UGM: Matlab code for undirected graphical models
Mark Schmidt (2007)
Summary
UGM is a set of Matlab functions implementing various tasks in probabilistic undirected graphical models of discrete data with pairwise (and unary) potentials. Specifically, it implements a variety of methods for the following four tasks:
- Decoding: Computing the most likely configuration.
- Inference: Computing the partition function and marginal probabilities.
- Sampling: Generating samples from the distribution.
- Training: Fitting a model to a given dataset.
The first three tasks are implemented for arbitrary discrete undirected graphical
models with pairwise potentials. The last task focuses on
Markov random fields and conditional random fields with log-linear potentials. The code is written entirely in Matlab, although more
efficient mex versions of many parts of the code are also available.
Documentation and Tutorial on Markov Random Fields and Conditional Random Fields
The documentation for UGM consists of a series of demos, showing how to use UGM to perform various tasks. These demos also contain some tutorial material on undirected graphical models. In Summer 2015 we also did a crash course on UGMs and the material from these courses is available here.
The first set of demos covers exact decoding/inference/sampling:
- Small: An introduction to UGMs and the tasks of decoding/inference/sampling on a a simple UGM where we can do everything by hand. This tutorial also introduces the edgeStruct used by all the codes.
- Chain: A chain-structured UGM, illustrating how the Markov independence properties present in the chain lead to efficient dynamic programming algorithms for decoding/inference/sampling.
- Tree: A tree-structured UGM, where dynamic
programming methods also apply.
- Condition: A demo that shows how we can
do conditional decoding/inference/sampling, if we know the values of some
of the variables.
- Cutset: Two examples of simple loopy UGMs, where we take advantage of the simplified graph structure after conditioning to perform exact decoding/inference/sampling.
- Junction: A more complicated loopy UGM, where we take advantage of the low treewidth of the graph structure to perform exact decoding/inference/sampling.
- GraphCuts: An example of a complicated loopy UGM, where the use of sub-moodular potentials (over binary data) allows us to perform exact decoding.
The second set of demos covers approximate decoding/inference/sampling methods:
- ICM: A demo showing how to use the iterated
conditional mode algorithm (and other local search algorithms) for approximate decoding.
- MCMC: A demo showing how to use Gibbs sampling for approximate sampling, and how to use sampling methods for approximate decoding/inference.
- Variational: A demo showing how to use the variational mean
field approximation for approximate inference, how to apply loopy belief
propagation for approximate inference/decoding, and how to use the (convex)
tree-reweighted belief propagation algorithms for these same tasks.
- Block: A demo showing how to use 'block' versions several
algorithms to impprove their performance. This includes a block ICM method that gives a
decoding satisfying stronger optimality conditions, a block-Gibbs sampler
that has a lower variance, and a block mean field method for inference that
uses a more complex approximating distribution.
- AlphaBeta: A demo showing how to use alpha-beta swaps, alpha-expansiosn, and alpha-expansion beta-shrink moves for approximate decoding in a model with some conditional sub-modular structure.
- Linprog: A demo showing how to use an integer programming formulation of decoding, as well as the linear programming relaxation of this problem.
The third set of demos covers parameter estimation:
- TrainMRF: A demo showing how to train Markov random fields when exact inference is possible. This demo also introduces the nodeMap and edgeMap structures used by all parameter estimation methods.
- TrainCRF: A demo showing how to train conditional random fields when exact inference is possible. This demo also shows how the nodeMap and edgeMap are used in the conditional scenario.
- TrainApprox: A demo showing how to train UGMs with a pseudo-likelihood or variational approximation, for scenarios where exact inference is not tractable.
- TrainSGD: A demo showing how to train conditional random fields with stochastic gradient descent, and with a hybrid deterministic/stochastic L-BFGS method.
Available Methods
Decoding
UGM has a variety of decoding methods using the functions UGM_Decode_*, where * is one of the following: Exact, Chain, Tree, Condition, Junction, GraphCut, ICM, Greedy, ICMrestart, Sample, MaxOfMarginals, LBP, TRBP, Block_ICM, AlphaBetaSwap, AlphaExpansion, AlphaExpansionBetaShrink, IntProg, LinProg.
Inference
UGM has a variety of inference methods using the functions UGM_Infer_*, where * is one of the following: Exact, Chain, Tree, Condition, Cutset, Junction, Sample, ViterbiApx, MeanField, LBP, TRBP, Block_MF.
Sampling
UGM has a variety of sampling methods using the functions UGM_Sample_*, where * is on of the following: Exact, Chain, Tree, Condition, Cutset, Junction, Gibbs, VarMMC, Block_Gibbs.
Training
UGM has a variety of training methods available for fitting Markov random fields and conditional random fields from data. It uses a log-linear parameterization but otherwise is very flexible: the graph structure can be arbitrary, each node can have a different number of states, the parameter can be tied in arbitrary ways, several approximate training methods like pseudo-likelihood and using approximate inference are available, different forms of regularization can be added, associatve constraints on the objective function can be added, features can be real-valued or binary, features can be used on both nodse and edges, and a variety of optimizers are available.
Download
The last fully-documented release of UGM was in 2011. The complete set of files for the 2011 version are available here (for parameter estimation, this package includes the 2009 versions of minFunc and minConf).
The latest version is avialable as part of a larger package here, which includes many additional features that are not included in the 2011 version.
Alternately, see the individual files below which can be added to the 2011 version to add various features. To significantly speed up the decoding methods based on graph cuts (GraphCuts, AlphaBetaSwap, AlphaExpansion, AlphaExpansionBetaShrink), you can install the mex
wrapper to the maxflow
code into a sub-directory of the UGM directory.
To run the demos, in Matlab type:
cd UGM
addpath(genpath(pwd))
example_UGM_*
Where in the above * is the name of one of the demos. For example, to run the Small demo, type example_UGM_Small.
We have included mex files for several operating systems, but if you try to use the mex files on other operating systems you will get errors saying that a file is not found (where the file ends with 'C'). To compile the mex
files for other operating systems, run the included mexAll function
(then e-mail me the mex files so I can add them to the zip file for others to use). On some architectures the mexAll function does not seem to handle the directory structure properly, and in these cases you can compile the mex files by switching to the mex directory and directly using the mex function to compile the files in that directory.
Citations
If you use this software in a publication, please cite the work using the following information:
- M. Schmidt. UGM: A Matlab toolbox for probabilistic undirected graphical models. http://www.cs.ubc.ca/~schmidtm/Software/UGM.html, 2007.
Updates
2020
I haven't been updating this page recently, but note that a newer version of UGM is available as part of a larger code package here. This version notably has some additional features that I haven't documented. These are demonstrated in the following examples from that package:
- example_UGM_QuickStart % A demo that shows how various parts of UGM work
- example_UGM_TrainHidden % Shows how to train CRFs with missing labels
- example_UGM_OCR % Shows how to train CRFs where different examples have different structres
- example_UGM_TrainApprox2 % Approximate training of UGMs (score matching, piecewise, min probability flow, product of marginals, block pseudolikelihood, sub-modular constraints)
- example_UGM_TrainSGD2 % Stochastic gradient training of UGMs (projected, max-margin, contrastive divergence, stochastic maximum likelihood)
2019
Updated the "mexAll.m" function to be compatible with the current versions of Matlab, by adding the "-compatibleArrayDims" flag (thanks to Yingnan Cui and Aglaia Chan).
2015
Here are updates that are not included in the 2011 version of UGM:
2014
Here are updates that are not included in the 2011 version of UGM:
- mexAll_hacked: A variant of mexAll from James Atwood for compiling under octave on OS X.
2013
Here are updates that are not included in the 2011 version of UGM:
- hcrf.zip: Code by Konstantinos Bousmalis that uses UGM for the hidden conditional random field model of Quattoni et al. [PAMI, 2007].
- UGM_getEdges.m: Returns a row vector instead of a column vector to increase code readability when using the result.
- UGM_LoopyBP.m: Fixed the NaN caused by an integer-division by nStates in the Matlab version of the code (thanks to Javier Juan Albarracin). Note that this function requires the new UGM_getEdges.m function above.
- UGM_Junction.zip: Fixed the junction tree methods to allow nodes to have different numbers of states (thanks to Elad Mezuman). Note that this function requires the new UGM_getEdges.m function above.
2012
Here are updates that are not included in the 2011 version of UGM:
- UGM_CRF_NLL_Hidden.m: A variant of UGM_CRF_NLL.m that allows hidden/missing values in Y (though it is quite slow because I haven't written a mex version). Setting Y(i,n)=0 indicates that the value is hidden (thanks to Benjamin Marlin, and especially to Lei Shi).
- CRFcell.zip: The function UGM_CRFcell_NLL.m, as well as example_UGM_OCR.m which is a demo showing how to apply UGM to data sets where different examples have different numbers of nodes and/or different graph structures, which was by far the most requested feature to add to UGM.
- UGM_Infer_TRBPC.c: Fixed an indexing bug in the message-passing.
- UGM_ChainFwd.m: Fixed the error when running Viterbi decoding on a chain with only one node (thanks to Simon Lacoste-Julien).
- UGM_CRF_PseudoNLL.m: Fixed the indexing problem for the non-mex version of this code (thanks to Natraj Raman).
- UGM_makeEdgeStruct.m: Fixed the error ("Undefined function or method 'prod' for input arguments of type 'int32'.") when calling prod with an int32 argument for older versions of Matlab (thanks to Nikolai Lebedev).
- example_UGM_quickStart.m: This function is intended to allow new users to quickly see how the UGM data structures and function calls work. In particular, it shows how to use UGM to perform the tasks of decoding/inference/sampling in a tree-structure graphical model, as well as parameter estimation in both MRFs and CRFs. Note that this function requires the UGM_makeMRFmaps.m and UGM_makeCRFmaps.m functions below.
- UGM_makeMRFmaps.m and UGM_makeCRFmaps.m: These functions construct the nodeMap and edgeMap variables required by the 2011 version of UGM that are equivalent to some of the special cases provided by the infoStruct variable used in earlier version of UGM. For example, if you set tied = 1 and ising = 1, then parameters will be shared across nodes and an Ising parameterization will be used. In contrast, setting tied = 0 and ising = 0 will make each node have its own parameters and will use a full parameterization of the node and edge potentials.
- UGM_Sample_Junction.m: Fixed a major bug in the junction tree sampler (made obsolete by the 2013 update to all junction tree codes).
2011
This is a major update to UGM. ALL mex files must be re-compiled to use this new version. In addition, parameter estimation now works in a completely different way to allow arbitrary parameter tying (there is no more infoStruct).
Below is the list of updates:
- Replaced the infoStruct with the nodeMap and edgeMap. These allow arbitrary parameter tying and make it easier to have different graph structures for different training examples.
- Added the alpha-expansion and alpha-expansion beta-shrink moves for approximate decoding in models satisfying a generalized triangle inequality, as well as the truncation trick to allow these methods to be applied when this inequality is not satisfied.
- Added a rudimentary implementation of junction trees for decoding/inference/sampling in models with low treewidth.
- The belief propagation code for tree-structured models now uses a proper message-passing schedule.
- The methods based on graph cuts can now be made significantly faster, since they will call the mex
wrapper to the maxflow code if it is found on the Matlab path.
- The graph construction in UGM_Decode_GraphCut was fixed for cases where the edge potentials are asymmetric (thanks to Mohamed Ghalwash).
- The max-product loopy belief propagation code now uses a mex file to speed up the computation (thanks to Hanwang Zhang).
- The tree-reweighted belief propagation codes now use mex files to speed up the computation.
- Added simulated annealing.
- A memory leak was fixed in the max_mult function, and a Matlab version of the function was added (thanks to Uday Kurkure).
- The minSpan function now does something sensible for disconnected graphs.
- The adjacency matrix construction for non-square lattice structures was fixed in the demos (thanks to Calden Wloka and Xuba Zhang).
- The UGM_Decode_ICMrestart function no longer ignores the number of restarts argument (thanks to Hanwang Zhang).
- The code no longer uses repmatC (the performance of repmat has been improved in recent versions of Matlab).
- The UGM_makeEdgeVE function now includes some better documentation (thanks to Adam Nitzan).
- Added the win32 mex files (thanks to Rajnish Kumar Yadav).
- Added the win64 mex files (thanks to Ka-Chun Wong).
- Thanks also to Dana Cobzas, Neil Birkbeck, and Jana Kosecka for other contributions that did not make it into the new version.
2009
Although the first line of code for UGM was written in 2007 and I included parts of it in previous packages (e.g. examples of using minFunc, examples of using L1General, Gsparse, and UGMlearn), the first complete and stand-alone version of UGM was released in 2009. Note that parameter estimation in this version works in a completely different way than the current version (while mex files from this older version may be incompatible with the newer version). This original version can still be downloaded here
Debugging Mex Files
UGM makes use of mex files (C functions that Matlab can call) in order to speed up certain computations. Unlike calling a Matlab function where an error will lead to a graceful exact and a trace of where the error occured, errors in mex files can lead to segmentation faults. But, all mex files in UGM have a corresponding Matlab equivalent that (while slower) should perform the exact same computation. This gives a readable version of the mex file that can also be used for debugging. Normally, the Matlab code is ignored and the mex code is used, but you can force the Matlab code to be used and the mex code to be ignored by setting edgeStruct.useMex = 0. So, if UGM gives you a segmentation fault and you cannot find the source of the error, I would recommend re-running the code with edgeStruct.useMex = 0 in order to see why the error is arising.
UGM in Publications
I have used UGM in a few publications:
- Structure Learning in
Random Fields for Heart Motion Abnormality Detection. M. Schmidt, K. Murphy, G. Fung, R. Rosales. CVPR'08.
- Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected
Quasi-Newton Algorithm. M. Schmidt, E. van den Berg, M. Friedlander, K. Murphy. AISTATS'09.
- Increased Discrimination in Level Set Methods with Embedded Conditional Random Fields. D. Cobzas, M. Schmidt. CVPR'09.
- Modeling Discrete Interventional Data using Directed Cyclic Graphical Models. M. Schmidt, K. Murphy. UAI'09.
- Causal Learning without DAGs. D. Duvenaud, D. Eaton, K. Murphy, M. Schmidt. JMLR W&CP'10
- Generalized
Fast Approximate Energy Minimization via Graph Cuts: Alpha-Expansion
Beta-Shrink Moves. M. Schmidt, K. Alahari. UAI'11.
- Hybrid Deterministic-Stochastic Methods for Data Fitting. M. Friedlander, M. Schmidt, SISC'12.
To reference UGM in a publication, please include my name and a link to this website. You may also want to include the date, since I may update the software in the future.
If you have made a modification of UGM or added extra functionality (i.e. an alternate decoding method), please send it to me and I can include it here for others to use.
Mark Schmidt > Software > UGM