
complete: the complete/max/farthest algorithm. (alias)
average: the average/UPGMA algorithm. (alias)
weighted: the weighted/WPGMA algorithm. (alias)
centroid: the centroid/UPGMC algorithm. (alias)
median: the median/WPGMC algorithm. (alias)
ward: the Ward/incremental algorithm. (alias)

Distance matrix computation from a collection of raw observation vectors

pdist: computes distances between each observation pair.
squareform: converts a sq. D.M. to a condensed one and vice versa.

Statistic computations on hierarchies

cophenet: computes the cophenetic distance between leaves.
from_mlab_linkage: converts a linkage produced by MATLAB(TM).
inconsistent: the inconsistency coefficients for cluster.
maxinconsts: the maximum inconsistency coefficient for each cluster.
maxdists: the maximum distance for each cluster.
maxRstat: the maximum specific statistic for each cluster.
to_mlab_linkage: converts a linkage to one MATLAB(TM) can understand.

Visualization

dendrogram: visualizes linkages (requires matplotlib).

Tree representations of hierarchies

cnode: represents cluster nodes in a cluster hierarchy.
lvlist: a left-to-right traversal of the leaves.
totree: represents a linkage matrix as a tree object.

Distance functions between two vectors u and v

braycurtis: the Bray-Curtis distance.
canberra: the Canberra distance.
chebyshev: the Chebyshev distance.
cityblock: the Manhattan distance.
correlation: the Correlation distance.
cosine: the Cosine distance.
dice: the Dice dissimilarity (boolean).
euclidean: the Euclidean distance.
hamming: the Hamming distance (boolean).
jaccard: the Jaccard distance (boolean).
kulsinski: the Kulsinski distance (boolean).
mahalanobis: the Mahalanobis distance.
matching: the matching dissimilarity (boolean).
minkowski: the Minkowski distance.
rogerstanimoto: the Rogers-Tanimoto dissimilarity (boolean).
russellrao: the Russell-Rao dissimilarity (boolean).
seuclidean: the normalized Euclidean distance.
sokalmichener: the Sokal-Michener dissimilarity (boolean).
sokalsneath: the Sokal-Sneath dissimilarity (boolean).
sqeuclidean: the squared Euclidean distance.
yule: the Yule dissimilarity (boolean).

Predicates

is_valid_dm: checks for a valid distance matrix.
is_valid_im: checks for a valid inconsistency matrix.
is_valid_linkage: checks for a valid hierarchical clustering.
is_valid_y: checks for a valid condensed distance matrix.
is_isomorphic: checks if two flat clusterings are isomorphic.
is_monotonic: checks if a linkage is monotonic.
Z_y_correspond: checks for validity of distance matrix given a linkage