Skip to content
An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more.
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
doc fixed a couple bugs in the greedy split routine, updated a bit of dox… Aug 17, 2012
include changed some interfaces in the python interface and c++ interfaces, n… Feb 17, 2016
python
src changed some interfaces in the python interface and c++ interfaces, n… Feb 17, 2016
test changed some interfaces in the python interface and c++ interfaces, n… Feb 17, 2016
.gitignore added travis CI script Jan 24, 2016
.travis.yml
CMakeLists.txt
COPYING
COPYING.LESSER changed to LGPLv3 Oct 9, 2014
FindEigen3.cmake think I may have solved the numpy fedora issue Jan 28, 2015
README.md

README.md

Libcluster

CI status

Author: Daniel Steinberg

License: LGPL v3 (See COPYING and COPYING.LESSER)

Overview:

This library implements the following algorithms with variational Bayes learning procedures and efficient cluster splitting heuristics:

  • The Variational Dirichlet Process (VDP) [1, 2, 6]
  • The Bayesian Gaussian Mixture Model [3 - 6]
  • The Grouped Mixtures Clustering (GMC) model [6]
  • The Symmetric Grouped Mixtures Clustering (S-GMC) model [4 - 6]. This is referred to as Gaussian latent Dirichlet allocation (G-LDA) in [4, 5].
  • Simultaneous Clustering Model (SCM) for Multinomial Documents, and Gaussian Observations [5, 6].
  • Multiple-source Clustering Model (MCM) for clustering two observations, one of an image/document, and multiple of segments/words simultaneously [4 - 6].
  • And more clustering algorithms based on diagonal Gaussian, and Exponential distributions.

And also,

  • Various functions for evaluating means, standard deviations, covariance, primary Eigenvalues etc of data.
  • Extensible template interfaces for creating new algorithms within the variational Bayes framework.

An example of using the MCM to simultaneously cluster images and objects within images for unsupervised scene understanding. See [4 - 6] for more information.


TABLE OF CONTENTS


DEPENDENCIES

  • Eigen version 3.0 or greater
  • Boost version 1.4.x or greater and devel packages (special math functions)
  • OpenMP, comes default with most compilers (may need a special version of LLVM).
  • CMake

For the python interface:

  • Python 2 or 3
  • Boost python and boost python devel packages (make sure you have version 2 or 3 for the relevant version of python)
  • Numpy (tested with v1.7)

INSTALL INSTRUCTIONS

For Linux and OS X -- I've never tried to build on Windows.

To build libcluster:

  1. Make sure you have CMake installed, and Eigen and Boost preferably in the usual locations:

     /usr/local/include/eigen3/ or /usr/include/eigen3
     /usr/local/include/boost or /usr/include/boost
    
  2. Make a build directory where you checked out the source if it does not already exist, then change into this directory,

     cd {where you checked out the source}
     mkdir build
     cd build
    
  3. To build libcluster, run the following from the build directory:

     cmake ..
     make
     sudo make install
    

    This installs:

     libcluster.h    /usr/local/include
     distributions.h /usr/local/include
     probutils.h     /usr/local/include
     libcluster.*    /usr/local/lib     (* this is either .dylib or .so)
    
  4. Use the doxyfile in {where you checked out the source}/doc to make the documentation with doxygen:

     doxygen Doxyfile
    

NOTE: There are few options you can change using ccmake (or the cmake gui), these include:

  • BUILD_EXHAUST_SPLIT (toggle ON or OFF, default OFF) This uses the exhaustive cluster split heuristic [1, 2] instead of the greedy heuristic [4, 5] for all algorithms but the SCM and MCM. The greedy heuristic is MUCH faster, but does give different results. I have yet to determine whether it is actually worse than the exhaustive method (if it is, it is not by much). The SCM and MCM only use the greedy split heuristic at this stage.

  • BUILD_PYTHON_INTERFACE (toggle ON or OFF, default OFF) Build the python interface. This requires boost python, and also uses row-major storage to be compatible with python.

  • BUILD_USE_PYTHON3 (toggle ON or OFF, default ON) Use python 3 or 2 to build the python interface. Make sure you have the relevant python and boost python libraries installed!

  • CMAKE_INSTALL_PREFIX (default /usr/local) The default prefix for installing the library and binaries.

  • EIGEN_INCLUDE_DIRS (default /usr/include/eigen3) Where to look for the Eigen matrix library.

NOTE: On linux you may have to run sudo ldconfig before the system can find libcluster.so (or just reboot).

NOTE: On Red-Hat based systems, /usr/local/lib is not checked unless added to /etc/ld.so.conf! This may lead to "cannot find libcluster.so" errors.

C++ INTERFACE

All of the interfaces to this library are documented in include/libcluster.h. There are far too many algorithms to go into here, and I strongly recommend looking at the test/ directory for example usage, specifically,

  • cluster_test.cpp for the group mixture models (GMC etc)
  • scluster_test.cpp for the SCM
  • mcluster_test.cpp for the MCM

Here is an example for regular mixture models, such as the BGMM, which simply clusters some test data and prints the resulting posterior parameters to the terminal,

#include "libcluster.h"
#include "distributions.h"
#include "testdata.h"


//
// Namespaces
//

using namespace std;
using namespace Eigen;
using namespace libcluster;
using namespace distributions;


//
// Functions
//

// Main
int main()
{

  // Populate test data from testdata.h
  MatrixXd Xcat;
  vMatrixXd X;
  makeXdata(Xcat, X);

  // Set up the inputs for the BGMM
  Dirichlet weights;
  vector<GaussWish> clusters;
  MatrixXd qZ;

  // Learn the BGMM
  double F = learnBGMM(Xcat, qZ, weights, clusters, PRIORVAL, true);

  // Print the posterior parameters
  cout << endl << "Cluster Weights:" << endl;
  cout << weights.Elogweight().exp().transpose() << endl;

  cout << endl << "Cluster means:" << endl;
  for (vector<GaussWish>::iterator k=clusters.begin(); k < clusters.end(); ++k)
    cout << k->getmean() << endl;

  cout << endl << "Cluster covariances:" << endl;
  for (vector<GaussWish>::iterator k=clusters.begin(); k < clusters.end(); ++k)
    cout << k->getcov() << endl << endl;

  return 0;
}

Note that distributions.h has also been included. In fact, all of the algorithms in libcluster.h are just wrappers over a few key functions in cluster.cpp, scluster.cpp and mcluster.cpp that can take in arbitrary distributions as inputs, and so more algorithms potentially exist than enumerated in libcluster.h. If you want to create different algorithms, or define more cluster distributions (like categorical) have a look at inheriting the WeightDist and ClusterDist base classes in distributions.h. Depending on the distributions you use, you may also have to come up with a way to 'split' clusters. Otherwise you can create an algorithm with a random initial set of clusters like the MCM at the top level, which then variational Bayes will prune.

There are also some generally useful functions included in probutils.h when dealing with mixture models (such as the log-sum-exp trick).

PYTHON INTERFACE

Installation

Easy, follow the normal build instructions up to step (4) (if you haven't already), then from the build directory:

cmake ..
ccmake .

Make sure BUILD_PYTHON_INTERFACE is ON

make
sudo make install

This installs all the same files as step (4), as well as libclusterpy.so to your python staging directory, so it should be on your python path. I.e. just run

import libclusterpy

Trouble Shooting:

On Fedora 20/21 I have to append /usr/local/lib to the file /etc/ld.so.conf to make python find the compiled shared object.

Usage

Import the library as

import numpy as np
import libclusterpy as lc

Then for the mixture models, assuming X is a numpy array where X.shape is (N, D) -- N being the number of samples, and D being the dimension of each sample,

f, qZ, w, mu, cov = lc.learnBGMM(X)

where f is the final free energy value, qZ is a distribution over all of the cluster labels where qZ.shape is (N, K) and K is the number of clusters (each row of qZ sums to 1). Then w, mu and cov the expected posterior cluster parameters (see the documentation for details. Alternatively, tuning the prior argument can be used to change the number of clusters found,

f, qZ, w, mu, cov = lc.learnBGMM(X, prior=0.1)

This interface is common to all of the simple mixture models (i.e. VDP, BGMM etc).

For the group mixture models (GMC, SGMC etc) X is a list of arrays of size (Nj, D) (indexed by j), one for each group/album, X = [X_1, X_2, ...]. The returned qZ and w are also lists of arrays, one for each group, e.g.,

f, qZ, w, mu, cov = lc.learnSGMC(X)

The SCM again has a similar interface to the above models, but now X is a list of lists of arrays, X = [[X_11, X_12, ...], [X_21, X_22, ...], ...]. This specifically for modelling situations where X is a matrix of all of the features of, for example, N_ij segments in image ij in album j.

f, qY, qZ, wi, wij, mu, cov = lc.learnSCM(X)

Where qY is a list of arrays of top-level/image cluster probabilities, qZ is a list of lists of arrays of bottom-level/segment cluster probabilities. wi are the mixture weights (list of arrays) corresponding to the qY labels, and wij are the weights (list of lists of arrays) corresponding the qZ labels. This has two optional prior inputs, and a cluster truncation level (max number of clusters) for the top-level/image clusters,

f, qY, qZ, wi, wij, mu, cov = lc.learnSCM(X, trunc=10, dirprior=1,
                                          gausprior=0.1)

Where dirprior refers to the top-level cluster prior, and gausprior the bottom-level.

Finally, the MCM has a similar interface to the MCM, but with an extra input, W which is of the same format as the X in the GMC-style models, i.e. it is a list of arrays of top-level or image features, W = [W_1, W_2, ...]. The usage is,

f, qY, qZ, wi, wij, mu_t, mu_k, cov_t, cov_k = lc.learnMCM(W, X)

Here mu_t and cov_t are the top-level posterior cluster parameters -- these are both lists of T cluster parameters (T being the number of clusters found. Similarly mu_k and cov_k are lists of K bottom-level posterior cluster parameters. Like the SCM, this has a number of optional inputs,

f, qY, qZ, wi, wij, mu_t, mu_k, cov_t, cov_k = lc.learnMCM(W, X, trunc=10,
                                                           gausprior_t=1,
                                                           gausprior_k=0.1)

Where gausprior_t refers to the top-level cluster prior, and gausprior_k the bottom-level.

Look at the libclusterpy docstrings for more help on usage, and the testapi.py script in the python directory for more usage examples.

NOTE if you get the following message when importing libclusterpy:

ImportError: /lib64/libboost_python.so.1.54.0: undefined symbol: PyClass_Type

Make sure you have boost-python3 installed!

GENERAL USABILITY TIPS

When verbose mode is activated you will get output that looks something like this:

    Learning MODEL X...
    --------<=>
    ---<==>
    --------x<=>
    --------------<====>
    ----<*>
    ---<>
    Finished!
    Number of clusters = 4
    Free Energy = 41225

What this means:

  • - iteration of Variational Bayes (VBE and VBM step)
  • < cluster splitting has started (model selection)
  • = found a valid candidate split
  • > chosen candidate split and testing for inclusion into model
  • x clusters have been deleted because they became devoid of observations
  • * clusters (image/document clusters) that are empty have been removed.

For best clustering results, I have found the following tips may help:

  1. If clustering runs REALLY slowly then it may be because of hyper-threading. OpenMP will by default use as many cores available to it as possible, this includes virtual hyper-threading cores. Unfortunately this may result in large slow-downs, so try only allowing these functions to use a number of threads less than or equal to the number of PHYSICAL cores on your machine.

  2. Garbage in = garbage out. Make sure your assumptions about the data are reasonable for the type of cluster distribution you use. For instance, if
    your observations do not resemble a mixture of Gaussians in feature space, then it may not be appropriate to use Gaussian clusters.

  3. For Gaussian clusters: standardising or whitening your data may help, i.e.

    if X is an NxD matrix of observations you wish to cluster, you may get better results if you use a standardised version of it, X*,

    X_s = C * ( X - mean(X) ) / std(X)
    

    where C is some constant (optional) and the mean and std are for each column of X.

    You may obtain even better results by using PCA or ZCA whitening on X (assuming ZERO MEAN data), using python syntax:

    [U, S, V] = svd(cov(X))
    X_w = X.dot(U).dot(diag(1. / sqrt(diag(S))))   #  PCA Whitening
    

    Such that

    cov(X_w) = I_D.
    

    Also, to get some automatic scaling you can multiply the prior by the PRINCIPAL eigenvector of cov(X) (or cov(X_s), cov(X_w)).

    NOTE: If you use diagonal covariance Gaussians I STRONGLY recommend PCA or ZCA whitening your data first, otherwise you may end up with hundreds of clusters!

  4. For Exponential clusters: Your observations have to be in the range [0, inf). The clustering solution may also be sensitive to the prior. I find usually using a prior value that has the approximate magnitude of your data or more leads to better convergence.


REFERENCES AND CITING

[1] K. Kurihara, M. Welling, and N. Vlassis. Accelerated variational Dirichlet process mixtures, Advances in Neural Information Processing Systems, vol. 19, p. 761, 2007.

[2] D. M. Steinberg, A. Friedman, O. Pizarro, and S. B. Williams. A Bayesian nonparametric approach to clustering data from underwater robotic surveys. In International Symposium on Robotics Research, Flagstaff, AZ, Aug. 2011.

[3] C. M. Bishop. Pattern Recognition and Machine Learning. Cambridge, UK: Springer Science+Business Media, 2006.

[4] D. M. Steinberg, O. Pizarro, S. B. Williams. Synergistic Clustering of Image and Segment Descriptors for Unsupervised Scene Understanding, In International Conference on Computer Vision (ICCV). IEEE, Sydney, NSW, 2013.

[5] D. M. Steinberg, O. Pizarro, S. B. Williams. Hierarchical Bayesian Models for Unsupervised Scene Understanding. Journal of Computer Vision and Image Understanding (CVIU). Elsevier, 2014.

[6] D. M. Steinberg, An Unsupervised Approach to Modelling Visual Data, PhD Thesis, 2013.

Please consider citing the following if you use this code:

  • VDP: [2, 4, 6]
  • BGMM: [5, 6]
  • GMC: [6]
  • SGMC/GLDA: [4, 5, 6]
  • SCM: [5, 6]
  • MCM: [4, 5, 6]

You can find these on my homepage. Thank you!

You can’t perform that action at this time.