text:
# User Guide Bayesian

Bayesian
Network Inference
with
Java
Objects, Version 2.2
User Guide
By Jürgen Sladeczek, Alexander J.
Hartemink, and Joshua Robinson
Banjo is licensed from Duke University.
Copyright © 2005-2008 by Alexander J. Hartemink.
==================
Licensing Overview
==================
You may license this software either under the non-commercial use license shown below or
under a specially-negotiated non-exclusive commercial use license. You may choose which
type of license is more appropriate for your needs. For strictly non-commercial use of the
software, you may prefer to license the software under the non-commercial use license below.
The term 'commercial use' is defined broadly: if the software is used for commercial gain or to
further any commercial purpose, a commercial use license is required. If you have any
question about whether your use would be considered commercial, or if you would like to
2008 are:
Alexander J. Hartemink, Ph.D.
Assistant Professor
Department of Computer Science
Duke University
Box 90129
Durham, NC 27708-0129
[email protected]
Henry Berger, Ph.D.
Licensing Director for Pratt School of Engineering
Office of Licensing & Ventures
Duke University
Box 90083
Durham, NC 27708-0083
[email protected]
====================================
Non-Commercial Use License Agreement
====================================
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Contents
What is New in Version 2 ......................................................................................................................................1
Introduction ...............................................................................................................................................................5
Getting Started .........................................................................................................................................................6
Requirements ........................................................................................................................................................6
Quick Start ............................................................................................................................................................7
Using Your Own Data.........................................................................................................................................8
Searching Using Multiple Threads .............................................................................................................8
Searching Using a Compute Cluster .........................................................................................................9
Supported Data Formats .............................................................................................................................10
Sample Output and Screenshots..................................................................................................................11
Example: Searching for the “Best” Static Bayesian Network ..........................................................11
Example: Searching for the “Best” Dynamic Bayesian Network ....................................................16
Using Banjo .............................................................................................................................................................23
The Banjo Application ......................................................................................................................................23
The Banjo Components ...................................................................................................................................24
The Searchers .................................................................................................................................................24
The Proposers .................................................................................................................................................25
The Cycle Checkers .......................................................................................................................................25
The Evaluators................................................................................................................................................25
The Deciders ....................................................................................................................................................26
The Equivalence Checkers ..........................................................................................................................26
Summary of Component Options .................................................................................................................27
Setting up a Banjo Search ..............................................................................................................................28
Tuning the Memory Use ..............................................................................................................................28
Performance Tuning ......................................................................................................................................29
Input Discretization Options ......................................................................................................................30
Experimenting with Banjo ..............................................................................................................................32
Example: Choice of Discretization............................................................................................................32
Example: Combinations of useCache, MaxParentCount, and precomputeLogGamma..........33
Example: Varying the Cache Level in an Intermediate-size Problem ...........................................35
Example: Varying the Cache Level in a Large-size Problem ............................................................35
Example: Comparing Searchers................................................................................................................36
Example: The Effects of Equivalence Checking on Performance ...................................................37
Example: Comparing different Cycle Checking Methods..................................................................37
Example: Cycle Checking Methods, Revisited ......................................................................................38
Post-Processing Options ..............................................................................................................................39
Using dot to Generate a Graph Representing the Found Network ................................................39
Influence Scores .............................................................................................................................................42
Consensus Graph ..........................................................................................................................................43
Finding Non-equivalent Networks ............................................................................................................45
Using Banjo in Matlab .....................................................................................................................................47
Hints and Tips ....................................................................................................................................................49
Computing a Network Score without Running a Search ..................................................................49
Displaying Debug Info ..................................................................................................................................49
Adding Structure Files to Output .............................................................................................................49
Using Time Stamps in Output Files ........................................................................................................49
Memory Info and Performance Tuning ...................................................................................................49
Accessing Additional Options via Internal Code Changes ................................................................50
Unique Output File Names .........................................................................................................................50
Combining Multiple Observations Files .................................................................................................51
Error Reporting to File .................................................................................................................................51
More Flexible Structure Files .....................................................................................................................51
Specifying Observations in Row or Column Format...........................................................................51
Specifying Names for the Variables .........................................................................................................52
Using a Greedy Searcher with the AllLocalMoves Proposer.............................................................52
Troubleshooting .....................................................................................................................................................53
When Little Things Don’t Work As Expected ............................................................................................53
Missing or Invalid Parameter in the Settings File ...............................................................................53
Miscellaneous Errors and Warnings .......................................................................................................55
Problems During Post-Processing ............................................................................................................55
Error During Program Execution: Handled by Banjo ........................................................................55
Error During Program Execution: “Insufficiently Handled” by Banjo ..........................................56
Running Out of Memory ..................................................................................................................................57
Parameters Affecting Memory Use ...........................................................................................................57
The First Thing to Check .............................................................................................................................57
What if Banjo still Runs out of Memory? ...............................................................................................58
What if Banjo Runs out of Memory well into a Search?....................................................................59
Crash with “System” Error Message ............................................................................................................60
Submitting an Error Report ...........................................................................................................................61
Appendix A ...............................................................................................................................................................62
File Formats.........................................................................................................................................................62
Example of a Minimal Settings File .........................................................................................................62
Example of a Comprehensive Settings File ...........................................................................................63
Observations File Example .........................................................................................................................65
Structure File: Static Bayesian Network ................................................................................................65
Structure File: Dynamic Bayesian Network ..........................................................................................66
Results Output in XML Format.................................................................................................................67
Appendix B ..............................................................................................................................................................69
Settings File: Parameter Names and Values .............................................................................................69
Appendix C ..............................................................................................................................................................79
References ............................................................................................................................................................79
Papers ................................................................................................................................................................79
Books and Manuals ......................................................................................................................................79
Appendix D ..............................................................................................................................................................80
Project Background and Acknowledgements ...........................................................................................80
Index ..........................................................................................................................................................................81
What is New in Version 2
For the release of Banjo Version 2, we focused our development efforts on four areas:
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We dramatically reduced the memory requirements for running Banjo, which in turn
resulted in significantly improved search performance, by as much as an order of
magnitude for problems with thousands of variables.
We added a number of new features, such as automatic dot graphics file creation,
consensus graph computation, and equivalence checking for the computation of the n-best
graphs.
We improved the overall ease-of-use with more convenient input and output handling, as
well as widely expanded control over various facets of the application’s execution.
We changed the underlying framework, especially in the handling of settings, their
validation, and error reporting, to make the infrastructure part of development easier, more
consistent, and more efficient.
In more detail, here is a complete list of improvements.
Performance:
•
•
For large problems with several thousands of variables, the memory required for executing
a search is reduced by more than 10-fold, and the newfound ability to use Banjo’s
extended caching options for such problems reduces search times by a similar factor.
Search performance for small problems (with less than 100 variables) is up to twice as fast
as Banjo 1.0.
An improved cycle checking implementation with optional optimization based on a paper by
Oded Shmueli further increases the search speed.
New features:
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Consensus graph for n-best networks.
Equivalence comparison for n-best networks (with statistics). When executing a search, the
tracking of the top-scoring networks can be done using 3 different settings: “nonIdentical”
works the way Banjo 1.0 did, namely without any equivalence checking;
“nonIdenticalThenPruned” prunes the set of non-identical networks down to a set of nonequivalent ones, but only after the search is completed; and “nonEquivalent” collects only
non-equivalent networks as it maintains its list of top-scoring networks during the search.
Output of dot files (top and consensus graphs), and automatic execution of dot to generate
graphics files (in user-specified formats).
Stopping criteria can be based on any combination of time, networks, and restarts. The
first criterion to be satisfied will cause the search to stop.
User can specify labels for the variables. These labels will be used for dot output.
Additional functionality is now under user control via the settings file, with default values
for unspecified settings. This includes various post-processing options such as
createDotOutput, computeInfluenceScores, and computeConsensusGraph. If the
fullPathToDotExecutable setting is correctly specified, then Banjo will automatically launch
the dot application after the search to create the graphic representation of the obtained
network (the top scoring and/or the consensus one). The output of the data report, and the
p. 1
•
•
•
•
•
display of specified structures (namely, for the initial, must-be-present, and must-not-bepresent parents) can now also be controlled via settings.
Input of time values can now we done in 3 valid ways: a single number (interpreted as
seconds), a single number with a time qualifier (e.g., h for hour), or in the colon-delimited
format that we used in the past. Note that the colon-delimited format does not require
“leading zeros” any more (e.g., 10:00 is equivalent to 0:0:10:00, or to 10 m, or to 600, all
resulting in 10 minutes of search time).
The feedback of results has been streamlined via the new fileReportingInterval and
screenReportingInterval settings, which are being specified as time values. The feedback
display itself is more uniform and informative.
For keeping results organized, a time stamp can be embedded in the names of output files.
Users with large data sets may appreciate the settings-based control over the internal
caching mechanism, as well as the optional memory usage information. Should Banjo run
out of memory, it now exits gracefully with a final report on memory use and a listing of the
results obtained before running out of memory.
And when things don’t go as expected, a new setting tells Banjo to display the stack trace
in run time mode, to provide the maximum amount of information in case of an internal
problem or bug.
Ease-of-use improvements:
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Optional settings can either be omitted entirely from the settings file, or can have their
values omitted.
A number of optional “settings” that used to be controlled via internal constants can now
be configured via the settings file.
The spelling of setting names in the settings file is now case-independent.
Observations and structures can be supplied using arbitrary white-space, or using different
delimiters (‘;:’ are implemented); even more generalization can be applied by setting a
custom pattern in the global setup.
Observation and structure files can include descriptive comments (any text that follows a #
in any text line is now being ignored).
Validation is performed as much as possible in a single pass (i.e., as long as no “showstopper” error is encountered, errors are collected and then reported all at once, instead of
one error at a time). In addition, non-fatal errors are collected separately, and displayed at
the end of the feedback section for the search.
When a network that is provided to Banjo (i.e., an initial or mandatory structure) contains
a cycle, the resulting error message will name a node in the cycle, making it easier for the
user to correct the input.
Development-related changes:
•
•
The handling of settings and validations has changed substantially. As the number of
options for controlling Banjo’s behavior grew, the centralized management of its options
would be virtually impossible to maintain due to the increasingly complex logic required.
Thus, settings and their validation are now handled within the object that uses a particular
setting. This makes intuitive sense, since each object can be assumed to know what
conditions it needs to impose on a setting’s data type and value. As a benefit of this
approach any setting that is shared between multiple objects can now be validated by any
number or all of such objects, each imposing its own set of restrictions.
Hand in hand with our new validation handling, we added a more granular tracking of the
results (i.e., errors and warnings) of the validation. Instead of immediately throwing an
error when an unacceptable input value is encountered, we now try to record as many
errors as we can within our validation pass, so that we can provide more comprehensive
p. 2
•
feedback in a single message. Of course, there are instances of dependencies between
settings, where we cannot continue once we encounter a “show stopper” error.
If you have used Banjo 1.0, and have made changes to the source code, please review the
Banjo Developer Guide for a more detailed discussion of the changes.
Of course, Banjo Version 2 contains the corrections for the (four) bugs from the Version 1.0.x
maintenance releases.
Finally, we updated the user guide to reflect the changes that went into Banjo Version 2. Based
on direct and indirect user feedback, there is now an extensive “Experimenting with Banjo”
section to help the users navigate the at times tricky trade-off between performance and
memory use based on the multitude of settings that Banjo affords for steering the underlying
search process.
New Features added in Version 2.1
In December 2007, Banjo Version 2.1 has added parallel execution capabilities to the
application: based on the value of the threads setting, Banjo will now execute multiple
searches in parallel on a multi-processor machine. By design the multiple threads share a
single copy of the observations, and – if used – the fast cache and the pre-computed logGamma values. Also by design, the “regular” cache is not shared between the threads based on
the heuristic that each thread may visit very different regions of the search space.
When running a search with the n-best networks settings greater than 1, each of the threads
will maintain its own set of n-best networks. After completion of the search these sets of
networks are then combined by the thread “controller” into a single, combined results set of nbest networks.
While executing the parallel search we save each thread’s results to individual files, specified
by a prefix to the results file name. The final combined results are then saved to the single,
originally specified results file. Note that tracked data such as intermediate results and search
statistics are written (only) to the individual files. On the other hand, only the combined results
file contains the n-best networks and any post-processing results.
New Features added in Version 2.2
In April 2008, Banjo Version 2.2 has added capabilities for convenient search execution in a
cluster environment. The conduit for this feature is the introduction of a (optional) results file
in XML format, together with Banjo’s new functions for reading and combining a set of such
results files into a single results file.
When executing a cluster-based search, one simply collects the produced output files (in the
newly introduced XML format), then executes Banjo while pointing it to the directory with the
results files: As a final output, Banjo will produce a single output file with the combined n-best
networks from all the searches.
As part of the implementation of the new features, new classes for handling the XML
processing as well as for handling the sets of n-best networks have been added in the utilities
package. In addition, the code for handling wildcard file processing has been extracted as a
class of its own. (For details please refer to the Banjo Developer Guide)
As an aide to testing and error tracking, we added a new (optional) setting called
seedForStartingSearch, which can be used to generate repeatable random number sequences.
p. 3
Finally, the error handling and error message feedback has received an overhaul: the error
message is now includes in the regular report file; the stack trace is included in the error
message by default; and the seed number is included to make it easier to reproduce a
scenario.
p. 4
Introduction
Banjo is a software application and framework for structure learning of static and dynamic
Bayesian networks, developed under the direction of Alexander J. Hartemink in the
Department of Computer Science at Duke University. Banjo was designed from the ground up
to provide the performance for analyzing large, research oriented data sets, while at the same
time being accessible enough for students and researchers to explore and experiment with the
algorithms. Because it is implemented in Java, the framework is both powerful and easy to
maintain and extend.
Banjo focuses on structure inference methods; for inference within a Bayesian network of
known structure, a plethora of existing code and applications are available. Banjo currently
performs structure inference for static and dynamic Bayesian networks using the Bayesian
Dirichlet (BDe) scoring metric for discrete variables; available search strategies include
simulated annealing and greedy, paired with evaluation of a single random local move or all
local moves at each step. A search algorithm in Banjo consists of a set of individual core
components:
•
•
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•
Proposing a new network (or networks), handled by a “proposer” component,
Checking the proposed network(s) for cycles, handled by a “cycle checker” component,
Computing the score(s) of the proposed network(s), handled by an “evaluator”
component, and
Deciding whether to accept a proposed network, handled by a “decider” component.
These core components are organized and implemented in such a way that they can be used to
study or extend the search algorithms themselves: a set of (easily expandable) statistics is
provided for monitoring the actual search process.
The core algorithms assume and have been optimized for discrete variables, but if some of your
variables are continuous, the current version of Banjo provides simple discretization
functionality using either quantile or interval discretization methods. Any number of highest
scoring networks can be retained in the search, and the highest can be processed by Banjo to
compute influence scores on the edges, or to generate a file formatted for rendering with dot, a
graph layout visualization tool from AT&T.
This Banjo User Guide explains how to install and run Banjo, the parameters that the
program requires, the names and formats of the data files that it uses, and how to put all the
different pieces together to flexibly use Banjo with your own data. It is organized into the
following main sections:
•
•
•
•
The Getting Started section provides instructions on how to obtain and set up the Java
executable and the source code, and how to execute the code on your computer.
The Using Banjo section describes the high-level organization of the application, with a
brief explanation of Banjo’s core components.
The Troubleshooting section describes the types of issues that a user might encounter
when running the Banjo code, and how to try to resolve them.
Finally, appendices provide examples of the file formats that Banjo uses, an annotated
list of available parameter settings, and examples of error messages.
p. 5
Getting Started
The installation instructions for Banjo are the same whether you simply want to use the code,
(i.e., run the application with your own data), or whether you also intend to modify the actual
source code to create your own applications. The sections below describe the details for
Requirements
The main prerequisite for using Banjo on a particular computer or operating system is the
availability of a Java virtual machine (JVM). The code has been written to comply with Java 2,
using the 1.4.2 or later release of the Java SDK.
We have successfully run Banjo on PCs running Mac OS X, Red Hat Linux, Sun Solaris, and
Windows (98, 2000, and XP), with available memory of at least 256 MB. If your system does
not have a JVM installed, you can download one for free from http://www.java.com (the Java
Runtime Environment) or http://java.sun.com (the Java SDK, needed only if you intend to
make changes to the Banjo code).
The amount of the memory available to the JVM will determine the size of the data set that
Banjo can handle. In Banjo 2 the memory requirements are substantially reduced, so that a
data set with 3000 variables, 2000 observations, and a fastCache setting of level 1 (i.e., all 0
and 1 parent configurations are cached in a special, non-hashed, cache) can now be tackled in
about 500 MB of memory. If you intend to tackle large data sets, you may want to take a look
at the section on memory management via the cache settings.
Any Java development environment, from simple text editor to full-featured IDE, can be used
to modify the Banjo code. We use the open source Eclipse IDE (http://www.eclipse.org) to
develop Banjo. The Banjo Developer Guide features a brief tutorial on how to modify Banjo
within the Eclipse IDE, version 3.0. We are currently using version 3.1.x of Eclipse, which
sports some interface and functional enhancements, but the setup instructions remain
essentially the same.
Banjo is available for download as a Java Archive file (banjo.jar) in a larger zip file
(banjo.zip). As long as you have the required version of Java installed, you will be able to run
Banjo from the command line without any need for compiling. The zip file also contains Banjo
source files, documentation, license information, and two examples for performing Bayesian
network structure inference. We recommend that you extract banjo.zip in a way that
preserves the directory structure within the zip file. This way you can test Banjo without any
additional configuration. The following is a manifest of the files within banjo.zip:
•
a welcome, a basic description of the files, and information on how to
test your whether your installation was successful
p. 6
•
an overview of how Banjo may be licensed, along with the full text of
carefully before proceeding to use Banjo
•
banjo.jar
a Java archive file containing all the compiled Banjo code, which you
can use to run Banjo
•
data/release2.0
a directory structure containing two subdirectories with settings
reflecting the changes in Version 2. One directory contains an
example settings file and data for learning static Bayesian networks;
the other similarly for learning dynamic Bayesian networks; these
will help you understand how Banjo works (see below for information
on how to run these two examples)
•
data/release1.0
a directory structure containing two subdirectories as distributed in
version 1, one with an example settings file and data for learning
static Bayesian networks; the other similarly for learning dynamic
Bayesian networks; these will help you understand how Banjo works
(see below for information on how to run these two examples)
•
doc/
a directory with documentation describing Banjo; within this folder
are PDF versions of the Banjo User Guide, the Banjo Developer
Guide, and a Javadoc directory containing a description of the Banjo
class APIs in browseable HTML
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src/
a directory containing Banjo’s full Java source tree
•
template.txt
a template that can be filled in to create a settings file for using Banjo
with your own data
Quick Start
For the following, we assume that you have a working Java VM installed and banjo.zip
downloaded and unzipped. Here are the steps for “test-driving” the Banjo software:
• Open a command shell, and change directories to the directory where you expanded
banjo.zip (and that now contains banjo.jar).
• To run the first test, type
java -jar banjo.jar settingsFile=data/release2.0/static/static.settings.txt
in your command shell, and press Enter. If your environment is set up properly, the
Banjo application will start by printing out the parameters, progress, and then final
results of the search, which is using simulated annealing to find a high scoring static
Bayesian network (this is set to search for 1 minute).
•
To run the second test, and to demonstrate an alternate syntax for specifying the
location of the settings file, type
java -jar banjo.jar settingsFile=dynamic.settings.txt settingsDirectory=data/release2.0/dynamic
p. 7
•
To use the Banjo application with your own data, edit the settings template file called
template.txt. Unlike the settings files in the previous two tests, this template file
does not contain enough information to specify a valid search, so to make it work,
appropriate values of the settings to describe your own data must be inserted. If you
choose to save your settings file with the name banjo.txt and place it in the same
directory as banjo.jar, you can then run Banjo with a shorter command line:
java -jar banjo.jar
By the way, Banjo lets you override any parameter from the settings file by appending it to the
command line in the form item=value. A detailed description and additional information
about parameter use in the settings file and from the command line is provided below. Feel
free to experiment with different parameter combinations to discover their effects on the
runtime performance of a particular algorithm.
Using Your Own Data
After your successful test drive of Banjo, you may want to try Banjo with your own data. Here
are the steps to accomplish just that.
To prepare your data, you’ll need a settings file that describes your data and how you want the
inference to proceed, i.e., the number of variables, your choice of search algorithm, etc. You’ll
also need an observations file in a very basic format: Each row contains a single observation,
with one entry for the value of each variable delimited by a tab between the values. Let’s say
that you saved your settings in my.settings.txt and saved your data as my.data.txt; the
observationsFile setting in my.settings.txt should list my.data.txt as its value (select
values for the remaining settings as you desire).
You can then run Banjo on your data with
java -jar banjo.jar settingsFile=my.settings.txt
You may want to save your data and associated settings file in a custom directory structure
that keeps your data sets organized for easy later retrieval. Simply set the parameters for the
file and directory information in the settings file to point to your structure.
Searching Using Multiple Threads
In Banjo version 2.1 we added support for executing our code on multi-processor equipped
hardware. Simply add the threads=N setting with an integer value, and Banjo will
automatically spawn N search threads.
java -jar banjo.jar settingsFile=my.settings.txt threads=15
All threads will use the same parameters as specified in the settings file. After all search
threads have completed their execution, Banjo will combine the obtained results and provide
them in a single output file.
p. 8
Note that you want to match the number of threads to be used closely with the number of
processors available. In some basic experiments we found that on a 16-core SUN machine we
obtained better performance (as defined as the combined number of networks visited by all the
threads) when selecting threads=15 than when matching the thread count to the number of
CPUs. This is likely attributable to the role of the scheduler/controller thread that is being run
by the OS in the background.
On commodity multi-core hardware that is becoming more and more standard for today’s
desktops, we found a performance increase of 1.8-1.9 fold (for dual core) to 3.2 fold (for quad
core) as compared to a single thread. In the extreme case of our 16-processor hardware the
performance increase was around 13 fold when we specified threads=15, compared to about 11
fold when we specified threads=16.
Searching Using a Compute Cluster
While each compute cluster may be set up a little different, the basic use is the same: one
submits a (large) “batch” of jobs -- in our case Banjo searches -- to a queue for distributed
execution, and “harvests” the results after the last job has completed its execution.
Previous versions of Banjo could already be used to execute distributed searches on a compute
cluster as long as the target machines were supporting Java. Of course, the challenge that
remained was how to efficiently manage the dozens if not hundreds of possible results files to
combine them into a single, combined results file. So we added some basic features to Banjo
version 2.2 to first export the search results in machine-readable, namely XML-based, format,
as well as an easy way to “harvest” a large number of results files into a single combined
results set.
For easy reference we provide a sample of the XML format used by Banjo in Appendix A.
The (optional) newly introduced settings for using a distributed search are XMLreportFile and
XMLoutputDirectory, for specifying the name and location of the results file with the XML
formatted data, and the XMLsettingsToExport setting, for listing those settings that we want to
have recorded as part of the XML output.
reportFile =
[email protected]@.txt
XMLreportFile =
XMLoutputDirectory =
XMLsettingsToExport =
[email protected]@.xml
data/static/output/xml
all
XMLinputFiles =
XMLinputDirectory =
*.xml
data/static/input/xml
Currently the output to the XML files is limited to the core search information, namely the
network and its score, and the settings related to the search. We do not export any of the
search statistics to XML; the original report file is the only file that will contain such
information.
It is possible to run multi-threaded searches in cunjunction with a compute cluster approach.
When we execute a multi-threaded search on any single machine, the results from those
threads will be combined first into the overall results for that particular machine before the
XML formatted data is written to file.
p. 9
Once we have obtained a set of xml files from a set of searches, we run Banjo one more time,
but now with the settings XMLinputFiles and XMLinputDirectory specified; in XMLinputFiles
we list the names of the XML files that we want to combine into a single results set.
When specifying the XMLinputFiles, we can use the Banjo wildcard file-naming conventions,
including the “—“ prefix for files to be excluded from processing.
Any non-empty specification of the XMLinputFiles settings indicates to Banjo that during this
execution, no search is to be performed, but instead the XML input files are to be combined
into a single results set of n-best networks.
The final combined output based on the listed XML files will be written out to a text file (named
based on the reportFile setting), with a brief header and the list of the combined n-best
networks with their scores, as well as the name of the XML file that provided the network.
----------------------------------------------------------------------------- Banjo
Bayesian Network Inference with Java Objects - Release 2.2.0
15 Apr 2008 - Licensed from Duke University
- Copyright (c) 2005-08 by Alexander J. Hartemink
----------------------------------------------------------------------------- Project:
banjo static example
- User:
demo
- Dataset:
33-vars-320-observations
- Notes:
cluster results
----------------------------------------------------------------------------Combined results from the 8 supplied XML files:
Original file: (path)\static.2008.06.06.12.59.41.xml
Network #1 of 10
Score: -8503.5789
33
0 2 25 30
1 2 4 19
2 1 17
…
32 1 13
Original file: (path)\static.2008.06.06.12.59.01.xml
Network #2 of 10
Score: -8504.7225
33
0 2 5 25
1 1 17
2 1 17
…
32 1 13
Supported Data Formats
In Banjo 1.0.x we only supported a single format for the observation data: each row had to
contain a single observation, listing the values of the variables delimited by spaces.
Banjo 2 also supports the use of observations that are column oriented, i.e., each observation
is supplied as a single column, by specifying the optional variablesAreInRows setting as “yes”.
In addition, users can now also specify the names of the variables, using the variableNames
Setting. By default Banjo expects a white-space separated list of strings of exactly as many
items as the specified variable count. Alternatively, one can prefix the list by “commas:” and
then provide a comma-separated list.
p. 10
For observations with variables in columns (the original Banjo format) the variable names can
also be entered on the very first data line (i.e., the first line in the settings file that is not blank,
and doesn’t start with a #-symbol) directly within the file, by setting variableNames to “inFile” .
Note: When specified in this way, the variable names need to be in the form of a white-space
separated list.
The import of observation data in Banjo 2 has changed towards more flexibility, by using an
implicit data mapping of integer data towards the 0..maxValueCount range of all supplied data
values. This implies that as long as the observation data is in integer format, Banjo 2 always
eliminates any gaps in the range of observations entries, as shown in the following example.
0
1
2
|
|
|
none
none
none
|
|
|
2.0
2.0
2.0
|
|
|
8.0
8.0
8.0
|
|
|
2
2
2
|
|
|
2
2
2
|
|
|
counts: {1.0=46, 4.0=6}
counts: {1.0=2, 4.0=50}
counts: {1.0=2, 4.0=50}
In this example the only supplied observation values for each of the 3 variables were 1 and 4.
Without the need for the user to select any discretization, the data import code will internally
map 1 to 0, and 4 to 1. Note that this mapping is desirable in view of computations for the
used BDe metric, which at one point utilizes the number of assumed values for each of the
variables. If we didn’t specify any discretization, then Banjo 1.0.x would have assumed a range
of values from 0 to 4 for each of the variables, and thus 5 would have been the “maximum
value count” in the score computation. Not surprisingly this results in different scores
compared to using the correct value count of 2 (because there are only 2 values, namely 1 and
4).
By providing the implicit data mapping we take some burden off the user when supplying the
observation data – and eliminate a potential reason of getting unexpected results that can be
quite difficult to reconcile with expectations.
Sample Output and Screenshots
In the examples below, the output was taken directly from the Banjo application and can be
viewed as simulated screenshots obtained by running Banjo in a console or terminal window
on a workstation.
Example: Searching for the “Best” Static Bayesian Network
The underlying problem for this example is a static Bayesian network with 33 variables and
320 observations. Say that we slightly modified the provided settings file to permit a longer
run (of 1 hour) and made a few other changes, saving them as static.settings.long.txt.
Then we execute Banjo by typing:
java -jar banjo.jar settingsFile=data/release2.0/static/static.settings.long.txt
The application will provide immediate feedback on the settings that were supplied. In Banjo 2
the feedback is organized into multiple sections, separated by dividing lines:
• The general header info for tracking the version of Banjo being used.
• The project information, which includes the four free-form settings ‘project’, ‘user’, ‘data
set’, and ‘notes’.
• The settings file from where Banjo loads its parameters.
p. 11
•
•
•
•
•
•
•
The values of the general parameters that set up a search, including the names and
locations of input directory and observations file, and the optional discretization policy for
the supplied data
The optional structure files for specifying a initial structure, the must-be-present and the
must-not-be-present parents, as well as the Markov lags, maximum parent count for each
variable, and the equivalent sample size for the Dirichlet parameter prior.
The core components that define the Search strategy, i.e., the searcher, proposer,
evaluator, and decider objects.
The main runtime performance settings, controlling the cache and the pre-computed array
of log-Gamma values.
The parameters that defines the specific searcher. In our case, Simulated Annealing is
governed by the values for the initial temperature, the cooling factor, the reannealing
temperature, the max. number of accepted networks before cooling, the max. number of
proposed networks before cooling, and the min. number of accepted networks before
reannealing.
The output related information, which includes the output directory, the results file name,
the number of high-scoring networks tracked, the stopping criteria (in terms of time,
number of networks, or number of restarts), and the limit on iterations of the inner search
loop (i.e., the number of search iterations to be executed without checking the stopping
criteria). It also includes the intervals that feedback information id written to the screen
(i.e., the console) and to the result file.
The parameters for using the post-processing functions, which include the computation of
influence scores, automatic dot output to text file as well as in various graphics formats,
and the computation of a consensus graph.
As sample output is shown here:
----------------------------------------------------------------------------- Banjo
Bayesian Network Inference with Java Objects - Release 2.0
1 Apr 2007 - Licensed from Duke University
- Copyright (c) 2005-2007 by Alexander J. Hartemink
----------------------------------------------------------------------------- Project:
banjo static example
- User:
demo
- Dataset:
33-vars-320-observations
- Notes:
static bayesian network inference
----------------------------------------------------------------------------- Settings file:
data/static/static.settings.txt
----------------------------------------------------------------------------- Input directory:
data/static/input
- Observations file:
static.data.txt
- Number of observations:
320
- Number of variables:
33
- Discretization policy:
none
- Exceptions to the discretization policy:
none
----------------------------------------------------------------------------- Initial structure file:
- 'Must be present' edges file:
static.mandatory.str
- 'Must not be present' edges file:
- Min. Markov lag:
0
- Max. Markov lag:
0
- Max. parent count:
5
- Equivalent sample size for Dirichlet parameter prior:
1.0
----------------------------------------------------------------------------- Searcher:
SimAnneal
- Proposer:
ProposerRandomLocalMove
- Evaluator:
defaulted to EvaluatorBDe
- Cycle checker:
CycleCheckerDFS
- Decider:
defaulted to DeciderMetropolis
-----------------------------------------------------------------------------
p. 12
- Pre-compute logGamma:
yes
- Cache:
fastLevel2
----------------------------------------------------------------------------- Initial temperature:
1000
- Cooling factor:
0.7
- Reannealing temperature:
800
- Max. accepted networks before cooling:
2500
- Max. proposed networks before cooling:
10000
- Min. accepted networks before reannealing:
500
----------------------------------------------------------------------------- Output directory:
data/static/output
- Report file:
static.report.txt
- Number of best networks tracked:
5
- Best networks are:
nonidentical
- Max. time:
1.0 h
- Min. networks before checking:
1000
- Screen reporting interval:
3.0 m
- File reporting interval:
10.0 m
----------------------------------------------------------------------------- Compute influence scores:
no
- Compute consensus graph:
no
- Create consensus graph as HTML:
no
- Create 'dot' output:
no
- Location of 'dot':
C:/Program Files/ATT/Graphviz/bin/dot.exe
----------------------------------------------------------------------------Memory info before starting the search: Banjo is using 11 mb of memory
Banjo can also display a “discretization report” for listing some of the core characteristics of the
data supplied in the observations file. In our case, the data was already discrete, with values
from 0 to 3, so no discretization was necessary. For this reason the “discretization policy”
setting was set to “none”. The report simply describes the original data and what was done to
it by the discretization policy, if anything.
----------------------------------------------------------------------------- Pre-processing
Discretization report
----------------------------------------------------------------------------Variable | Discr. | Min. Val. | Max. Val. | Orig. | Used |
|
|
|
| points | points |
----------------------------------------------------------------------------0
| none |
0.0 |
1.0 |
2 |
2 |
1
| none |
0.0 |
3.0 |
4 |
4 |
2
| none |
0.0 |
3.0 |
4 |
4 |
3
| none |
0.0 |
3.0 |
4 |
4 |
…
29
| none |
0.0 |
3.0 |
4 |
4 |
30
| none |
0.0 |
3.0 |
4 |
4 |
31
| none |
0.0 |
3.0 |
4 |
4 |
32
| none |
0.0 |
3.0 |
4 |
4 |
-----------------------------------------------------------------------------
In Banjo 2, the display of the discretization report can be controlled by the end user via the
createDiscretizationReport setting, which can take the values “none”, “standard” (the report
format displayed above), “withMappedValues“ (which includes a summary of the mapping to
the discretized values), and “withMappedandOriginalValues“ (which contains a complete
description of the mapping; note that for data sets with a large number of distinct values, this
results in a lot of data being displayed).
Banjo then starts the execution of the search, and provides periodic feedback on its progress:
in our case, the max. time allotted for the search was 60 minutes, and we had requested a
progress reports every 3 minutes. In addition, we instructed Banjo that the intermediate
results be saved to file every 10 minutes.
p. 13
Starting search at 8/31/06 1:04:53 PM
Prep. time used: 953.0 ms
Beginning to search: expect a status report in 3.0 m
Status:
Status:
Status:
Networks
25349900
Time
3.0 m ( 5.0% of max.
Re-anneals 266
Banjo is using 11 mb of memory
Networks
51209600
Time
6.0 m ( 10.0% of max.
Re-anneals 537
Banjo is using 11 mb of memory
Networks
77414900
Time
9.0 m ( 15.0% of max.
Re-anneals 811
Banjo is using 11 mb of memory
1.0 h)
1.0 h)
1.0 h)
…
Finally, when the maximum allotted search time or number of search loops is reached, Banjo
prints out the search results, which includes the n best networks – in our case the single best
network – and a set of statistical information collected by the search components. Since each
data set has its own unique characteristics, the statistics are helpful in tuning a search
strategy.
Status:
Status:
Networks
493480900
Time
57.0 m ( 95.0% of max.
Re-anneals 5174
Banjo is using 11 mb of memory
Networks
518834300
Time
1.0 h (100.0% of max.
Re-anneals 5440
Banjo is using 11 mb of memory
1.0 h)
1.0 h)
----------------------------------------------------------------------------- Final report
Best network(s) overall
----------------------------------------------------------------------------These are the 5 top-scoring non-identical networks found during the search:
Network #1, score: -8451.65, first found at iteration 164065386
33
0 2 5 25
1 1 17
2 1 17
3 1 5
…
29 1 13
30 1 0
31 1 29
32 1 13
Network #2,…
----------------------------------------------------------------------------- Search Statistics
----------------------------------------------------------------------------Statistics collected in searcher 'SearcherSimAnneal':
Search completed at 8/31/06 2:04:53 PM
Number of networks examined: 518834300
Total time used: 1.0 h
High score: -8451.65, first found at iteration 164065386
p. 14
Number of re-anneals: 5440
Statistics collected in proposer 'ProposerRandomLocalMove':
173576103
Deletions -- proposed:
172645027
Reversals -- proposed:
172613169
Statistics collected in cycle checker 'CycleCheckerDFS':
Additions -- considered: 173576103, acyclic: 157439233
Deletions -- considered: 172645027, acyclic: 172645027
Reversals -- considered: 172613169, acyclic: 169869811
Statistics collected in evaluator 'EvaluatorBDe':
Scores computed:
15424311
Scores (cache)
placed
fetched
with 0 parents:
33
285857828
with 1 parents:
1056
154165202
with 2 parents:
16368
209307755
with 3 parents:
14092605
4972684
with 4 parents:
1198133
107711
with 5 parents:
116116
5881
Statistics collected in decider 'DeciderMetropolis':
Additions -- considered: 157439233, better score:
41811555
Deletions -- considered: 172645027, better score:
22657459
Reversals -- considered: 169869811, better score:
21930375
Average permissivity:
0.223
22710155,
other accepted:
41864242,
other accepted:
48425220,
other accepted:
No Statistics collected in equivalence checker 'EquivalenceCheckerSkip'.
Memory info after completing the search: Banjo is using 11 mb of memory
Explanation of the Results
Banjo supplies the obtained high-scoring Bayesian network in the following form (the data for
nodes id = 4 to id = 28 is omitted):
Network #1, score: -8451.65, first found at iteration 164065386
33
0 2 5 25
1 1 17
2 1 17
3 1 5
…
29 1 13
30 1 0
31 1 29
32 1 13
The first line indicates the score (-8451.65) and when it was first encountered (iteration
164065386).
Line 2 indicates that the number of variables in the network is 33.
Lines 3 to 35 (one for each of the 33 variables) first list the id of a variable, then the number of
parents, and then a listing of the parents. E.g., “0 2 5 25” means that variable id = 0 has 2
parents, namely id = 5 and id = 25.
The graphical representation of the obtained network, generated using the Banjo dot format
output looks like this. Note that we used the variableNames setting to display the name of each
variable (we assigned variable “i” the name “Vi”, for i from 0 to 32)
p. 15
Example: Searching for the “Best” Dynamic Bayesian Network
The second example is a search for a dynamic Bayesian network (DBN), described as a
problem with 20 variables and 2000 observations. The minimum and maximum Markov lag in
this example are both equal to 1, which means that no links between nodes of Markov lag 0
are permitted. You may notice in the resulting statistics that no reversals were considered as
possible changes to the Bayesian network. In addition, there was no need for the search
algorithm to perform any cycle checking when proposing a change. We run the search with
java -jar banjo.jar settingsFile=data/release2.0/dynamic/dynamic.settings.txt
The application will provide immediate feedback on the settings that were supplied:
----------------------------------------------------------------------------- Banjo
Bayesian Network Inference with Java Objects - Release 2.0
1 Apr 2007 - Licensed from Duke University
- Copyright (c) 2005-2007 by Alexander J. Hartemink
----------------------------------------------------------------------------- Project:
banjo dynamic example
- User:
demo
- Data set:
20-vars-2000-temporal-observations
- Notes:
dynamic bayesian network inference
----------------------------------------------------------------------------- Settings file:
data/dynamic/dynamic.settings.txt
-----------------------------------------------------------------------------
p. 16
- Input directory:
data/dynamic/input
- Observations file:
dynamic.data.txt
- Number of observations (in file):
2000
- Number of observations used for learning DBN:
1999
- Number of variables:
20
- Discretization policy:
none
- Exceptions to the discretization policy:
none
----------------------------------------------------------------------------- Initial structure file:
- 'Must be present' edges file:
- 'Must not be present' edges file:
- Min. Markov lag:
1
- Max. Markov lag:
1
- DBN mandatory identity lag(s):
1
- Max. parent count:
5
- Equivalent sample size for Dirichlet parameter prior:
1.0
----------------------------------------------------------------------------- Searcher:
SearcherGreedy
- Proposer:
ProposerAllLocalMoves
- Evaluator:
defaulted to EvaluatorBDe
- Cycle checker:
CycleCheckerDFS
- Decider:
defaulted to DeciderGreedy
----------------------------------------------------------------------------- Pre-compute logGamma:
yes
- Cache:
fastLevel2
----------------------------------------------------------------------------- Min. proposed networks after high score:
1000
- Min. proposed networks before restart:
3000
- Max. proposed networks before restart:
5000
- Restart method:
use random network
with max. parent count:
3
----------------------------------------------------------------------------- Output directory:
data/dynamic/output
- Report file:
dynamic.report.txt
- Number of best networks tracked:
5
- Best networks are:
nonidentical
- Max. time:
3.0 m
- Min. networks before checking:
1000
- Screen reporting interval:
20.0 s
- File reporting interval:
10.0 m
----------------------------------------------------------------------------- Compute influence scores:
no
- Compute consensus graph:
no
- Create consensus graph as HTML:
no
- Create 'dot' output:
no
- Location of 'dot':
not supplied
--------------------------------------------------------------------------------------------------------------------------------------------------------- Pre-processing
Discretization report
----------------------------------------------------------------------------Variable | Discr. | Min. Val. | Max. Val. | Orig. | Used |
|
|
|
| points | points |
----------------------------------------------------------------------------0
| none |
0.0 |
2.0 |
3 |
3 |
1
| none |
0.0 |
2.0 |
3 |
3 |
2
| none |
0.0 |
2.0 |
3 |
3 |
3
| none |
0.0 |
2.0 |
3 |
3 |
4
| none |
0.0 |
2.0 |
3 |
3 |
5
| none |
0.0 |
2.0 |
3 |
3 |
6
| none |
0.0 |
2.0 |
3 |
3 |
7
| none |
0.0 |
2.0 |
3 |
3 |
8
| none |
0.0 |
2.0 |
3 |
3 |
9
| none |
0.0 |
2.0 |
3 |
3 |
10
| none |
0.0 |
2.0 |
3 |
3 |
11
| none |
0.0 |
2.0 |
3 |
3 |
12
| none |
0.0 |
2.0 |
3 |
3 |
13
| none |
0.0 |
2.0 |
3 |
3 |
14
| none |
0.0 |
2.0 |
3 |
3 |
15
| none |
0.0 |
2.0 |
3 |
3 |
16
| none |
0.0 |
2.0 |
3 |
3 |
17
| none |
0.0 |
2.0 |
3 |
3 |
18
| none |
0.0 |
2.0 |
3 |
3 |
19
| none |
0.0 |
2.0 |
3 |
3 |
p. 17
Banjo then provides periodic feedback on its progress, and, when the search is completed, it
supplies the final results. In our case this includes the 5 highest scoring networks, the
statistical information about the search, a basic output of the best network for generating a
graph in dot, and the list of influence scores.
Memory info before starting the search: Banjo is using 12 mb of memory
Starting search at 8/31/06 10:46:08 AM
Prep. time used: 1.3 s
Beginning to search: expect a status report in 20 s
Status:
Status:
Networks
Time
Restarts
Banjo is
376201
20.05 s ( 11.1% of max.
65
using 13 mb of memory
Networks
Time
Restarts
Banjo is
758101
40.06 s ( 22.2% of max.
132
using 12 mb of memory
Networks
Time
Restarts
Banjo is
2991361
2.67 m ( 88.9% of max.
524
using 12 mb of memory
Networks
Time
Restarts
Banjo is
3373261
3.0 m (100.0% of max.
591
using 12 mb of memory
3.0 m)
3.0 m)
…
Status:
Status:
3.0 m)
3.0 m)
----------------------------------------------------------------------------- Final report
Best network(s) overall
----------------------------------------------------------------------------These are the 5 top-scoring non-identical networks found during the search:
Network #1, score: -15935.29, first found at iteration 4941
20
0
1:
2 0 7
1
1:
1 1
2
1:
3 0 1 2
3
1:
2 2 3
4
1:
2 1 4
5
1:
2 4 5
6
1:
1 6
7
1:
2 3 7
8
1:
2 3 8
9
1:
3 5 6 9
10
1:
3 8 9 10
11
1:
2 10 11
12
1:
1 12
13
1:
1 13
14
1:
1 14
15
1:
1 15
16
1:
1 16
17
1:
1 17
18
1:
1 18
19
1:
1 19
Network #2, score: -15939.38, first found at iteration 4561
20
0
1:
2 0 7
1
1:
1 1
2
1:
3 0 1 2
3
1:
2 2 3
4
1:
2 1 4
5
1:
2 4 5
6
1:
1 6
p. 18
7
8
9
10
11
12
13
14
15
16
17
18
19
1:
1:
1:
1:
1:
1:
1:
1:
1:
1:
1:
1:
1:
2
2
2
3
2
1
1
1
1
1
1
1
1
3 7
3 8
5 9
8 9 10
10 11
12
13
14
15
16
17
18
19
Network #3, score: -15986.77, first found at iteration 4181
20
0
1:
2 0 7
1
1:
1 1
2
1:
3 0 1 2
3
1:
2 2 3
4
1:
2 1 4
5
1:
2 4 5
6
1:
1 6
7
1:
2 3 7
8
1:
2 3 8
9
1:
2 5 9
10
1:
2 9 10
11
1:
2 10 11
12
1:
1 12
13
1:
1 13
14
1:
1 14
15
1:
1 15
16
1:
1 16
17
1:
1 17
18
1:
1 18
19
1:
1 19
Network #4, score: -15996.63, first found at iteration 3801
20
0
1:
2 0 7
1
1:
1 1
2
1:
3 0 1 2
3
1:
2 2 3
4
1:
2 1 4
5
1:
2 4 5
6
1:
1 6
7
1:
2 3 7
8
1:
2 3 8
9
1:
2 5 9
10
1:
1 10
11
1:
2 10 11
12
1:
1 12
13
1:
1 13
14
1:
1 14
15
1:
1 15
16
1:
1 16
17
1:
1 17
18
1:
1 18
19
1:
1 19
Network #5, score: -16061.54, first found at iteration 3421
20
0
1:
2 0 7
1
1:
1 1
2
1:
3 0 1 2
3
1:
2 2 3
4
1:
2 1 4
5
1:
2 4 5
6
1:
1 6
7
1:
2 3 7
8
1:
2 3 8
9
1:
1 9
10
1:
1 10
11
1:
2 10 11
12
1:
1 12
13
1:
1 13
p. 19
14
15
16
17
18
19
1:
1:
1:
1:
1:
1:
1
1
1
1
1
1
14
15
16
17
18
19
----------------------------------------------------------------------------- Search Statistics
----------------------------------------------------------------------------Statistics collected in searcher 'SearcherGreedy':
Search completed at 8/31/06 10:49:08 AM
Number of networks examined: 3075721
Total time used: 3.0 m
High score: -15935.29, first found at iteration 4941
Number of restarts: 539
Statistics collected in proposer 'ProposerAllLocalMoves':
3019628
Deletions -- proposed:
56092
Reversals -- proposed:
0 (min. Markov lag = 1)
Statistics collected in cycle checker 'CycleCheckerDFS':
Additions -- no cyclicity test necessary
Deletions -- no cyclicity test necessary
Reversals -- none proposed
Statistics collected in evaluator 'EvaluatorBDeOriginal':
Scores computed:
820787
Scores (cache)
placed
fetched
with 0 parents:
0
0
with 1 parents:
20
55034
with 2 parents:
380
2140957
with 3 parents:
727674
70156
with 4 parents:
92713
8080
with 5 parents:
0
0
Statistics collected in decider 'DeciderGreedy':
Additions -- considered: 7016, better score: 7016
Deletions -- considered: 1078, better score: 0
Reversals -- considered: 0 (min. Markov lag = 1)
No Statistics collected in equivalence checker 'EquivalenceCheckerSkip'.
Memory info after completing the search: Banjo is using 12 mb of memory
----------------------------------------------------------------------------- Post-processing
DOT graphics format output
----------------------------------------------------------------------------digraph abstract {
label = "Banjo Version 1.0.0\nHigh scoring network, score: -15935.29\nProject:
banjo dynamic example\nUser: demo\nData set: 20-vars-2000-temporalobservations\nNetworks searched: 3075721";
labeljust="l";
7->0;
0->2;
1->2;
2->3;
1->4;
4->5;
3->7;
3->8;
5->9;
6->9;
8->10;
9->10;
10->11;
}
----------------------------------------------------------------------------- Post-processing
Influence scores
p. 20
----------------------------------------------------------------------------Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
(7,1)
(0,1)
(1,1)
(2,1)
(1,1)
(0,1)
(3,1)
(2,1)
(4,1)
(1,1)
(5,1)
(4,1)
(6,1)
(7,1)
(3,1)
(8,1)
(3,1)
(9,1)
(6,1)
(5,1)
(10,1)
(9,1)
(8,1)
(11,1)
(10,1)
(12,1)
(13,1)
(14,1)
(15,1)
(16,1)
(17,1)
(18,1)
(19,1)
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
(0,0)
(0,0)
(1,0)
(2,0)
(2,0)
(2,0)
(3,0)
(3,0)
(4,0)
(4,0)
(5,0)
(5,0)
(6,0)
(7,0)
(7,0)
(8,0)
(8,0)
(9,0)
(9,0)
(9,0)
(10,0)
(10,0)
(10,0)
(11,0)
(11,0)
(12,0)
(13,0)
(14,0)
(15,0)
(16,0)
(17,0)
(18,0)
(19,0)
-0.4377
0.7398
0.8321
0.7182
-0.2764
0.1788
0.7487
0.4088
0.831
0.2829
0.7699
-0.3771
0.8502
0.7327
0.4538
0.759
-0.4027
0.693
-0.1581
0.3163
0.6724
0.1913
0.3029
0.7665
-0.4201
0.8469
0.8396
0.8457
0.8427
0.8625
0.8746
0.8476
0.8608
Explanation of the Results
The obtained high-scoring Bayesian network is supplied in the following form (the data for
nodes id = 4 to id = 17 is omitted):
Network #1, score: -15935.29, first found at iteration 4941
20
0
1:
2 0 7
1
1:
1 1
2
1:
3 0 1 2
3
1:
2 2 3
…
18
1:
1 18
19
1:
1 19
In Banjo 2, we omit the (redundant) data for lags that are less than the specified minimum
Markov lag. For illustration, we show what the output would have been in Banjo 1.0:
[Deprecated Banjo 1.0 format]
Network #1, score: -15935.2860609,
20
0
0:
0
1:
2
1
0:
0
1:
1
2
0:
0
1:
3
3
0:
0
1:
2
…
18
0:
0
1:
1
19
0:
0
1:
1
first found at iteration 4941
0 7
1
0 1 2
2 3
18
19
p. 21
The first line indicates the ranking of the network (here: #1), and its associated score (here:
-15935. 29), first encountered at iteration 4941.
The second line indicates that the number of variables is 20.
Since we have a dynamic network with max. Markov lag 1, we list the parents for each node
and for each Markov lag in a separate “block”, starting with the respective Markov lag and a
colon (“:”). I.e., lines 3 to 22 (one for each of the 20 variables) first list the id of a variable, and
then a block for each Markov lag starting at the minimum Markov lag (here, 1), and up to the
maximum Markov lag (here, also 1). As an example, for node id = 0, “1: 2 0 7” indicates that
variable id = 0 has 2 parents of Markov lag 1, namely variable id = 0 and variable id = 7). In
Banjo 2, the block of data for lag 0 (which is excluded from contributing parents by the
specified minimum Markov lag) is suppressed from the output.
The graphical representation of the network, obtained using dot, is this:
Note that since the min. and max. Markov lags are the same, all displayed nodes (variables) are
displayed without reference to their corresponding lag.
p. 22
Using Banjo
To make the most of Banjo, it is useful to take a look at what approach Banjo takes to solving
the network inference problem.
The Banjo Application
The Banjo application is built around the Searcher class. A Searcher examines the space of
possible solutions using a suitable search strategy. Banjo currently implements greedy and
simulated annealing searches.
The focus of the remainder of this section will be the internal structure of the Searcher class
because the structure of the internal components of Searchers lets us fine-tune the search
strategy that we choose.
From a high-level point of view, each Searcher can be decomposed into the following tasks:
•
Select an initial “current” network (can be the empty network, or some other preselected network). Then iterate through the following set of steps:
1. Propose a new network that is to be considered. Often, the proposed network is
dependent on the current network (it represents a local change to the current
network).
2. Check the proposed network for cycles (but only if they are even possible).
3. Compute the score of the proposed network using a predefined metric.
4. Decide, possibly stochastically, whether to accept the proposed network (as the
new current network).
Banjo is also able to propose, check for cycles, and evaluate a set of local changes to the
current network (for example, an exhaustive list of all local changes). In this case, the decider
then decides whether to accept the best change in the set.
Beneath this conceptual component layer is a secondary data structure layer that is crucial for
Banjo to achieve high performance. These data structures include the actual representation of
a Bayesian network, a “change to a Bayesian network”, a “high-scoring network structure”, etc.
A note to developers: From a development point of view, the component layer and the data
structure layer are almost completely separated, so it would be fairly straightforward to
experiment with a different set of data structures. There are only a few (2 or 3) welldocumented code locations where we chose to sacrifice the separation between the layers to
achieve better performance.
p. 23
The Banjo Components
(The Core Banjo Objects)
The Searchers
As the top-level component of the Banjo architecture for implementing a structure learning
strategy, a Searcher’s main task is to manage all aspects of searching in a space of possible
networks for the “best possible” (i.e., highest scoring based on a scoring metric) network. In
general, a search is built around a search loop that executes for an allotted amount of time or
until a specified number of networks have been proposed and considered. At the end of the
search execution, Banjo reports the network(s) with the best score(s) found.
Within the search loop, Banjo allows various combinations of Proposer, CycleChecker,
Evaluator, and Decider components to handle the internal aspects of each iteration step. For
example, a greedy search may examine a single, randomly selected change to the network and
keep the new network if it scores better than the current network, or discard the change if it
scores lower. Note that for efficiency reasons, a change to the network (for the purpose of our
application) is typically defined as a local change: the addition, deletion, or reversal of a single
edge in the current network.
Let’s take a look at Banjo’s design and break down the Searcher into some well-defined
internal components. Instead of examining the single, randomly selected change to the
network, a greedy search could also be implemented by examining all possible moves that are
available from the current network in a single step (i.e., by examining all possible additions,
deletions, and reversals of any single edge in the current network). These two variations of
greedy search share most of their logic except for the “proposing” of the change(s) to the
current network! By having a clearly defined “Proposer” subtask, we only have to implement a
new Proposer, combine it with the other existing component tasks, and we conveniently end up
with a completely new search strategy. To produce a new search strategy, we just have to
p. 24
specify what components we want the search to use, mixing and matching with what is already
available or possibly implementing a new search component.
The Proposers
A Proposer implements the part of the search algorithm that specifies what possible change or
changes are to be considered at a single search iteration step. Choices for available Proposers
depend on the selected search algorithm. If incompatible choices are selected for the Proposer
and Searcher components, Banjo will notify the user and stop execution.
Currently, Banjo implements 2 types of Proposers, namely ProposerRandomLocalMove, and
“ProposerAllLocalMoves”, which both turn out to be compatible with either currently available
search strategy, greedy and simulated annealing. The designated default proposer is
ProposerRandomLocalMove, which simply selects a move at random from all available local
moves. By a move we mean the adding of subtracting of a parent, or the reversing of a parent
relationship (i.e., reversing of an edge in the corresponding graph representation of the
network). In contrast, ProposerAllLocalMoves composes a list of all available local moves, given
the current state of the network, and then selects the move that yields the highest scoring
network. Note that for greedy searches, this tends to find local maxima quickly, and, by its
nature, only makes sense to be used with restarts based on randomly configured networks.
The Cycle Checkers
The task of the cycle checker is to examine whether each proposed network contains a cycle.
Trivially, if it does, then the proposed change is discarded, and the search goes back to the
Proposer to request another possible network change.
If the proposed network does not contain a cycle, then the next step in the search is the score
computation performed by an Evaluator.
In version 2 we have replaced the depth first search (DFS) strategy with an improved version,
and have added a modified DFS strategy based on the paper by O. Shmueli. Both strategies
provide significant improvements in performance, so we discontinued the use of the breadth
first search strategy altogether.
The structure of the underlying problem will affect the performance of the algorithms. We
added user access to the selection of the search strategies, because each has its unique
strength. Some experimentation may be necessary to find the best method for a particular
problem.
From a functional (code) point of view, the cycle checker choice is independent of the choices of
the other core objects. However, the choice of searcher and proposer does significantly
influence the effectiveness of the Shmueli optimization: there is a “penalty” for reversals of
edges, which is especially pronounced for a Greedy search with AllRandomMoves.
The Evaluators
An Evaluator in Banjo computes the score of a network, based on some scoring metric. There
is currently only one Evaluator available in Banjo, which uses the BDe metric to compute a
network’s score, as described first by Cooper and Herskovits and later by Heckerman.
p. 25
You can specify an Evaluator via the value of evaluatorChoice in the settings file. The valid
choices are “default” and “EvaluatorBDe”, with identical effect: both will cause Banjo to select
the BDe metric in the score computation.
The evaluator choice is also independent of the choices of the other core objects.
The Deciders
A Decider in Banjo determines whether the proposed network in the current search iteration
will be accepted as the new current network for the next iteration, or if it will be rejected, in
which case the search proceeds from the current network.
The choice of the decider is tightly connected to the selected search strategy, which is
expressed in fact via the naming of our deciders, DeciderMetropolis and DeciderGreedy.
The Equivalence Checkers
The EquivalenceChecker has been added in Banjo 2 for comparing networks when tracking a
set of high-scoring networks during a search. In Banjo 1.0, the obtained high-scoring set was
composed of N networks that were only compared to each other with respect to identity, thus
allowing multiple equivalent networks to be part of the final result.
Note that the choice of equivalence checker is determined by the bestNetworksAre setting.
The equivalence checker choice is also independent of the choices of the other core objects.
p. 26
Summary of Component Options
When you decide you want to implement a new search strategy, you may want to look at the
existing search algorithms in Banjo. Both Greedy and Simulated Annealing search strategies
can be applied via the provided Searchers. Each of them can be used with a random local
move or all local moves approach by specifying the appropriate Proposer. The greedy approach
uses a greedy Decider, which only accepts networks with better scores, whereas the simulated
annealing approach accepts networks based on a stochastic Decider (implementing MetropolisHastings). The table below shows how one can select different components in the existing
search implementations:
Searcher
Options
SimAnneal
(for simulated
annealing
search)
Dependent
core objects
Proposer
Choices for the
dependent core objects
RandomLocalMove
AllLocalMoves
Greedy
(for “greedy”
search)
Decider
Metropolis
Proposer
RandomLocalMove
AllLocalMoves
Skip
Decider
Greedy
N/A
N/A
Explanation
Addition, deletion, or reversal of
an edge in the current network,
selected at random.
All changes arising from a single
addition, deletion, or reversal of
an edge in the current network.
A Metropolis-Hastings stochastic
decision mechanism, where any
network with a higher score is
accepted, and any with a lower
score is accepted with a
probability based on a system
parameter known as the
“temperature”.
Addition, deletion, or reversal of
an edge in the current network,
selected at random.
All changes arising from a single
addition, deletion, or reversal of
an edge in the current network.
A network is accepted if and only
if its score is better than or equal
to that of the current network.
In the case of AllLocalMoves, the
best local move is considered.
The search is skipped entirely, to
immediately apply the postprocessing options.
(Introduced in Banjo 2)
A new searcher in Banjo version 2 is indicated by the searcher option named “Skip”, which
simply instructs Banjo to only setup the underlying network structures, but skip the actual
search execution, and go straight to the post processing code.
In addition, we want to add that the AllLocalMoves proposer option should be used cautiously,
so that the search does not visit the same networks over and over again. In particular, the
restartWithRandomNetwork setting should always be set to “yes” when using AllLocalMoves.
p. 27
Setting up a Banjo Search
One of the goals of the Banjo distribution was to provide all the pieces for a researcher to
quickly get the program up and running, by including sample settings files and detailed
instructions in both this user guide and the developer guide. However, there are some intrinsic
issues with using software that is intended for the serious researcher, the most important
being the fact that the user needs to have a thorough understanding of the methodology that
he/she is going to use, and that software like Banjo cannot be expected to be successfully
applied to a research problem without some further investigating, and in many cases, careful
and extensive experimenting with a number of parameters that govern the program execution.
The sections on tuning the memory use, tuning the search performance, and selecting the
input discretization describe in detail the tuning parameters and their settings. In the section
on experimenting with Banjo we use some basic examples to try to illustrate some effects that
may surprise a first time user.
Tuning the Memory Use
The previous section describes the methods that are available for a search. Obviously, there
are only a small number of ways to combine the core options for a search. However, to run
each search effectively it is quite important to tune a number of parameters that determine the
run time performance of the program. This section is intended to assist the user in selecting
the appropriate choices for their problem at hand.
The main constraint on Banjo as it is executed on a computer is the memory available to its
code. Depending on the size of the underlying problem, i.e., the number of variables and the
number of observations, Banjo may not be able to run at all unless we modify some or all of
the settings that control the amount of memory that Banjo will request from the system.
The settings that may need to be adjusted are:
1. The useCache setting, which adjusts the amount of information that Banjo caches to
avoid re-computing already obtained scores. Note that internally there are 2 different
storage containers (“caches”), namely the so-called “fastCache”, and a “general cache”.
The fastCache stores all scores for variables with up to a fixed number of parents, up to
a maximum level of 2 (parents). E.g., when fastCache is set to level 1, then the fast
cache will request room for storing scores for all variables, and their parent
configurations with 0 and 1 parent. Note that the fastCache is filled on an as-needed
basis, but no scores are ever deleted from the fastCache. The heuristic justification for
the fastCache use lies in the observation that the search algorithms visit these network
configurations over and over, so we don’t want them to be subjected to the same
mechanism as the regular cache. All other scores are stored in a general cache that
uses whatever memory is “left”.
Note that for performance reasons, a higher fastCache level is always better.
2. The precomputeLogGamma setting, which – when set to “yes” – causes Banjo to precompute a table for looking up the potential log gamma value for the score computation
based on the BDe metric.
3. On a secondary level, Banjo’s memory use gets affected (for large problems) by the
nBestNetworks setting. We need to keep in mind that storing potentially thousands of
networks, each with thousands of variables, will substantially increase the memory
requirements.
p. 28
The default settings for Banjo 2 are tuned for small problems, which we define as up to 100
variables, with up to several thousand observations. There is nothing to adjust in this case,
since the required memory is well within the bounds of the standard allocation by the Java
virtual machines. The cache use is set at fastLevel2, and the precomputeLogGamma is set to
“on”. We can expect the total memory use by Banjo to be far less than the 64 mb, the standard
memory allocation for the SUN JVM.
For a medium size problem, which we define as up to 2000-3000 variables, and 2000-3000
observations, we need to restrict the fastCache to Level 1 (raising the fastCache to level 2
requires an additional storage of up to several GB of memory). At his point we also want to
turn off the precomputeLogGamma setting. With these modified settings we can expect the
memory use to be within about 300-500 MB, easily feasible for a suitably equipped desktop
computer.
For all problems that are larger than mentioned above, it will be important to know how much
memory can be made available to the JVM, and a little trial and error may be necessary to find
the optimal balance between performance (i.e., the number of networks we can visit in a given
amount of time), and having sufficient memory available simply to be able to run a search at
all. As long as the displayMemoryInfo setting is set to “yes”, the total amount of memory used
by Banjo will be displayed. To gain even more insight where the memory is being allocated, a
brief excursion into the Banjo code will be necessary: at the developer level one can set the
TRACE_MEMORYUSE constant to true – the provided info is the most detailed for a SimAneal
search and a localRandomMove proposer.
Performance Tuning
The second major consideration for setting up Banjo is the overall performance (speed) of the
search. For obvious reasons, the more variables and/or observations have to be factored into
the computations, the more time-consuming it becomes for the search to move from one
network to the next. Thus one should closely examine a problem theoretically before running a
search, and eliminate all redundant or unnecessary information, whenever possible: for many
large data sets this could mean anything from removing variables to limiting the maximum
parent count that each node may have.
Another major influence on performance is the choice for the bestNetworksAre setting. This is a
Banjo 2 feature that allows a search to be restricted to finding non-equivalent networks only.
There are three valid choices for bestNetworksAre, namely nonidenticalThenPruned,
nonidentical, and nonequivalent. “Nonidentical” corresponds to the way that Banjo 1.0.x used to
search, i.e., when nBestNetworks=N, then simply find the N highest scoring, different
networks. The drawback of this approach is that the result set can consist of 1 or more subsets
of networks that are “equivalent”.
It turns out that when comparing networks for equivalence each time we encounter a new
network comes at a very high performance cost (as documented in the literature). We can get
partially around this performance penalty by letting the search collect the best n non-identical
networks, and pruning this result set at the very end. Of course, in general we will likely end
up with fewer non-equivalent networks than the found non-identical networks, so we have to
compensate by tracking a larger set of non-identical networks from the start. Still, the
mentioned problem remains – there is no exact recipe for working around this issue.
Finally, there are the choices for intermediate feedback to the console (via the
screenReportingInterval setting), and the intermediate results output to file (via the
fileReportingInterval). Each of these operations will slow down the actual search, so we want to
choose reasonable intervals. On the other hand, should a Banjo search be terminated
p. 29
externally, then the last intermediate report that was written to file would be the final recorded
search result.
Input Discretization Options
This version of Banjo requires networks to be over discrete variables, since it only implements
the BDe scoring metric. It is often best to transform your data to discrete values using some
intelligent discretization strategy, but if you simply want to perform either quantile or interval
discretization, Banjo can do this for you.
Discretization is controlled by two settings. The first, called discretizationPolicy, specifies a
default policy for all variables; the second, called discretizationExceptions, specifies a list of
potential exceptions to the default policy. The default policy can be either the token “none” or
a token like “q2” or “i4”. The latter types of tokens begin with either a “q” for quantile
discretization or an “i” for interval discretization and are then followed by an integer specifying
the desired number of states. This integer should be at least 1, and no larger than the
maximum number of states permitted for any discrete variable in Banjo (which is 5 by default,
but can be adjusted to a larger value in BANJO.java (beware that this can consume a lot of
memory)). It should be noted that if you specify quantile discretization and a number of states
that is greater than the number of values in your data for one of your variables, then that
variable’s states will not be altered.
If there are any exceptions to the default policy, then you specify a comma-separated list of
tokens which have the form: variable index (starting at 0), colon, and discretization policy for
that variable, e.g. “0:q4,5:none,7:i2”.
To monitor the data mapping done by Banjo, version 2 lets you specify the new setting
createDiscretizationReport, using the values no, standard, withMappedValues or
withMappedAndOriginalValues, shown below with a sample output:
•
“standard”,
Variable | Discr. | Min. Val. | Max. Val. | Orig. | Used |
|
|
|
| points | points |
----------------------------------------------------------------------------0
|
q5 |
0.0 |
1.0 |
2 |
2 |
1
|
q5 |
0.0 |
3.0 |
4 |
4 |
2
|
q5 |
0.0 |
3.0 |
4 |
4 |
3
|
q5 |
0.0 |
3.0 |
4 |
4 |
4
|
q5 |
0.0 |
3.0 |
4 |
4 |
This is the most compact output, showing only the minimum and maximum values,
as well as the number of different original and used values, for each variable.
•
“withMappedValues”
Variable | Discr. | Min. Val. | Max. Val. | Orig. | Used |
|
|
|
| points | points |
----------------------------------------------------------------------------0
|
q5 |
0.0 |
1.0 |
2 |
2 | map: {0.0=0, 1.0=1}
1
|
q5 |
0.0 |
3.0 |
4 |
4 | map: {0.0=0, 1.0=1,
2
|
q5 |
0.0 |
3.0 |
4 |
4 | map: {0.0=0, 1.0=1,
3
|
q5 |
0.0 |
3.0 |
4 |
4 | map: {0.0=0, 1.0=1,
4
|
q5 |
0.0 |
3.0 |
4 |
4 | map: {0.0=0, 1.0=1,
2.0=2,
2.0=2,
2.0=2,
2.0=2,
3.0=3}
3.0=3}
3.0=3}
3.0=3}
In our example, the mapping consists of a trivial one, since our original data had
the integer values 0 to 3. So the mapping has 0 (interpreted as the real valued 0.0)
going to 0, 1 (again, interpreted as a real value, 1.0) going to 1, etc.
p. 30
•
“withMappedAndOriginalValues”.
Variable | Discr. | Min. Val. | Max. Val. | Orig. | Used |
|
|
|
| points | points |
----------------------------------------------------------------------------0
|
q5 |
0.0 |
1.0 |
2 |
2 | map: {0.0=0, 1.0=1}
counts: {0.0=250, 1.0=70}
1
|
q5 |
0.0 |
3.0 |
4
values with counts: {0.0=4, 1.0=2, 2.0=60, 3.0=254}
orig. values with
|
4
|
map: {0.0=0, 1.0=1, 2.0=2, 3.0=3}
orig.
2
|
q5 |
0.0 |
3.0 |
4 |
values with counts: {0.0=12, 1.0=54, 2.0=116, 3.0=138}
4
|
map: {0.0=0, 1.0=1, 2.0=2, 3.0=3}
orig.
3
|
q5 |
0.0 |
3.0 |
4
values with counts: {0.0=8, 1.0=36, 2.0=234, 3.0=42}
|
4
|
map: {0.0=0, 1.0=1, 2.0=2, 3.0=3}
orig.
4
|
q5 |
0.0 |
3.0 |
4 |
values with counts: {0.0=4, 1.0=12, 2.0=108, 3.0=196}
4
|
map: {0.0=0, 1.0=1, 2.0=2, 3.0=3}
orig.
The additional information provided tells us what values (for each variable) occurred how many
times in the original data set. E.g., variable id=2 had 12 occurrences of 0.0, 54 occurrences of
1.0, etc.
p. 31
Experimenting with Banjo
In the sections above we have described in detail the most important tuning parameters. The
question of course remains how to actually use them in a “real” problem. Unless otherwise
indicated, we use our supplied example problem for the static bayesnet case, with 33 variables
and 320 observations, to show how various setting values affect the outcome of a search.
Ultimately, our goal is to alert Banjo users that the program is not a magic black box, but a
tool that requires careful planning, experimenting, and execution on part of the user.
The results below show that Banjo 2 allows the user to tackle problems with a large number of
variables of, say, in the thousands. However, we admit frankly that the Banjo code is not fully
optimized for such large problems. While the current code base has eliminated a number of
inefficiencies stemming from Java’s implementation of multidimensional arrays, one can
expect a sizable performance boost from rewriting the underlying parent set implementation
using any of a number of well-known techniques for implementing sparse matrices. Tackling
these large problems is not within the scope and immediate interest of our research group,
and, with finite resources at our disposal, we leave such changes to the motivated
user/developer/student. The required changes are easily identifiable within the Edges
subhierarchy of the Banjo code, and the few locations where we have to break the
encapsulation of our classes for performance reasons are well documented in the code.
Note: All searches for a given example have been executed on the same machine, running as
the only computing-intensive process running at the time. Searches for different examples may
have been executed on different machines, so absolute comparisons across examples may not
be meaningful.
Example: Choice of Discretization
The data for our static example has 2 values for variable 0, 3 values for variable 1, and 4
values for all the rest of the variables. To illustrate the effect of something as simple as the
choice input discretization, we will compare the results from a batch of short searches for 1
minute each, using internal and quantile discretization of 2 and 3 values for each variable.
Discretization
Networks visited
(1 min. test run)
Graph
None
9 million
Q3
6.5 million
Q2
4.5 million
I3
7.2 million
I2
4.6 million
Networks visited
that have 4 or 5
parents
Highest parent
count for a
single variable in
top network
23,000
62,000
248,000
51,000
235,000
2 (for 1 var.)
2 (for 12 var.)
4
2 (for 7 var.)
4
p. 32
We observe a much higher complexity of the resulting networks for the i2 and q2
discretizations when compared to the i3 and q3 results, and for all of them compared to the
reference result based on the “known” data. In addition, there is a significant performance
impact from visiting networks with higher parent counts for the individual variables.
Example: Combinations of useCache, MaxParentCount, and
precomputeLogGamma
In this case we execute short (1 minute) searches to examine the effect of the various cache
settings when combined with different maxParentCount and precomputeLogGamma values, on
the search performance, in our 33 variable sample data. The search used a Simulated
Annealer searcher with a RandomLocalMove proposer.
Test 1: maxParentCount=5, precomputeLogGamma=no
Cache level
None
Basic
Number of
1,404,000
1,867,000
networks
examined
12
17
Number of Reanneals
Memory used
1 mb
1 mb
High score
-8,452.44
-8,467.48
(Node) Scores
1,812,226
1,092,991
computed
Baseline
1.33
Networks visited
improvement
Fastlevel0
1,927,000
Fastlevel1
2,510,000
Fastlevel2
6,778,000
17
23
63
1 mb
-8,477.89
1,127,346
1 mb
-8,475.26
807,418
1 mb
-8,471.7
217,877
1.37
1.79
4.83
We observe that the performance in terms of searched networks increases substantially the
higher we set our cache. Since our problem only has a small number of variables we are not
impacted by an increase in memory requirements, so it is safe to turn the cache to its
maximum level.
Test 2: maxParentCount=5, precomputeLogGamma=yes
Cache level
None
Basic
Number of
2,449,000
3,237,000
networks
examined
22
30
Number of Reanneals
Memory used
11 mb
11 mb
High score
-8,472.79
-8,459.66
(Node) Scores
3,163,662
1,891,031
computed
Baseline
1.32
Networks visited
improvement
Fastlevel0
3,181,000
Fastlevel1
4,128,000
Fastlevel2
8,419,000
29
38
78
11 mb
-8,466.67
1,859,609
11 mb
-8,465.07
1,334,203
11 mb
-8,464.89
267,525
1.30
1.68
3.43
We observe that using the precomputed log-gamma values allow us to compute the node
scores faster, so that for each cash setting we can visit a larger number of networks. The
relative advantage of the highest cache level shrinks somewhat, but only because the baseline
scenario of using no cache at all performs the most computations, so it also benefits the most
from the precomputed values.
p. 33
Test 3: maxParentCount=10, precomputeLogGamma=no
Cache level
None
Basic
Number of
1,311,000
1,702,000
networks
examined
11
16
Number of Reanneals
Memory used
21 mb
21 mb
(Node) Scores
-8468.1
-8469.37
computed
Scores
1,691,666
1,000,986
computed
Baseline
1.29
Networks visited
improvement
Fastlevel0
1,740,000
Fastlevel1
2,173,000
Fastlevel2
6,741,000
16
20
63
21 mb
-8473.54
21 mb
-8481.13
21 mb
-8455.11
1,025,207
691,906
215,815
1.32
1.66
5.14
When we increase the maximum number of parents that we allow any variable to have, the
performance for each cache level decreases, because computing scores for nodes with more
parents are more expensive.
Test 4: When we set precomputeLogGamma to yes with maxParentCount=10 or larger, we
cannot fit our problem in (512mb) of memory, even with no caching of node scores.
Finally, we compare what happens when we increase the maximum parent count, combined
with potential cache and pre-compute logGamma choices. Again, the tests are short 1 minutes
runs that one might perform to get a “feel” for different parameter choices, before running a full
blown search (that may many hours).
Max. parent
count
Cache level
Pre-compute loggamma
Number of
networks
examined
Prep. time
Memory used
(Node) Scores
computed
5
6
7
8
9
10
11
12
Fastlevel2
yes
Fastlevel2
yes
Fastlevel2
yes
Fastlevel2
no
Fastlevel2
no
Fastlevel2
no
Fastlevel2
no
Fastlevel2
no
8,388,000
8,586,000
8,564,000
6,885,000
6,826,000
6,730,000
6,423,000
6,552,000
1.0 s
11 mb
265235
3.2 s
42 mb
270,105
12.6 s
163 mb
272,409
0.265 s
2 mb
225,323
0.281 s
6 mb
224,198
0.329 s
21 mb
215,955
0.421 s
81 mb
206,893
0.766 s
321 mb
211,800
We clearly observe the effects on the required memory and the prep time, when we increase the
parent counts, especially when the “precompute logGamma values” is specified. Also note that
even for the short 1 minute searches we already observe a drop in networks visited, due to the
more expensive computation of scores with higher parent counts.
In a variation of this search we compare the lowest and highest cache levels when paired with
the precompute-logGamma parameter. Again, we perform 1 minute searches using a
Simulated Annealer searcher with a RandomLocalMove proposer.
Cache level +
Precompute of
logGamma
Number of
networks
examined
Number of Reanneals
Memory used
High score
(Node) Scores
None +
precompute
logGamma=no
1,429,000
None +
precompute
logGamma=yes
2,474,000
Fastlevel2 +
precompute
logGamma=no
6,800,000
Fastlevel2 +
precompute
logGamma=yes
8,500,000
13
22
64
79
1 mb
-8,492.8
1,844,520
11 mb
-8,472.97
3,193,712
1 mb
-8,463.58
221,069
11 mb
-8,467.29
269,878
p. 34
computed
Networks visited
improvement
Baseline
1.73
4.75
5.95
Here we want to point out the increase in performance related to the increase in cache use, as
well as the increase in memory use due to the precomputing of the log-Gamma values.
However, you have probably noticed that the scores for the obtained top networks are not
always in the order that we would expect. Well, since we are executing a random process, there
is no guarantee that the number of networks searched is directly correlated to the quality of
the obtained top-scoring network. On the other hand we only ran very short tests, and we will
definitely increase our “odds” of finding better and better scores by searching over as many
networks as possible.
Example: Varying the Cache Level in an Intermediate-size
Problem
In this example we take a look at the effect of the cache for a larger problem of 320 variables
and 33 observations, precompute Log-Gamma = yes, max. time = 1 minute.
Cache level
Number of
networks
examined
Number of Reanneals
Memory used
(Node) Scores
computed
Networks visited
improvement
None
Basic
Fastlevel0
Fastlevel1
Fastlevel2
332000
336000
333000
331000
348000
2
2
2
2
3
5 mb
5 mb
5 mb
6 mb
258 mb
436976
221625
219153
144640
137831
Baseline
1.01
1
1
1.05
As expected, the cache setting has a larger influence on the memory use than in the 33
variable case, since the fast cache is proportional to the number of variables. On the other
hand, the effect of the precomputeLogGamma setting is not as pronounced, because we have a
smaller number of observations. Note that in this case a 1 minute search reveals no difference
in performance between the different cache settings – this is simply due to the fact that we are
still in the startup phase of the search, where the compute more scores than we retrieve from
the cache (compare to the values in the 33 variables case!). The following table lists the
memory use as we increase the maximum parent count:
Max. parent
count
Cache level
Pre-compute loggamma
Prep. time
Memory used
5
6
7
8
9
10
11
12
Fastlevel2
yes
Fastlevel2
yes
Fastlevel2
yes
Fastlevel2
yes
Fastlevel1
yes
Fastlevel2
no
Fastlevel1
no
none
no
1.7 s
258 mb
1.7 s
258 mb
3.2 s
275 mb
7.5 s
335 mb
26.1 s
301 mb
1.6 s
276 mb
0.875 s
325 mb
0.86 s
324 mb
Example: Varying the Cache Level in a Large-size Problem
In this example we take a look at the effect of the cache for a larger problem of 3200 variables
and 33 observations. The following data is from 1 hour searches.
Cache level
Number of
networks
None
178,000
Basic
178,000
Fastlevel0
180,000
p. 35
Fastlevel1
176,000
examined
Number of Reanneals
Memory used
(Node) Scores
computed
Networks visited
improvement
0
0
0
0
295 mb
240,029
296 mb
113,066
296 mb
114,386
374 mb
87,813
(see comment)
In this case the outcome is somewhat more drastic, since we cannot select the 2-parent fast
cache without running out of memory (on a desktop machine with 1 gb of memory). Even after
one hour into the search there is no noticeable difference between the results from the different
cache settings. Note that we can expect to visit approximately as many networks as we visit in
a 1-3 minute search for our 33 variable problem. Of course, there are many more values to
cache, so we don’t get as much of an acceleration effect as we do for smaller problems.
Example: Comparing Searchers
In this example we compare search results when combining our available searchers with the
available proposers. Note that the quality of the search is not directly related to the number of
networks visited, but instead also depends on the intrinsic capabilities of a searcher to visit
different regions within the (very large) solution space. In our experience the simulated
annealer method seems to produce the best results.
Searcher
Proposer
Networks visited
deletions/
reversals
Top score
(Node) Scores
computed
Scores
computed for
variables with 4
or 5 parents
Sim. Anneal
RandomLocal
12,705,000
4,251,588
4,22,8316
4,225,095
-8,457.15
393,636
Greedy
RandomLocal
11,998,000
3,996,182
4,002,915
3,998,902
-8,585.73
461,451
Sim. Anneal
AllLocal
21,563,355
21,563,355
695,013
695,013
-8,476.19
2,130
32,997
11,417
0
Greedy
AllLocal
10,285,791
9,552,226
366,782
366,782
-8,476.19
1,645,477
68,051
Anologous results hold still true when we run with the same search parameters, but a search
time of 60 minutes:
Searcher
Proposer
Networks visited
deletions/
reversals
Top score
Scores
computed
Scores
computed for
variables with 4
or 5 parents
Sim. Anneal
RandomLocal
765,474,000
256,132,104
254,670,930
254,670,965
-8450.66
22,654,599
Greedy
RandomLocal
726,968,000
242,332,695
242,322,670
24,2312,634
-8567.85
26,737,902
Sim. Anneal
AllLocal
1,374,637,606
1,291,327,323
41,655,141
41,655,141
-8476.19
2,130
Greedy
AllLocal
610,888,955
566,997,844
21,945,569
21,945,541
-8468.73
100,126,278
1,950,474
574,571
0
4,120,024
p. 36
Example: The Effects of Equivalence Checking on Performance
We now take a look at the various effects of the bestNetworksAre parameter, both in terms of
performance and obtained results. Our underlying data is from our 33 variable problem, using
a simulated annealer search with the randomMove proposer.
NBestNetworks=10, max. time = 5 m
BestNetworksAr
Nonidentical
e
(obtained 10 non-identical
networks only)
66,552,000
Networks visited
Number of reanneals
Top score
Memory used
nonidenticalThenPruned
(obtained 2 nonequivalent networks)
65,821,000
626
617
nonequivalent
(obtained 10 nonequivalent networks)
66,423,000
(13,538 equivalence
checks)
624
-8453.97
11 mb
-8459.01
11 mb
-8457.67
11 mb
We observe that when we search for a small number of nBest networks, there is not much
difference in performance between the 3 choices. We want to point out, though, that only the
nonidentical and the nonequivalent choices will provide us with exactly nBest networks. It is
the nature of the pruning approach that will yield anywhere from 1 to nBest results when we
use nonidenticalThenPruned.
NBestNetworks=100, max. time = 5 m
BestNetworksAr
Nonidentical
e
(obtained 100 nonidentical networks only)
67,007,000
Networks visited
Number of reanneals
Top score
Memory used
631
627
nonequivalent
(obtained 100 nonequivalent networks)
60,322,000
(678,080
equivalence checks)
567
-8455.13
12 mb
-8454.56
12 mb
-8461.37
12 mb
nonidenticalThenPruned
(obtained 231 nonequivalent networks)
65,581,000
NBestNetworks=1000, max. time = 5 m
BestNetworksAr
Nonidentical
e
(obtained 1000 nonidentical networks only)
66,657,000
Networks visited
Number of reanneals
Top score
Memory used
nonidenticalThenPruned
(obtained 11 nonequivalent networks)
66,617,000
626
618
nonequivalent
(obtained 1000 nonequivalent networks)
49,000
(8,033,553
equivalence checks)
0
-8461.97
16 mb
-8455.18
13 mb
-9465.26
17 mb
We notice that as we increase the nBest setting, the performance the “nonequivalent” is
severely impacted by the number of equivalence checks necessary to maintain the set of
nonequivalent networks during the search.
Example: Comparing different Cycle Checking Methods
This is a comparison between the different choices for cycle checking. Note that for Banjo 2,
the choices are a new depth-first search (dfs), a variation of the dfs using an optimization
based on the paper by Oded Shmueli, and the (non-recursive) dfs implementation from version
1. It turns out that all 3 implementations produce relatively similar results. The main surprise
is the fact that the Shmueli optimization is not very effective whenever we allow reversals,
because whenever we need to undo a reversal, we encounter a penalty for having to bring the
p. 37
“tracking numbers” back up to date. However, as we show in the second example on
experimenting with cycle checking, when we restrict our proposer to additions and deletions
only, then the Shmueli-based optimization produces better performance than the other
methods.
Cycle-checking
method
Networks visited
Dfs
Dfs from version 1
8,469,000
8,155,000
Dfs with Shmueli
optimization
8,292,000
Example: Cycle Checking Methods, Revisited
It turns out that by using additions, deletions, and reversals as part of our standard proposers,
the optimization of the Shmueli-variation of the depth-first search is somewhat lost due to the
necessary adjustments every time we discard a reversal and need to revert back to a previous
network. This effect is illustrated by looking at a slightly modified proposer that doesn’t use
reversals. Note that this test requires a simple code change to the internal constant
CONFIG_OMITREVERSALS.
Cycle-checking
method
Networks visited
Dfs
Dfs from version 1
11,255,000
10,465,000
Dfs with Shmueli
optimization
12,141,000
We wanted to present this fact to the user, because it may come in handy when examining
large problems where score computations tend to be expensive, and where it may be desirable
to visit as many networks as possible just to obtain the full effect of the score caching.
p. 38
Banjo contains a few advanced features that may be of interest to some users. These include
expanded post-processing options including the automatic generation of a graph using dot, the
computation of influence scores for each node with a parent, the computation of a consensus
graph, and how to use Banjo to find non-equivalent networks.
Post-Processing Options
All post-processing options that were – prior to Banjo 2 – only controllable via internal
constants, are now fully accessible via input settings. This includes the following:
•
•
•
•
•
Creating the graphical representation of the top-scoring network using dot.
Creating the graphical representation for the consensus graph.
Creating a text file with the commands for creating dot output, for both the topscoring and the consensus graph.
Creating a table representation of the nodes that are part of the consensus
graph, in html format.
Computing the influence scores for the nodes in the top-scoring network.
The createDotOutput setting controls whether dot graphics files are produced for the top scoring
network and the consensus graph (not necessarily a network). To function properly, the user
will have to supply the path to the dot executable, via the fullPathToDotExecutable setting (on a
standard graphViz install, “C:/Program Files/ATT/Graphviz/bin/dot.exe” on a Windows PC).
The computeInfluenceScores setting instructs Banjo to compute the influence scores for the top
scoring network. The computeConsensusGraph setting is for computing the consensus graph
from the N highest-scoring networks. The createConsensusGraphAsHtml setting outputs the
consensus graph together with the N best networks in form of a simple html table, for
convenient review.
At a more granular level, the dotGraphicsFormat setting provides access to a number of the
graphics formats that dot supports (e.g., jpg, imap, vrml, pic, ps, etc). The dotFileExtension
setting adds an extension to the graphics file; Ditto for the htmlFileExtension.
The fileNameForTopGraph and the fileNameForConsensusGraph specify the file names for the
top-scoring and the consensus graph, respectively.
Note that by embedding a token string (@[email protected]), one can specify the time when the
search has started within the result file names. For additional customization, one can change
the default time stamp, by simply providing a valid Java time format for the timeStampFormat
setting.
A special searcher (“Skip”) skips the regular search part, and proceeds directly to the postprocessing. This enables us to keep the search-specific setting values in our settings file
without performing an actual search.
Using dot to Generate a Graph Representing the Found Network
The free GraphViz library from AT&T has powerful capabilities for laying out graphs and
creating images of networks such as the ones we try to discover with Banjo. The dot
p. 39
application is responsible for graph layout within GraphViz. For instructions on the use of
GraphViz (it’s very easy) and for downloading the library, visit http://www.graphviz.org/.
GraphViz dot lays out graphs and then generates images. It takes as input graph files that
have a particular format. For the highest scoring network it finds, Banjo creates a set of
instructions in this format so that they can be passed to dot. You can use this output to create
a picture of the learned network. Just copy the dot commands from the Banjo output, and
save them to a file.
As an example, suppose Banjo creates the following set of dot commands for its highest scoring
network:
digraph abstract {
label = "Banjo Version 1.0\nHigh scoring network, score: -6764.73\nProject:
Banjo_dev\nUser: hjs\nData set: 33-vars-320-cases ";
labeljust="l";
2->17;
4->3;
5->16;
7->1;
8->16;
9->11;
9->13;
9->25;
9->29;
10->1;
11->1;
12->0;
12->8;
13->23;
15->4;
16->14;
17->14;
17->25;
18->24;
19->0;
21->15;
21->16;
22->15;
22->29;
24->7;
25->1;
26->17;
31->11;
}
Suppose that we save the dot commands in the file graph.dot. The program within the
GraphViz package that interprets the dot commands and generates an image is called dot.exe
in Windows. If the program is installed to C:\ATT\Graphviz\bin\, then we can generate an
image by opening the command shell, changing to the directory containing graph.dot, and
typing:
C:\ATT\Graphviz\bin\dot.exe –Tgif graph.dot -o graph.gif
Note that the flag “-Tgif” specifies the type of output graphics file, and “-o” specifies the name
of our output file. Provided that the dot executable is in your path, the corresponding
command in Linux, Unix, or Mac OS X would be:
dot –Tgif graph.dot –o graph.gif
p. 40
The output image file graph.gif then looks like this:
For dynamic Bayesian networks with min. Markov lag equal to the max. Markov lag, we use a
collapsed representation of the network. For all other dynamic Bayesian networks, we display
the nodes in a format that indicates the lag, e.g., node 2 of lag 1 would be denoted by “(lag 1)
2”. Adding additional representations simply requires you to modify the existing code for the
composeDotGraph method in the PostProcessor class.
Banjo 2 expands on the use of dot, by automating the creation of the graphics file. In order to
take advantage of this capability, we need to specify the location of the dot executable in the
settings file, as well as the names of the files where we want Banjo to place its output. Here is
an example:
# Excerpt from the settings file:
createDotOutput =
computeConsensusGraph =
yes
yes
fullPathToDotExecutable =
fileNameForTopGraph =
fileNameForConsensusGraph =
C:/ATT/Graphviz/bin/dot.exe
[email protected][email protected]
[email protected][email protected]
dotGraphicsFormat =
dotFileExtension =
jpg
txt
timeStampFormat = yyyy.MM.dd.HH.mm.ss
Notes:
1. The specified path to the dot executable in fullPathToDotExecutable must be specified
using forward slashes (important for Windows users). If Banjo cannot find dot, no
graphic output will be created, and an error message will be recorded.
2. The file names specified in fileNameForTopGraph and fileNameForConsensusGraph can
contain a (relative) path. All directories that are being specified need to exist in the
p. 41
outputDirectory that Banjo uses for all its output. Both fileNameForTopGraph and
fileNameForConsensusGraph can also contain a time stamp, which is specified by
embedding any of the time stamp tokens (e.g., @[email protected]) anywhere in the file name
(only). A time stamp token in the directory part of the string will cause an error, since
Banjo is not capable of creating directories on the fly.
3. By specifying the (optional) dotGraphicsFormat, you can control the format of the
graphics that dot produces.
4. The (optional) dotFileExtension is used for the accompanying dot text file that contains
the instructions for creating the graphics.
5. The (optional) timeStampFormat lets you specify the exact time stamp that you may
want to use.
Executing Banjo 2 with the above settings would produce 2 graphics files, one each for the top
graph and the consensus graph, with .jpg extension, and 2 structure files with the dot
commands, with .txt extension. The time stamp token would be replaced with the current time
in the specified format (the time “snapshot” is taken when the search starts), e.g., the file
name for the top graph could be _2006.03.31.00.25.28_graph.top.jpg, located in
(myproject/output)/graph.
Another new feature of Banjo version 2 is the variableNames setting, which lets the user
specify a comma-delimited list of labels for each of the variables, to be used in the creation of
the graphics file via dot. Note that the standard Banjo structure output to file still uses the
variable indexes instead of the labels, so that the structures can easily be re-imported into
Banjo.
Influence Scores
An influence score is a metric for representing the degree to which a parent variable’s influence
on a child is monotonic in nature, and if so, in what direction (positive or negative) and what
magnitude. A more detailed description of influence scores, and how they are computed, can
be found in a paper by Yu, et al., in Bioinformatics (2004). For our purposes, it should simply
be noted that a value of zero does not mean that a parent has no influence on a child, but
simply that the influence is not definitively positive or negative in nature, given the observed
data.
Banjo automatically computes and displays the influence scores for the top-scoring network
found in the search. Suppose the top-scoring network is represented by the data below.
33
0 1
1 1
2 1
3 1
4 0
5 1
6 0
7 1
8 1
9 0
10 0
11 1
12 0
13 1
14 1
15 0
16 0
17 1
18 0
19 0
32
5
12
26
16
17
16
16
7
28
27
p. 42
20
21
22
23
24
25
26
27
28
29
30
31
32
2
1
0
0
0
1
0
0
0
0
0
2
0
7 11
16
9
16 26
The influence scores for all nodes that have a parent might be computed and printed out as
follows.
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
Influence
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
score
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
(32,0)
(5,0)
(12,0)
(26,0)
(16,0)
(17,0)
(16,0)
(16,0)
(7,0)
(28,0)
(27,0)
(11,0)
(7,0)
(16,0)
(9,0)
(26,0)
(16,0)
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
->
(0,0)
(1,0)
(2,0)
(3,0)
(5,0)
(7,0)
(8,0)
(11,0)
(13,0)
(14,0)
(17,0)
(20,0)
(20,0)
(21,0)
(25,0)
(31,0)
(31,0)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.5934
0.5500
0.2116
0.0
0.0
0.0
0.0
0.0
In Banjo 2 you can control the computation of influence scores by specifying the new (optional)
setting computeInfluenceScores, with values yes and no.
Consensus Graph
Banjo 2 provides a post processing option of computing a “consensus” graph from a set of
high-scoring networks, by assigning exponentially weighted probabilities to the individual
edges in each of the high-scoring networks, based on the “ranking” of each network in the set.
Note that the consensus graph does not need to be a valid network structure, i.e., a consensus
graph can contain cycles.
p. 43
The 2 pictures above show the consensus graph and the top scoring graph, based on a search
that found 10 top-scoring networks.
As an aid for comparing the consensus graph to the high-scoring networks that it is based on,
we added an option to output all structures in the form of an html table. Column 1 (“Var”)
contains the index for each variable, column 2 (“Consensus”) contains the edges for the
consensus graph, and columns #1 to #N contain the edges for the N top scoring networks.
p. 44
Finding Non-equivalent Networks
The N top-scoring networks obtained by a search in Banjo 1.0 were a set of non-identical
networks, some of which were potentially (actually: likely) equivalent structures. Banjo 2
provides the option to also obtain sets of non-equivalent structures. To access this new
feature, one can specify the optional “nBestNetworksAre” setting. The default value remains
the Banjo 1.0 behavior of “nonIdentical”. The listing of the obtained results will indicate the
selected option.
----------------------------------------------------------------------------- Best 10 non-identical Structures
----------------------------------------------------------------------------Network #1, score: -9685.83, first found at iteration 59177
…
Banjo 2 provides 2 choices for obtaining a set of non-equivalent networks: “nonEquivalent” and
“nonIdenticalThenPruned”.
When “nonEquivalent” is selected, the search will compare potential high-scoring networks for
equivalence whenever a new high-scoring network is encountered. The actual test that we
employ makes use of several “shortcuts” via already available internal data, but still is
extremely time consuming. Due to the large amount of additional computations necessary for
p. 45
comparing 2 networks for equivalence one can expect that the runtime performance for a
“nonEquivalent” search will be an order of magnitude slower than a search that only employs
identity checking.
This performance issue led us to implement the special “hybrid” option, called
“nonIdenticalThenPruned”, which performs a search using the identity checking during the
search, then prunes away any equivalent networks every time a restart or reannealing is
performed, as well as after the search is complete.
----------------------------------------------------------------------------- Best 8 non-identical then pruned Structures
----------------------------------------------------------------------------Network #1, score: -9893.69, first found at iteration 57937
…
There is one inherent drawback to this hybrid approach, though: While the performance of a
“nonIdenticalThenPruned” search is close to the performance of a “nonIdentical” search, the
nonIdenticalThenPruned option will likely not yield a “full” set of N top-scoring networks, but
instead a subset anywhere from 1 to N non-equivalent networks. The reason for this lies in the
“bumping” of non-equivalent networks by a potentially large number of higher scoring (but
possibly – in fact, very likely – equivalent) networks found further into the search process.
p. 46
Using Banjo in Matlab
Since the release of Matlab 6, it has been possible to run Java programs from within Matlab.
With only a little bit of work, we can get Banjo to run in Matlab.
Although Matlab integrates heavily with Java, you may notice a decrease in performance when
running Banjo from within Matlab. This is most likely due to translating the user’s requests
into commands in Java, and thus will probably not dramatically affect the running time of a
long search.
Change to the Correct Directory
The first step is to change to the directory in which Banjo is stored. Open up Matlab and
change the directory to the location of banjo.jar or the edu/ directory if you extracted the jar
file already. For example, if the jar file is located at C:\code\banjo.jar or if you extracted the
jar file to C:\code, you would type:
cd C:\code
Update the classpath
Next, you need to tell Matlab the location of the Banjo class files. If you wish to use the jar file,
simply type:
javaclasspath('banjo.jar');
If you are not using the jar file, you must be in the directory that edu/ is located in. Note that
this must be an absolute pathname.
javaclasspath('C:\code\');
Run the Banjo Program
Now that the classpath has been set, we need to import the classes that we would like to run.
Since we only want to run the Banjo class, we only need to import the application package.
Assuming that the settings file is my.settings.txt, running Banjo just requires a simple call
to the main function:
import edu.duke.cs.bayes.application.*;
Banjo.main('settingsFile=my.settings.txt');
You will notice that nothing initially prints out on the screen. Unfortunately, everything that is
normally printed to the console as the program runs is buffered internally in Matlab. Thus,
the feedback and search results will only be printed after the program has completed running.
p. 47
To provide several options to the Banjo program that would normally be specified on the
command line, you must use an array of strings. An array of strings in Matlab is specified in
brackets {}. For example, if we wished to additionally specify that the user is john, the new
command would be:
Banjo.main( {'settingsFile=my.settings.txt', 'user=john'} );
More options can be specified the same way: with comma separated strings between brackets.
p. 48
Hints and Tips
Computing a Network Score without Running a Search
To have Banjo compute the score of a single, initial network only, simply set the searcher
setting to “Skip”. Specify the initialStructure setting to point to the file that describes the
structure. Then execute Banjo. This will ignore any termination criteria in the settings file,
compute the network score, and then proceed straight to the post-processing options.
By using the command line option you can conveniently keep all the values in your settings file
unchanged; simply add “searcher=Skip initialStructure=filename” to the end of the command
string for running Banjo.
Displaying Debug Info
When encountering a problem while running Banjo as a jar file, the displayDebugInfo setting,
when set to “yes”, can reveal additional information (by displaying the stack trace) without
having to load Banjo into a development environment.
Adding Structure Files to Output
In case any of the structure files, such as intialStructure, mustBePresentEdges or
mustNotBePresentEdges, are specified, they can be displayed (and saved in the results file) as
part of the output, by setting the displayStructures setting to “yes”.
Using Time Stamps in Output Files
Using a time stamp as part of a file name provides a simple mechanism to save the results for
each search in individual files. Simply embed a token (@[email protected]) anywhere in the file
name string, and Banjo will replace it at run time with the current time/date, based on the
(optional) time stamp format specified by the timeStampFormat setting. The default time stamp
format is of the form “yyyy.MM.dd.HH.mm.ss”, where yyyy = year in 4-digits, MM = month (1 to
12), dd = day, HH = hour in 24h notation, mm = minutes, ss = seconds, of the current time and
date. The format follows the Java time stamp format.
The time stamp can be applied to the report file, as well as the file names for the top graph and
the consensus graph.
Memory Info and Performance Tuning
The displayMemoryInfo can come in handy when running Banjo on large data sets that may
reach the computer’s limit of available memory. When set to “yes”, the memory used by Banjo
is being displayed at every regular feedback interval. This makes it a little easier to tune the
useCache setting that controls to a large extend the amount of memory that Banjo uses, above
a minimum amount for running a very basic search.
Banjo uses 2 different types of cache: a basic cache that stores a limited number of (node)
scores in a variable container, and what we call “fastCache”, which stores (node) scores for all
variables of a given maximum parent count in an array for fast retrieval. The fast cache can
handle a maximum parent count of up to 2, and can be used in addition to the basic cache. By
p. 49
default, all cache settings are turned “on”, i.e., useCache is set to fastLevel2. However, for
problems with several hundreds or several thousands of variables the cache use generally has
to get dialed down to keep Banjo from running out of memory.
For additional info about tuning a search, please consult the Setting up your Search section.
Accessing Additional Options via Internal Code Changes
Banjo 2 has replaced most of the internal configuration constants with settings options.
However, there are a few settings that are somewhat in conflict with regular Banjo behavior,
but that can be useful especially to developers.
A prime example is the use of the variableCount setting with values that differ from the
number of data entries supplied by the observations file. When Banjo operates “normally”,
such a difference will be flagged as an error – as a user would come to expect:
[ERROR: Banjo 2, 10/10/06 12:55:35 PM]
The info below has been gathered by the application:
(ObservationsAsMatrix.LoadData) Observation #1 in observations file
'static.data.txt' contains 33 data points instead of the expected 31.
On the other hand, being able to run different search scenarios from the same data set is very
convenient for developers. So we provide a “magic” switch in form of the DEBUG constant in
the BANJO file. When set to true, validation checks such as for the variable count are skipped
in the code.
In addition to altering the Banjo behavior, the DEBUG switch also provides additional internal
trace feedback in several core parts of the code. This makes it easy, e.g., to monitor the
memory use by individual components during setup of a search.
Unique Output File Names
Banjo 2 allows the use of time stamps in various file names for its output, by inserting the
@[email protected] token anywhere in the file name. The use of time-stamped output files comes in
handy when you need to make repeated searches with the same or similar settings, since the
result for each search can be stored in an individual file. If the main result file happens to have
the same name as an earlier result file, Banjo proceeds as in version 1, and appends the new
results to the existing file.
Note: If you don’t use timestamps, but use the automatic generation of graphics files via the
dot program, you want to be aware that your graphics output will be overwritten with the new
file in case you forget to change the graphics file name(s).
The default time stamp format programmed into Banjo is of the form “yyyy.MM.dd.HH.mm.ss”,
where yyyy=year, MM=month (numeric value), dd=day of the month, HH=hours in 24h clock,
mm=minutes, ss=seconds. You can specify your own time stamp format by specifying the
optional timeStampFormat value in the settings file.
In addition to using time stamps in organizing your output, it is also possible to include
(relative) path info as part of a file name (Use forward slash as directory indicator). When used
in this way one can route all graphics files into a separate subdirectory of the input directory,
p. 50
and the text files into another one. Note: Any such path info cannot go to the parent directory,
but has to be a proper subdirectory of the input directory. If you use this feature, you need to
be careful in spelling your file name, because Banjo does not look for any directory info, and
thus does not create any non-existing directories for you. Instead you may end up with an
error message similar to this one:
----------------------------------------------------------------------------(Final Checkpoint) A final check revealed the following issues that were
encountered during Banjo's execution:
----------------------------------------------------------------------------(Post-processing) The 'dot' output could not be created. Detail info:
'(PostProcessor.execute) When the file name 'top/top.graph.2006.10.12.15.31.13'
for the top graph is combined with the Output directory
'data/release2.0/static/output', the result does not form a valid path to a file.
Please make sure that all specified directories exist!'.
Combining Multiple Observations Files
Even though this has been a feature in version 1, it has not been well documented. If your
observations are located in multiple files, you can easily “combine them” for the purpose of a
Banjo search, by simply specifying them in the settings file as a comma-separated list, or using
wildcard notation. And in case you have a set of files in your input directory from which you
want to exclude one or more observations files for a particular search, you can do that by
listing the “to-be-excluded” file with a “-”-prefix. Of course, this means that you cannot name
your observation files with names that start with a “-”-sign.
Note that when setting up a search for a dynamic network, where the data is based on possibly
several independent (time based) experiments, each data file will be treated as a separate
“entity” not related to the data in any other supplied data file (which is the expected behavior,
of course). Consequently, all data from the same experiment for the dynamic case needs to be
supplied in the same file.
This explains the separate feedback in the Banjo results file when examining dynamic
networks, displaying a value for “observations in file” and for “observations used”.
Error Reporting to File
Prior to version 2, Banjo would report errors only to a special errors file, located in the
directory of the Banjo executable. Starting with version 2, errors will also be reported in the
standard results file.
More Flexible Structure Files
The less stringent input processing of Banjo 2 lets us skip lines in a structure file for variables
that don’t have any parents. Any unspecified variable is assumed to have no parent (unless a
dbnMandatoryLag is used for a dynamic bayesnet). In addition, we don’t enforce the order in
which the variables and their parents are listed.
Specifying Observations in Row or Column Format
The default for the observations file is the column oriented format that was already in use in
Banjo 1.0, i.e., each row in the observations file contains a single observation with values for
all of the variables.
p. 51
Banjo 2 adds the option to supply the observations in a “transposed” format, where each row
contains the values of a variable for all observations. To input observations in this format, the
optional setting variablesAreInRows needs to be set to “yes”
Specifying Names for the Variables
To assign names to the used variables, we can use the variableNames setting. Several options
are available:
- Provide a list of the names in white-space delimited format in the settings file.
If you need to use white-space within your variable names, then prefix your list with
“commas:”, followed by the comma delimited list of variable names. [In case you
need a different delimiter, you can change the choice of delimiter by editing a
constant in the code].
- Or, set the value of variableNames to “inFile” , and provide the variable names in the
actual observations file as follows:
For the standard (each observation in a row) observations format, the list of variable
names needs to be supplied as the very first non-blank and non-commented text
line in the file.
For the “transposed” observations format, the first entry of each line needs to be the
name of the variable for that row of data.
Whenever the “inFile” option is to be used, the observationCount needs to be
supplied in the settings file, to protect against unintended side effects from
interpreting the first data row or column mistakenly as variable names.
When supplied in the observations file, the variable names can only be separated by
white-space.
Note that the “inFile” option cannot be used when multiple observations files are specified.
Using a Greedy Searcher with the AllLocalMoves Proposer
When using the AllLocalMoves proposer, especially in conjunction with the Greedy searcher,
one needs to be especially careful when specifying the search parameters. The values of the
parameters minProposedNetworksBeforeRestart and maxProposedNetworksBeforeRestart are
of particular importance, and need to be set based on the size of the underlying problem.
Remember that allLocalmoves will examine in the order of N2 networks in each individual step,
so we want to allow the search to visit a meaningful number of networks before restarting. A
common mistake is to choose values that lead the search to be very shallow with respect to the
restart networks.
It should be obvious that Greedy and AllLocalMoves should only be used when the parameter
restartWithRandomNetwork is set to yes, to avoid repeating a partial search over and over.
We found that in some problems the greedy search converges very fast, but it is always a good
habit to check by running a simulated annealer search to make sure that there is nothing
wrong with the way the greedy search is set up.
p. 52
Troubleshooting
When working with a new software program it is important to know what to do when
something doesn’t work. In this section, we describe the main sources of problems that a user
may encounter when working with Banjo and how to go about finding solutions. The problems
themselves can be categorized into a number of different types, each described in detail below.
When Little Things Don’t Work As Expected
Missing or Invalid Parameter in the Settings File
If a required parameter is not specified for executing a particular search strategy, Banjo’s
internal validation will catch this problem, and display a short report. The Banjo 2 feedback
includes the Banjo header section and project data, as well as a listing of all issues with the
user-supplied settings that it can safely determine. The main types of issues reported are
“missing value of a required setting”, “value out of accepted range”, “invalid choice”, “rule
violation”, and “wrong data type”.
Examples (with error types highlighted in bold face):
----------------------------------------------------------------------------- Banjo
Bayesian Network Inference with Java Objects - Release 2.0
1 Apr 2007 - Licensed from Duke University
- Copyright (c) 2005-2007 by Alexander J. Hartemink
----------------------------------------------------------------------------[ERROR: Banjo 2, 8/7/06 12:51:14 PM]
(Checkpoint) Cannot continue without a valid searcher.
(Invalid setting choice) The supplied value 'default' is not a valid option for
the setting 'searcherChoice'.
----------------------------------------------------------------------------End of error notification
-----------------------------------------------------------------------------
In this case Banjo lets us know that the searcherChoice setting needs to be specified with a
value that is the set of acceptable values (in this case, “simulated annealing”, “greedy”, or
“skip”). While “default” is a valid choice for several of the core objects that are being used by
the search, it cannot be used for specifying the searcher. Note that any setting that expects a
string from a prescribed set of values is now validated in this manner.
Note that Banjo cannot continue its validation (what searcher would it use?), so it indicates
that it stopped at a checkpoint, and it lists the issues that it encountered so far.
----------------------------------------------------------------------------ERROR DETAILS
----------------------------------------------------------------------------- Banjo
Bayesian Network Inference with Java Objects - Release 2.0
1 Mar 2007 - Licensed from Duke University
- Copyright (c) 2005-2007 by Alexander J. Hartemink
-----------------------------------------------------------------------------
p. 53
- Project:
- User:
- Dataset:
- Notes:
----------------------------------------------------------------------------[ERROR: Banjo 2, 8/7/06 12:46:12 PM]
(Data is of unexpected type) The value for the setting 'Min. Markov lag'
(minMarkovLag = '3.5') is not of the correct data type (expected: Integer).
(Missing value of required setting) The value for the setting 'Max. Markov lag'
(maxMarkovLag = '') needs to be supplied.
(Value out of accepted range) The value of the setting
'numberOfIntermediateProgressReports' (='-10') needs to be greater than 0.
(Value out of accepted range) The value of the setting 'maxParentCount' (='-3')
needs to be greater than 0.
----------------------------------------------------------------------------End of error notification
-----------------------------------------------------------------------------
First, Banjo tells us that the minMarkovLag needs to be an integer.
Next, Banjo is complaining about the missing value for the required setting maxMarkovLag. We
need to make sure that the settings file contains a line with this setting name and its value. In
the case of our example, we would add
maxMarkovLag=<value>
with <value> filled in with the desired value (a non-zero integer greater or equal to the
minMarkovLag).
Finally, Banjo also flags several settings with values that cannot be used as supplied: The
numberOfIntermediateProgressReports and the maxParentCount all need to be positive numbers.
Note that Banjo again stops with its validation at this point. Any errors that would be found in
code that would depend on the successful validation of the searcher would not execute until
the flagged issues are resolved.
----------------------------------------------------------------------------- Banjo
Bayesian Network Inference with Java Objects - Release 2.0
1 Apr 2007 - Licensed from Duke University
- Copyright (c) 2005-2007 by Alexander J. Hartemink
----------------------------------------------------------------------------- Project:
- User:
- Dataset:
- Notes:
----------------------------------------------------------------------------[ERROR: Banjo 2, 8/7/06 1:12:58 PM]
(Data is of unexpected type) The value for the setting 'Max. parent count'
(maxParentCount = 'three') is not of the correct data type (expected: Integer).
(Rule violation) The value of the setting 'minMarkovLag' (='3') needs to be less
than or equal to that of setting 'maxMarkovLag' (='1').
In this example Banjo complains that the numeric value for maxParentCount is not a number
(in fact: it tells us that we need to enter an integer), and that the minMarkovLag is not less than
or equal to the maxMarkovLag.
p. 54
Miscellaneous Errors and Warnings
For some errors, e.g. when encountering a cycle in a structure file, you’ll notice that Banjo will
display a complete list of the already validated settings, as well as the loaded “raw” values. This
can assist in the trouble-shooting and crosschecking of the supplied input data.
Structure files, used for example to supply an initial network structure or the
mustBePresentEdges file, need to represent a valid network, i.e., they cannot contain cycles. In
addition, the mustBePresentEdges and mustNotBePresentEdges have to be consistent with
respect to each other (i.e., they cannot overlap).
Finally, for some minor issues with the input data, Banjo may only report a warning. This
happens, e.g., when the number of supplied variable names does not match the number of
variables. In this case Banjo will execute the search after displaying a brief message stating
the issue.
Problems During Post-Processing
As the list of post-processing options grew, we wanted to keep the execution of the different
post-processing code sections as independent from each other as possible. To achieve this we
employ a separate error tracking mechanism for each option, with its own output “checkpoint”.
If Banjo encounters a problem, it will keep track of it, but try to continue with the remaining
post-processing options. A sample output may look similar to this:
----------------------------------------------------------------------------(Final Checkpoint) A final check revealed the following issues that were
encountered during Banjo's execution:
----------------------------------------------------------------------------(Post-processing) The influence scores could not be computed.
Detailed info: '(InfluenceScorer constructor) Error in executing the
InfluenceScorer constructor.'.
('dot' execution) The attempted execution of 'dot' to create the graphics file
'data/release2.0/static/output\consensus.graph.2006.10.10.10.08.21.jpg' did not
succeed. No output has been produced.
Error During Program Execution: Handled by Banjo
Here we encounter a problem that occurred during regular program execution: The Banjo code
encounters (internal) data that it doesn’t expect, and stops the program. A typical example
could be the discovery of a cycle in a user-supplied structure: the optional initial structure file
cannot contain a cycle.
----------------------------------------------------------------------------ERROR DETAILS
----------------------------------------------------------------------------- Banjo
Bayesian Network Inference with Java Objects - Release 2.0
1 Apr 2007 - Licensed from Duke University
- Copyright (c) 2005-06 by Alexander J. Hartemink
----------------------------------------------------------------------------- Project:
banjo static example
- User:
demo
- Dataset:
33-vars-320-observations
- Notes:
static bayesian network inference
-----------------------------------------------------------------------------
p. 55
[ERROR: Banjo 2, 10/10/06 10:39:50 AM]
The info below has been gathered by the application:
The must-be-present edges file 'static.mandatory.with.cycle.str' contains a cycle.
----------------------------------------------------------------------------End of error notification
-----------------------------------------------------------------------------
Many of these errors are easily fixable by correcting the data.
Another type of error may be caused by an unexpected combination of data that is not handled
properly by the Banjo code. This would be a bug in the software and has to be corrected by the
developer.
----------------------------------------------------------------------------ERROR DETAILS
----------------------------------------------------------------------------- Banjo
Bayesian Network Inference with Java Objects - Release 2.0
1 Apr 2007 - Licensed from Duke University
- Copyright (c) 2005-06 by Alexander J. Hartemink
----------------------------------------------------------------------------- Project:
banjo static example
- User:
demo
- Dataset:
33-vars-320-observations
- Notes:
static bayesian network inference
----------------------------------------------------------------------------[ERROR: Banjo 2, 10/10/06 11:00:54 AM]
[Development-related error: This message is usually generated to remind the Banjo
developer to complete or restructure a section of code]
Error details: Please notify the developer that the application provided the
following info:
(BayesNetManager.applyChange) Development issue: Can only apply a BayesNetChange
with READY status, but encountered status value = '1'.
----------------------------------------------------------------------------End of error notification
-----------------------------------------------------------------------------
Hopefully, such instances will be rare.
Error During Program Execution: “Insufficiently Handled” by
Banjo
In this situation, an error is being handled by the Banjo code, but the error message doesn't
provide enough information. Quite possibly this is due to an oversight of the developer, as the
following example illustrates.
----------------------------------------------------------------------------ERROR DETAILS
----------------------------------------------------------------------------- Banjo
Bayesian Network Inference with Java Objects - Release 2.0
1 Apr 2007 - Licensed from Duke University
- Copyright (c) 2005-06 by Alexander J. Hartemink
----------------------------------------------------------------------------- Project:
banjo static example
- User:
demo
- Dataset:
33-vars-320-observations
- Notes:
static bayesian network inference
----------------------------------------------------------------------------[ERROR: Banjo 2, 10/10/06 10:47:46 AM]
p. 56
[Development-related error: This message is usually generated to remind the Banjo developer to
complete or restructure a section of code]
Error details: Please notify the developer that the application provided the following info:
(BayesNetStructure constructor) Developer issue: 'bayesNetStructure' object is of unknown data
type.
----------------------------------------------------------------------------End of error notification
-----------------------------------------------------------------------------
Note that an extended error reporting section can be displayed using the “displayDebugInfo =
stackTrace” setting:
edu.duke.cs.banjo.utility.BanjoException: (BayesNetStructure constructor) Developer issue:
'bayesNetStructure' object is of unknown data type.
at edu.duke.cs.banjo.bayesnet.BayesNetStructure.<init>(BayesNetStructure.java:81)
at
edu.duke.cs.banjo.learner.SearcherSimAnneal.setupSearch(SearcherSimAnneal.java:1327)
at edu.duke.cs.banjo.learner.SearcherSimAnneal.<init>(SearcherSimAnneal.java:722)
at edu.duke.cs.banjo.application.Banjo.runSearch(Banjo.java:110)
at edu.duke.cs.banjo.application.Banjo.<init>(Banjo.java:60)
at edu.duke.cs.banjo.application.Banjo.main(Banjo.java:320)
Finally, a closer, in-depth examination of the faulty code section would reveal that it is missing
the capability to handle the new compact parent set representations.
Running Out of Memory
Let’s say you have installed Banjo, made sure that it runs on the supplied examples, and have
just set up your very first own project, with a large number of variables, and the default
settings for your Java Virtual Machine (JVM). Banjo starts executing, but returns after a few
seconds, with a message that it ran out of memory. This section explains a few easy steps to
get you past these hurdles.
Parameters Affecting Memory Use
The internal storage requirements for Banjo can be considerable, and certain choices of
settings might cause the program to request more memory from the system than is available to
Banjo. The main parameters that influence memory use are:
1.
2.
3.
4.
5.
6.
7.
8.
The number of variables
The number of observations
The maximum Markov lag
The number of values or discrete states per variable
The maximum number of parents that any variable can have
The assigned level for the “fast cache” in the useCache setting
The value of the precomputeLogGamma setting
The amount of memory (possibly implicitly) assigned to the Java VM (this could
be much smaller than the amount of memory that Banjo could reasonably use!).
The First Thing to Check
If your search runs out of memory, here is the first thing to do: check the amount of memory
that is available on the computer that Banjo is running on. E.g., if your computer has 1 GB of
physical memory, and there are no other applications running besides Banjo, then you could
allow Banjo to use most of this memory (remember to leave some memory for the operating
system’s processes). Considering that the default memory that the JVM sets aside for an
application is only 64MB, this is the first parameter you want to adjust.
p. 57
Here is an example on how to instruct the Java Virtual Machine (JVM) to use a much higher
amount of memory than its default setting (which is only 64MB):
java –Xms256m –Xmx900m -jar banjo.jar
The “-Xms256m” flag indicates to the JVM to set aside a minimum of 256 MB of memory for
the execution of Banjo, and “-Xmx900m” flag indicates to the JVM to set aside a maximum of
900 MB of memory. The default values are 2m and 64m respectively, so if you change “-Xms”
to be greater than 64m, you must also specify “-Xmx” to be greater than 64m.
To help you find a useable value for the Xmx setting, Banjo 2 provides a new setting called
displayMemoryInfo that provides a view at its memory use. When its value is set to “yes”, a
memory “snapshot“ is displayed at various intervals during a search, thus enabling some
coarse monitoring, and a basis for tuning the memory parameters.
What if Banjo still Runs out of Memory?
What if increasing the amount of memory made available to Banjo by the JVM has not solved
our problem, and we still get an out-of-memory message. Let’s examine the different
parameters listed above more closely:
•
•
•
Parameters 1 to 3 are intrinsic to the problem at hand, and likely not adjustable (except
maybe for experimentation with the maximum Markov lag).
Parameters 4 and 5 can be somewhat at the user’s discretion, and should be chosen
carefully if the problem at hand poses large memory requirements.
Parameters 6 and 7, on the other hand, are entirely “program-internal” settings that
can be chosen at our discretion. There is a caveat, however: its values not only affect
memory use, but they also have a huge influence on performance. In essence, the more
memory we can allow for parameter 6 and 7, the faster the search runs. Consequently,
it pays dividends to examine the used memory closely and adjust the fastCache level to
the largest value that is still feasible without running out of memory.
To facilitate easier control over the main parameters that control its memory use, Banjo 2
enables direct access to the internal cache settings that are used to store computed results
(mainly, already computed node scores for the Bde metric), via the useCache setting. Especially
the so-called “fastCache” levels (see below) for this setting increase the amount of memory
allocated by Banjo (and being worst for problems with large number of variables). On the other
hand the fastCache also increases the overall performance of Banjo, in terms of the number of
networks that Banjo can visit during a given time. Sometimes experimentation is the only way
for finding the right balance between memory use and search performance.
The values for the useCache setting are
•
•
“none”: This disables any use of the node score cache.
“basic”: This enables the use of a basic hash-based cache, where the available amount
of memory is used for storing the node score and identifying info (i.e., its parents
configuration). Note that this cache is Java-system controlled (i.e., the VM uses
whatever amount of free memory it has available)
p. 58
The remaining useCache options take advantage of a special cache that is built around
the use of arrays for parent configurations of a fixed parent count (currently
implemented are 0, 1, and 2 parents):
•
•
•
“fastCache0”: Caches all node scores when each node has no parents.
“fastCache1”: Caches all node scores when each node has 1 or no parents.
“fastCache2”: Caches all node scores when each node has 2, 1 or no parents.
Obviously, the higher the fastCache number, the larger is the memory requirement. In
particular, the cubic order of the entire 2-parent set leads to huge memory requests, which on
standard desktop machines can only be expected to succeed when the variable count is
relatively small (<100 or so). When examining networks with hundreds or even thousands of
variables, one will likely have to choose a lower fastCache value. Note that with the compact
parent set implementations in Banjo 2, the total memory requirement of a problem with 2500
variables, 2500 observations, min. Markov lag = max. Markov lag, and fastCache1 will now fit
into 500 MB.
Note that the new compact observations implementation is the default in Banjo 2; the older
implementation is still available, but only selectable via constants in the code. When
monitoring Banjo’s memory use, one should know that the actual memory requirements
during the initial setup are about 10-20% above what will be displayed at the first feedback
(i.e., the memory feedback right after the initial settings are shown), because by the time Banjo
displays the initial memory use, it will already have released some large temporary storage that
was used for setting up the observations. Hence, when the nBestNetworks setting is a small
number, it will be unlikely that the application will ever run out of memory during search
execution. However, when nBestNetworks is large, and the number of variables is large, then
the amount of memory necessary for accumulating the top scoring networks can still lead to an
out-of-memory error.
What if Banjo Runs out of Memory well into a Search?
In addition to displaying its memory usage, Banjo 2 implements a graceful exit when it runs
out of memory: even though Banjo cannot continue the execution of a search, it prints an error
message, and all the results obtained so far, then writes the obtained results to the report file.
[Unrecoverable runtime error: out of memory]
Banjo's memory requirements during the search execution exceeded its maximum
alloted memory of 500 mb.
Although the search cannot be continued, Banjo will try to display as much
information as possible about the obtained results.
In addition, Banjo will attempt to complete as many post-processing options as
possible.
----------------------------------------------------------------------------- Best 84 non-equivalent Structures
----------------------------------------------------------------------------Network #1, score: -8835.06, first found at iteration 11992
… (listing of best networks displayed here)
----------------------------------------------------------------------------- Search Statistics
----------------------------------------------------------------------------… (search statistics displayed here)
p. 59
Statistics collected in searcher 'SearcherSimAnneal':
----------------------------------------------------------------------------ERROR DETAILS
----------------------------------------------------------------------------- Banjo
Bayesian Network Inference with Java Objects - Release 2.0
1 Apr 2007 - Licensed from Duke University
- Copyright (c) 2005-06 by Alexander J. Hartemink
----------------------------------------------------------------------------- Project:
banjo static example
- User:
demo
- Dataset:
33-vars-320-observations
- Notes:
static bayesian network inference
----------------------------------------------------------------------------[ERROR: Banjo 2, 10/10/06 9:55:21 AM]
The info below has been gathered by the application:
Out of memory in (SearcherSimAnneal\$LocalSearchExecuter.executeSearch)
----------------------------------------------------------------------------End of error notification
-----------------------------------------------------------------------------
We expect that this feature will likely only come into play when the nBestNetworks setting is
set to a (very large) number, because in this case the storage area for the top scoring networks
can increase substantially as the search progresses. For all other searches, the peak memory
is usually encountered during setup, while loading the observations. The memory
requirements during the subsequent search are lower by more than 10%, and only fluctuate
slightly.
Crash with “System” Error Message
This can be a nightmare problem if you are not familiar with modifying Java code, and Banjo
quits on you unexpectedly: any error like this is what’s generally referred to as a software bug.
Here is a “typical” example (typical of bugs, but hopefully not typical for Banjo): Suppose the
application attempted a division by zero somewhere in its code, the error message would be
forwarded by the error handling mechanism in this way:
----------------------------------------------------------------------------ERROR DETAILS
----------------------------------------------------------------------------- Banjo
Bayesian Network Inference with Java Objects - Release 2.0
1 Apr 2007 - Licensed from Duke University
- Copyright (c) 2005-06 by Alexander J. Hartemink
----------------------------------------------------------------------------- Project:
banjo static example
- User:
demo
- Dataset:
33-vars-320-observations
- Notes:
static bayesian network inference
----------------------------------------------------------------------------[ERROR: Banjo 2, 10/10/06 9:42:26 AM]
Execution has stopped due to the following exception:
'java.lang.ArithmeticException: / by zero'
----------------------------------------------------------------------------End of error notification
-----------------------------------------------------------------------------
To get additional information about the location of the error, we can set the optional Banjo 2
setting “displayDebugInfo” to the value “stacktrace”:
p. 60
[Banjo 2] Execution has stopped due to the following exception:
'java.lang.ArithmeticException: / by zero'
java.lang.ArithmeticException: / by zero
at edu.duke.cs.bayes.structures.BayesNetManager.<init>(BayesNetManager.java:121)
at edu.duke.cs.bayes.learner.SearcherSimAnneal.<init>(SearcherSimAnneal.java:338)
at edu.duke.cs.bayes.application.Banjo.runSearch(CommandLine.java:65)
at edu.duke.cs.bayes.application.Banjo.main(CommandLine.java:198)
Note that the message itself may be much more detailed, since most code sections in Banjo are
designed to trap possible exceptions, and supply some informative feedback. This feedback will
hopefully enable the developer to locate and fix the problem more easily.
Submitting an Error Report
Hopefully you will never see the above message, or any other error message due to an internal
problem with the Banjo code. However, even the best coding practices and testing procedures
sometimes let a “bug” slip through. If you encounter an error in the Banjo code, we ask you to
report it, and we will try to fix it as soon as possible. However, to do so we will need your help –
most importantly, we need as much information about the issue as possible:
1. We ask that you email us a copy of the error message along with a detailed description
of the problem (see 2 below), your settings file, your data file(s), and your result file
to our contact address (Jürgen Sladeczek, email: hjs(at)cs.duke.edu). This information
will (hopefully) enable us to recreate and quickly correct the bug.
2. Please include any additional information that you may know about the problem: is it
repeatable? Does it only show up with certain data or settings, and not with others –
after what change did it first occur? Did it appear after you just upgraded to a new
version of Banjo, a new Java VM, etc? Did you run the jar file or within an IDE? What
version of Java are you using?
p. 61
Appendix A
File Formats
Example of a Minimal Settings File
Banjo 2 implements truly optional settings, in the sense that settings that are not used, or that
can default to internally specified values, don’t need to be part of the settings file anymore.
This leads to very compact setting files. The following is a sample settings file that contains
only the minimal set of settings that need to be specified for executing Banjo 2.
###------------------------------------------------### Input parameter settings file for
###
###
BA
Bayesian
###
N
Network Inference
###
J
with Java
###
O
Objects
###
### Banjo is licensed from Duke University.
### Copyright (c) 2005-06 by Alexander J. Hartemink.
###
### Settings file consistent with version 2.0
###------------------------------------------------###------------------------------------------------### Search component specifications
###------------------------------------------------searcherChoice =
SimAnneal
###------------------------------------------------### Input and output locations
###------------------------------------------------inputDirectory =
observationsFile =
outputDirectory =
reportFile =
data/static/input
static.data.txt
data/static/output
[email protected]@.txt
###------------------------------------------------### We require this only to validate the input
###------------------------------------------------variableCount =
33
###------------------------------------------------### Network structure properties
###------------------------------------------------minMarkovLag =
maxMarkovLag =
equivalentSampleSize =
0
0
1.0
###------------------------------------------------### Stopping criteria
###------------------------------------------------maxTime =
10 h
p. 62
After removing all comments, the minimal settings file becomes:
searcherChoice =
SimAnneal
inputDirectory =
observationsFile =
outputDirectory =
reportFile =
data/static/input
static.data.txt
data/static/output
static.report.txt
variableCount =
33
minMarkovLag =
maxMarkovLag =
equivalentSampleSize =
0
0
1.0
maxTime =
10 h
Example of a Comprehensive Settings File
The following setting file lists all available settings for Banjo 2. Note that when settings don’t
apply, they are simply ignored.
###------------------------------------------------### Input parameter settings file for
###
###
BA
Bayesian
###
N
Network Inference
###
J
with Java
###
O
Objects
###
### Banjo is licensed from Duke University.
### Copyright (c) 2005-06 by Alexander J. Hartemink.
###
### Settings file consistent with version 2.0
###------------------------------------------------###------------------------------------------------### Project information
###------------------------------------------------project =
user =
dataset =
notes =
banjo static example
demo
33-vars-320-observations
static bayesian network inference
###------------------------------------------------### Search component specifications
###------------------------------------------------searcherChoice =
proposerChoice =
evaluatorChoice =
deciderChoice =
SimAnneal
RandomLocalMove
default
default
###------------------------------------------------### Input and output locations
###------------------------------------------------inputDirectory =
observationsFile =
outputDirectory =
reportFile =
data/static/input
static.data.txt
data/static/output
static.report.txt
###------------------------------------------------### We require this only to validate the input
###------------------------------------------------variableCount =
33
p. 63
variablesAreInRows =
observationCount =
###------------------------------------------------### Pre-processing options
###------------------------------------------------discretizationPolicy =
discretizationExceptions =
createDiscretizationReport =
none
withMappedValues
###------------------------------------------------### Network structure properties
###------------------------------------------------minMarkovLag =
maxMarkovLag =
dbnMandatoryIdentityLags =
equivalentSampleSize =
maxParentCount =
defaultMaxParentCount =
0
0
0
1.0
5
8
###------------------------------------------------### Network structure properties, optional
###------------------------------------------------initialStructureFile =
mustBePresentEdgesFile =
mustNotBePresentEdgesFile =
static.mandatory.str
###------------------------------------------------### Stopping criteria
###------------------------------------------------maxTime =
maxProposedNetworks =
maxRestarts =
minNetworksBeforeChecking =
1:00
1000
###------------------------------------------------### Search monitoring properties
###------------------------------------------------nBestNetworks =
5
nbestNetworksAre =
nonIdenticalThenPruned
numberOfIntermediateProgressReports =
10
writeToFileInterval =
0
###------------------------------------------------### Parameters used by specific search methods
###------------------------------------------------### For simulated annealing:
initialTemperature =
coolingFactor =
reannealingTemperature =
maxAcceptedNetworksBeforeCooling =
maxProposedNetworksBeforeCooling =
minAcceptedNetworksBeforeReannealing =
### For greedy:
minProposedNetworksAfterHighScore =
minProposedNetworksBeforeRestart =
maxProposedNetworksBeforeRestart =
restartWithRandomNetwork =
maxParentCountForRestart =
1000
0.9
500
1000
10000
200
1000
3000
5000
yes
3
###------------------------------------------------### Command line user interface options
no
###------------------------------------------------### Post-processing options
###-------------------------------------------------
p. 64
createDotOutput =
yes
computeInfluenceScores =
yes
computeConsensusGraph =
yes
createConsensusGraphAsHtml =
yes
dotGraphicsFormat =
jpg
dotFileExtension =
txt
htmlFileExtension =
html
fullPathToDotExecutable = C:/Program Files/ATT/Graphviz/bin/dot.exe
variableNames = (whitespace-delimited list of labels for the variables)
fileNameForTopGraph =
[email protected]@
fileNameForConsensusGraph =
[email protected]@
timeStampFormat =
yyyy.MM.dd.HH.mm.ss
###------------------------------------------------### Memory management and performance options
###------------------------------------------------precomputeLogGamma =
useCache =
no
fastLevel2
###------------------------------------------------### Misc. options
###------------------------------------------------displayMemoryInfo =
displayStructures =
displayDebugInfo =
yes
yes
stackTrace
Observations File Example
The following is a sample file of observations, based on 20 variables, and values of 0, 1, 2, and
3 for each variable. Individual values are separated by tabs.
1
0
3
2
3
3
0
3
2
1
0
3
#
# this line describes the variable
# var 1 = X, var 2 = Y, ...
#
2 3 3 3 0 1 1 3 2 3 3
1 2 1 1 2 3 2 3 2 2 1
3 0 1 3 2 2 2 1 2 2 1
3 3 0 1 3 2 2 3 3 3 1
mapping:
2
3
3
0
3
3
1
0
2
2
2
3
1
3
2
2
2
3
1
3
2
3
2
3
3
1
3
2
3
3
3
2
3
1
1
2
(... more observations omitted)
3
3
3
3
1
3
2
3
3
3
2
2
1
0
3
1
2
3
3
1
2
3
1
3
2
3
2
3
If a variable is not to be discretized, then it is important that its values are integers and lie in
the range between 0 and CONFIG_MAXVALUECOUNT-1; since the latter constant is set to 5 by
default, this means that you should use values between 0 and 4, inclusive, if not discretizing.
Banjo 2 relaxes the data format to arbitrary white space separation between data entries, as
well as optional comments: any text after the #-symbol is being treated as a comment and
ignored by the data loader.
Structure File: Static Bayesian Network
The following is a sample structure file for a static Bayesian network that illustrates the format
currently being used by Banjo.
p. 65
33
0 0
1 1 7
2 1 20
3 4 5 9 17 22
4 1 26
5 1 27
6 0
7 1 5
8 1 9
9 1 4
10 1 30
11 1 9
12 1 5
13 1 5
14 1 4
15 1 17
16 1 25
17 2 1 2
18 1 30
19 1 22
20 1 8
21 1 9
22 0
23 1 17
24 1 5
25 1 20
26 1 28
27 0
28 1 7
29 1 13
30 1 0
31 1 29
32 0
#
#
#
#
#
Header info about a structure file for a static network
(Banjo 2 format)
number of variables in data set
#
“Expert knowledge”: node has several parents
#
Maybe another comment for variable 27?
Banjo 2 supports an expanded format, allowing comments and white space to be embedded in
the text. Comments are indicated by the # character; all text that follows the # character is
being ignored when the structure is parsed.
The first (non-comment) line specifies the number of variables, 33. Each of the remaining lines
specifies first the id of the variable (starting with 0), then its number of parents, and finally the
id’s of its parents. For example, “3 4 5 9 17 22” on line 5 means that variable with id = 3 has 4
parents, namely variables with id = 5, 9, 17, and 22.
Structure File: Dynamic Bayesian Network
The following is a sample structure file for a dynamic Bayesian network that illustrates the
format currently being used by Banjo.
20
0
1
2
3
4
0:
0:
0:
0:
0:
0
0
0
0
0
#
#
#
#
Header info about a structure file for a static network
(Banjo 2 format)
#
number of variables in data set
1:
1:
1:
1:
1:
2 0 7
1 1
3 0 1 2
2 2 3
2 1 4
p. 66
# comment 1
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0:
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1:
1:
1:
1:
1:
1:
1:
1:
1:
1:
1:
1:
1:
1:
1:
2
1
2
2
3
3
2
1
1
1
1
1
1
1
1
4 5
6
3 7
3 8
5 6 9
8 9 10
10 11
12
13
14
15
16
17
18
19
# comment 2
Again, the first (non-comment) line specifies the number of variables (here: 20). Each of the
remaining lines specifies first the id of the variable (starting with 0), then a block of data for
each of the possible Markov lags of the underlying problem. In our case, the maximum
Markov lag is 1, so there are 2 “blocks” of data, one for Markov lag 0 (indicated by “0:”), and
one for Markov lag 1 (indicated by “1:”). Each of the blocks follows the convention already
described for static Bayesian networks. For example, “2 0: 0
1: 3 0 1 2”, on line 4
means that the variable with id = 2 has 0 parents at Markov lag 0, and 3 parents at Markov lag
1, namely, variables with id = 0, 1, and 2.
Note: by examining the network representation, we see that no variable has a parent of Markov
lag 0. Indeed, the underlying problem specifies the minimum Markov lag to be 1.
Results Output in XML Format
The following is a sample output file in the machine-readable XML format, as introduced in
version 2.2.
<BanjoData>
<BanjoXMLformatVersion>
1.0
</BanjoXMLformatVersion>
<BanjoSettings>
<bestNetworksAre>nonidentical</bestNetworksAre>
<computeConsensusGraph>no</computeConsensusGraph>
<computeInfluenceScores>no</computeInfluenceScores>
<coolingFactor>0.7</coolingFactor>
…
<xmlReportFile>static.xml</xmlReportFile>
<xmlSettingsToExport>all</xmlSettingsToExport>
</BanjoSettings>
<nBestNetworks>
<network>
<networkScore>
-8456.2484
</networkScore>
<networkStructure>
33
0 2 5 25
1 1 14
…
p. 67
30 1 0
31 1 13
32 1 13
</networkStructure>
</network>
… (remaining n-best networks)
<network>
<networkScore>
-8466.0800
</networkScore>
<networkStructure>
33
0 2 8 21
1 1 14
…
30 1 8
31 1 5
32 1 13
</networkStructure>
</network>
</nBestNetworks>
</BanjoData>
Note that the variable count, minimum and maximum Markov lags, and the seed value for the
random number sequence are always exported as part of the BanjoSettings listing.
p. 68
Appendix B
Settings File: Parameter Names and Values
The handling of the setting parameters has changed substantially in version 2. Optional
settings can now be omitted entirely from the settings file, or can have their values omitted.
The spelling of setting names and values in the settings file is now case-independent.
In addition, a number of optional “settings” that used to be controlled via internal constants
can now be configured via the settings file.
Note that for any settings parameter that accepts a set of string values, and that has an
associated default value, the string “default” can be specified in the settings file. The output by
the Banjo application will record the actual value that was being used for program execution,
prefixed by “defaulted to”.
Setting Name
Allowed Values
Explanation
Project information
project
Any string
dataset
Any string
user
Any string
notes
Any string
Provided to users for organizing
their data.
No restriction on user choice
Provided to users for organizing
their data.
No restriction on user choice
Provided to users for organizing
their data.
No restriction on user choice
Provided to users for organizing
their data.
No restriction on user choice
Search component specifications
searcherChoice
•
•
•
•
SimAnneal
Greedy
Skip
default = Skip
proposerChoice
•
•
•
RandomLocalMove
AllLocalMoves
default = RandomLocalMove
•
•
BDe
default = BDe
evalutatorChoice
p. 69
Valid string specifying any of the
available searchers.
Valid string specifying any of the
available proposers that is
compatible with the selected
searcher.
Valid string specifying any of the
available evaluators.
Note that the version 2 BDe
evaluator encompasses all the
functionality of the original BDe
(and literally all the original code is
executed within a new inner class).
deciderChoice
•
•
•
Metropolis
Greedy
default = Metropolis for
SimAnneal, Greedy for
Greedy search
Valid string specifying any of the
available deciders that is
compatible with the selected
searcher.
Input and output locations
inputDirectory
observationsFile
outputDirectory
ReportFile
Any valid directory path; can be
specified as (valid) absolute or
relative path
• Any comma-delimited list of
valid file names
• Optionally, can be specified
using *-wildcard notation;
When using wildcards, files
that are prefixed with a “-”-sign
will be excluded from the (set
of) listed files.
Any valid directory path; can be
specified as (valid) absolute or
relative path
Any valid file name;
• May be specified using a time
stamp token as part of the
name.
Specifies the directory where the
input files for the current data run
are located.
Specifies the name(s) of the file(s)
that contain(s) the observations
data for the underlying problem.
Specifies the directory where the
output files for the current data
run will be placed by Banjo.
The name of the (detailed) report
file.
We require this only to validate the input
variableCount
Integer greater than 1
Specifies the number of variables
of the underlying problem.
(Optional)
variablesAreInRows
If specified as “Yes”, observations
are expected to be in columnoriented format. (in Banjo 1.0 each
row in the observation file was a
single observation, thus
containing N entries, where N is
the number of variables).
Specifies whether observations are
in the transposed format compared
to the original Banjo 1.0 format.
Defaults to the original Banjo 1.0
(i.e., row-oriented) format, when
unspecified.
Pre-processing options
discretizationPolicy
discretizationExceptions
String, of the form qX, iX, or
“none”, where X is a positive
integer less than the max. number
of values that a variable can have.
“q” indicates “quantile”, and “i”
indicates “interval” discretization.
A list of exceptions to the
discretization policy, in the form
“variable index”: “d”, where d is a
valid discretization specification.
p. 70
Specifies the default type of
discretization to be applied to all
variables.
Specifies the discretization to be
applied to a particular variable,
when we want that discretization
to be different from the overall
discretizationPolicy.
createDiscretizationReport
Report options:
• no
• standard (provides an overview
of the mapping)
• withMappedValues ( adds the
actual value counts for each
mapped value)
• withMappedAndOriginalValues
(also adds a list of the original
values and their mapping - This
seems only useful for a small
set of values)
Specifies whether to create a
report on the applied
discretization, as well as the
extend of the report..
Network structure properties
minMarkovLag
Integer greater or equal to 0
maxMarkovLag
Integer greater or equal to 0, and
greater or equal to minMarkovLag
dbnMandatoryIdentityLags
Comma-separated list of integer
values, each between (or equal) to
minMarkovLag and
maxMarkovLag
equivalentSampleSize
Real number greater than 0
Integer greater than 0 and less
than the defaultMaxParentCount
setting.
maxParentCount
defaultMaxParentCount
Note: for maxParentCount>7, the
preComputeLogGamma should
likely be set to “no”, due to the
extensive memory requirements.
Integer greater than 0. If not
specified, defaults to the internal
constant
DEFAULT_MAXPARENTCOUNT
(currently set to 5)
Sets the minimum Markov lag
given by the underlying problem.
Sets the maximum Markov lag
given by the underlying problem.
For specifying entire sets of
parents with Markov lags that
need to be included in all
networks. This is always based on
a-priori information about the
underlying problem (applicable to
dynamic Bayesian networks only).
Note: if you use an initialStructure
or mustBePresentParents file, you
cannot list any parents that are
derived from the bdnMandatoryIdentityLags property (else Banjo
will display an error).
The equivalent sample size.
The maximum number of parents
that a node is allowed to have.
Note that any number greater than
4 or 5 probably won’t make much
sense for the underlying problem.
Generally, numbers greater than 7
will raise the memory
requirements for Banjo
substantially.
default setting for the max. parent
count.
Note: due to the large memory
requirements for larger values of
the max. parent count, an internal
limit of 12 is imposed (as a
constant; edit at your own risk).
Network structure properties, optional
initialStructureFile
Valid structure file
mustBePresentEdgesFile
Valid structure file
p. 71
Specifies edges in initially
prescribed network structure.
Specifies edges that any proposed
network must contain.
mustNotBePresentEdgesFile
Valid structure file
Specifies edges that any proposed
network must not contain.
Stopping criteria
maxTime
A valid “Banjo” time format:
• Integer greater than or equal
to 0 for the time in seconds
• Time in format
days:hr:min:sec, where days,
hr, min, and sec are
integers greater than or equal
to 0; prefixes can be omitted,
so 1:00 means 1 minute.
• A number with a qualifier
(d=days, h=hours,
m=minutes, s=seconds), e.g.
2.5 h for running a search for
the duration of 2.5 hours.
Integer greater than or equal to 0.
maxProposedNetworks
If set to 0, the score for the
initially specified network will be
computed, and Banjo exits.
Integer greater than or equal to 0.
maxRestarts
If set to 0, the score for the
initially specified network will be
computed, and Banjo exits.
The maximum time that a search
is scheduled to run.
The maximum number of search
iterations that a search will run
before Banjo will terminate.
The maximum number of restarts
that a search will run before Banjo
will terminate.
Note: At least 1 of the 3 search
termination criteria (time,
networks, restarts) needs to be
specified. In Banjo 2.0, the search
will run until the first specified
stop criteria is reached.
minNetworksBeforeChecking
Integer greater than 0
The number of networks that
Banjo will “propose” without
checking for any of the “stop”
conditions.
Search monitoring properties
nBestNetworks
Integer greater than 0
•
nBestNetworksAre
•
•
“nonIdentical” (by default
the only choice for nbest=1)
“nonIdenticalThenPruned”
“nonEquivalent”
p. 72
The number of highest scoring
networks to be tracked during a
search.
For “nonIdentical” , the tracked
networks are only checked for
identity, when they are to be added
to the set of n-Best networks.
For n-Best>1,
“nonIdenticalThenPruned”
compares using the identity
comparison to track the highest
scoring networks, then runs an
equivalence check after completing
the search to prune away any
networks that are equivalent to
others in the set. Note that
generally the resulting set of nBest non-equivalent networks after
pruning may contain anywhere
from 1 to n-Best members.
To get exactly n-Best networks in
the result set (assuming that the
search runs long enough, and the
problem parameters are consistent
with the n-Best value), the
“nonEquivalent” setting is
available, which compares any
potentially n-Best network for
equivalence against the current
set.
Due to computational efforts for
equivalence checking, searches
using the
“nonIdenticalThenPruned” setting
are much faster than settings
using the “nonEquivalent” setting.
A valid time format
fileReportingInterval
Defaults to
DEFAULT_FILEREPORTINGNTERVAL
if invalid or not specified
(currently set to 60 seconds)
A valid time format
screenReportingInterval
Defaults to
DEFAULT_SCREENREPORTINGINTER
VAL if invalid or not specified
Determines the interval that Banjo
executes a search between writing
out intermediate reports (which
only includes the n best networks
found) to file.
Determines the interval that Banjo
executes between writing out
feedback to screen.
(currently set to 10 seconds)
Parameters used by specific search methods
For simulated annealing:
initialTemperature
Real greater than 0.
coolingFactor
Real greater than 0
reannealingTemperature
Real greater than 0.
maxAcceptedNetworksBeforeCooling
Integer greater than 0
maxProposedNetworksBeforeCooling
Integer greater than 0
minAcceptedNetworksBeforeReanneal
ing
Integer greater than 0
Sets the initial temperature on
starting a simulated annealing
search.
Sets the cooling factor for a
simulated annealing search.
Sets the reannealing temperature
for “restarting” a simulated
annealing search.
The maximum number of networks
that Banjo will propose before
adjusting the cooling factor.
The maximum number of search
iterations that Banjo will propose
before adjusting the cooling factor.
The minimum number of search
iterations that Banjo will propose
before reannealing.
For greedy:
minProposedNetworksAfterHighScore
Integer greater than 0
p. 73
The minimum number of search
iterations that Banjo will execute
minProposedNetworksBeforeRestart
Integer greater than 0
maxProposedNetworksBeforeRestart
Integer greater than 0, and
greater than
minProposedNetworksBeforeRe
start
restartWithRandomNetwork
“yes” or “no”
(defaults to “yes”)
maxParentCountForRestart
Integer greater than 0 and less
than the internal constant
MAXPARENTCOUNTFORREST
ART (currently set to 5)
after it has found a new high
score, before a restart is initiated.
The minimum number of search
iterations that Banjo will execute
after a restart, before the next
restart is initiated.
The maximum number of search
iterations that Banjo will execute
after a restart, before the next
restart is initiated.
If set to “no”, Banjo will either use
the initial structure file (if
specified) or the empty network as
the starting point for each restart.
If set to “yes”, Banjo will compute
a random network as the starting
point for each restart. Note that
maxParentCountForRestart limits
the number of parents for each
node for the restart network.
A constraint on the number of
parents in any network that Banjo
computes as a restart network
(when restartWithRandomNetwork
is set to “yes”)
Command line user interface options
“yes” or “no”
If “yes” is chosen, Banjo will echo
print the selected settings values,
and pause for user confirmation
before executing the search.
“yes” or “no”
(defaults to “no”)
When set to “yes”, uses the dot
executable (if specified) to
automatically create a graphics file
of the top scoring and/or the
consensus graph.
Post-processing options
createDotOutput
fullPathToDotExecutable
dotGraphicsFormat
dotFileExtension
The valid (absolute) path to the
dot executable. Example:
“C:/Program Files
/ATT/Graphviz/bin/dot.exe”
Windows users should note
the forward slashes in the
path.
Supported are the format
options for dot’s –Tformat as
listed in the “Drawing graphs
with dot” manual, Feb. 4 2002,
most notably:
•
jpg
•
png
•
gif
•
ps
Defaults to png.
Customarily a 3-letter short
cut for the file type (don’t
p. 74
The location of the dot executable
as part of the GraphViz package.
Used by Banjo to create graphics
output of the top scoring network.
For specifying the graphics format
to the dot graphics program.
The dot commands are in simple
text format, so the dot output file
fileNameForTopGraph
computeConsensusGraph
fileNameForConsensusGraph
createConsensusGraphAsHtml
htmlFileExtension
computeInfluenceScores
specify the period – if you do,
you’ll end up with 2).
Defaults to “txt”.
Any valid file name that Banjo
will use for the top graph.
The file name will be appended
to the output directory, after
any time stamp token has
been replaced with an actual
time stamp.
“yes” or “no”
Only relevant when Banjo
computes the top N best
networks, N>1.
(defaults to “no”)
Any valid file name that Banjo
will use for the consensus
graph.
The file name will be appended
to the output directory, after
any time stamp token has
been replaced with an actual
time stamp.
“yes” or “no”
Only relevant when Banjo
computes the top N best
networks, N>1, and when the
computeConsensusGraph flag
is set to “yes”.
(defaults to “no”)
The extension of the file for the
consensus graph and the N
best networks. Typically “htm”
or “html”.
“yes” or “no”
(defaults to “no”)
is a basic text file.
Used as the file name, with the
appropriate extension added, for
the top graph’s graphics and dot
file output.
When set to “yes”, Banjo computes
the consensus graph from the top
N highest scoring networks.
Used as the file name, with the
appropriate extension added, for
the consensus graph’s graphics
and dot file output.
When set to “yes”, Banjo creates
an html table of the consensus
graph and the top N best
networks.
Appended to the consensus file
name, when the html table is
created.
When set to “yes”, Banjo computes
the influence scores for the nodes
of the top-scoring network.
(Optional)
variableNames
Variable names can be listed
in the settings file in
whitespace-delimited format.
Alternatively, the list can be
prefixed with the token
“commas:”, after which the
variable names can be listed in
comma-separated format (thus
allowing the use of whitespace
in the names)
If variablesAreInRows is NOT
set to “yes”, the variableNames
setting can also be specified as
“inFile”, and the actual
variable names can then be
listed in the observations file
instead, as the first noncomment text line, in
whitespace-delimited format.
p. 75
Used for specifying (optional)
names for the variables. Note that
the names for all the variables
must be listed, in numerical order
of the variable indexes.
XML-processing options (first introduced in version 2.2)
(Optional, unless
XMLinputFiles are specified)
XMLreportFile
XMLoutputDirectory
For specifying the name of the
XML output file.
String representing a valid file
name on the system that is
executing Banjo.
When left unspecified, no XML
output will be written.
(Optional) The valid (absolute)
path to the XML-formatted
input files.
For specifying the path of the XML
output file(s).
XMLinputFiles
(Optional) List of XMLformatted files that contain
results, e.g., from a search
execution on a cluster
environment.
Any file with a “-“ prefix will be
excluded from the processing.
XMLinputDirectory
(Optional) The valid (absolute)
path to the XML-formatted
input files.
Used to combine the results of all
listed (XML-formatted) files into a
single result file.
When specified, no search will be
performed. Instead Banjo will
combine the n-best networks in
the listed XML input files into a
single set of n-best networks, and
write the results into a single XML
result file.
For specifying the path of the XML
input file(s).
Used to add any number of
settings and their values to the
XML output file.
(Optional)
•
•
XMLsettingsToExport
•
•
Comma-delimited list of
setting names
“All” for including all
settings known to the
current version of banjo
Setting names with a
single “-“ prefix, to indicate
the exclusion of any such
marked setting
Defaults to “no setting
being listed”
Note: the order in which the
settings are listed is not important.
First all settings “to be listed” and
“to omitted” are collected into a set
each, then the set of settings to be
omitted is removed from the set of
settings to be listed.
Unknown settings are simply
ignored, so spelling is important
(no feedback is provided when
invalid setting names are
encountered).
Extra spaces are ignored at
beginning and end of settings
names.
Capitalization is not important (as
usual with Banjo settings).
Miscellaneous options
p. 76
timeStampFormat
Any valid time stamp format.
Please consult the Java
documentation for more
details. Banjo simply takes the
provided string and validates it
as a time stamp, then uses the
format when replacing its time
stamp token(s) in various files.
Defaults to the format
“yyyy.MM.dd.HH.mm.ss”.
“yes” or “no”
(defaults to “yes”)
precomputeLogGamma
useCache
Note that this option uses a lot
of memory when the
maxParentCount is above 7
parents.
displayStructures
displayDebugInfo
The precomputeLogGamma setting
is a tuning option that affects the
memory requirements of Banjo.
•
•
•
•
•
None
Basic
FastLevel0
FastLevel1
FastLevel2
The useCache setting is the main
tuning option for the runtime
memory requirements of Banjo.
This is especially an issue for very
large networks: for several
thousands of nodes, it is usually
best to set the cache to “none”.
•
•
•
•
•
dfs
dfsWithShmueli
dfsOrig (to be deprecated)
default = dfsWithShmueli
(Developer note: when the
old “parent set as matrix”
classes are used, then
(only) the old bfs and dfs
implementations are
available; both are
scheduled to be
deprecated in a
maintenance release)
Valid string specifying any of the
available cycle checkers.
Note: dfsOrig is only provided for
testing, and generally slower than
the other choices. For most
problems, dfsWithShmueli can be
expected to be the fastest, with
increasing performance further
into a search (due to the kept
“history”).
cycleCheckerChoice
displayMemoryInfo
For specifying a time format for
use in the time stamp token
replacement. Banjo will replace
any time stamp token with the
actual time stamp obtained at the
start of the search.
Useful for keeping output data
organized.
The valid time stamp tokens are
any of the following:
"@[email protected]", "@time [email protected]",
"@[email protected]", "@[email protected]", and
"@[email protected]", which can be inserted at
any location into a file name.
“yes” or “no”
(defaults to “yes”)
“yes” or “no”
(defaults to “no”)
•
•
no
stacktrace
Integer greater than 0 and less
than the internal constant
Defaults to 1.
p. 77
Adds the amount of memory
currently used by Banjo to the
output. Useful for fine-tuning the
useCache setting.
For selecting whether to display
the initial structure, the must-bepresent-parents and the must-notbe-present-parents, as loaded from
their respective files.
Useful in case of a Banjo bug, to
find the location of the offending
code, to pass on to a developer.
Useful when running Banjo on a
multiprocessor machine: executes
the specified number of threads in
parallel.
String that is appended to the
beginning of the results file
name. The tokens @[email protected]
or @[email protected] can be used to
identify the individual threads.
Defaults to the string
“thread=i_”, where i is the
number (ID) of the thread.
Used to place the search results
obtained by the individual threads
(esp. for multi-processor hardware)
into separate result files.
Note that the combined n-Best set
of networks is placed in the
standard results file.
(Development settings)
Used to put Banjo into a special
“test” mode with (as of version 2.2)
the following properties:
(Optional)
seedForStartingSearch
Integer greater or equal to 0.
Defaults to “no value”, thus
resulting in a randomly
selected seed.
p. 78
When a valid value for
seedForStartingSearch is provided,
then the random number sequence
used by Banjo will be seeded with
the supplied seed, resulting in
repeatable results.
Note: the default seed value for the
random number sequence is based
on the system time. (Developers:
refer to the BanjoRandomNumber
class for details)
Appendix C
References
Papers
•
G.F. Cooper and E. Herskovits (1992): A Bayesian Method for the Induction of
Probabilistic Networks from Data. Machine Learning, 9, pp. 309- 347.
•
Hartemink, A. (2001) “Principled Computational Methods for the Validation and
Discovery of Genetic Regulatory Networks.” Massachusetts Institute of Technology,
Ph.D. dissertation.
Chapter 3 has a detailed introduction to discretization techniques. Later chapters
describe the use of simulated annealing for network inference.
•
Hartemink, A., Gifford, D., Jaakkola, T., & Young, R. (2002) “Combining Location and
Expression Data for Principled Discovery of Genetic Regulatory Networks.” In Pacific
Symposium on Biocomputing 2002 (PSB02), Altman, R., Dunker, A.K., Hunter, L.,
Lauderdale, K., & Klein, T., eds. World Scientific: New Jersey. pp. 437–449.
Describes the background for the concept of consensus graphs (model averaging).
•
Yu, J., Smith, V., Wang, P., Hartemink, A., & Jarvis, E. (2002) “Using Bayesian Network
Inference Algorithms to Recover Molecular Genetic Regulatory Networks.” International
Conference on Systems Biology 2002 (ICSB02), December 2002.
Introduces the concept of influence scores.
•
Yu, J., Smith, V., Wang, P., Hartemink, A., & Jarvis, E. (2004) “Advances to Bayesian
Network Inference for Generating Causal Networks from Observational Biological Data.”
Bioinformatics, 20, December 2004. pp. 3594–3603.
•
David Maxwell Chickering (2002) “Learning Equivalence Classes of Bayesian Network
Structures.” The Journal of Machine Learning, 2, March 2002. pp. 445-498.
•
Oded Shmueli (1983) “Dynamic Cycle Detection.” Information Processing Letters, 17, 8
November 1983. pp. 185-188.
Books and Manuals
•
“Drawing graphs with dot”, Emden Gansner et al., 2002. Online document, from the
AT&T Lab’s GraphViz library. Can be found, together with several other useful guides,
at http://www.research.att.com/sw/tools/graphviz/dotguide.pdf.
•
Hang T. Lau, “A numerical library in Java for scientists and engineers”, 2004, Chapman
& Hall/CRC, book and CD. A numeric library in Java that we used for the computation
of the Gamma function. Details at www.crcpress.com.
p. 79
Appendix D
Project Background and Acknowledgements
Dear User,
Banjo owes much to the work of others. I am obviously grateful to the many who went before
in developing the foundations for Bayesian networks, the BDe scoring metric, structure
inference, and heuristic search algorithms. So many have contributed mightily to the field that
I could not possibly name them all, but I would like to offer special thanks to Judea Pearl,
Wray Buntine, Peter Sprites, Greg Cooper, Ed Herskovits, David Heckerman, Max Chickering,
Christopher Meek, Nir Friedman, Daphne Koller, Kevin Murphy, and especially Tommi
Jaakkola for their contributions to my own learning.
Regarding network inference code development, things probably first began when Tommi
Jaakkola shared a small snippet of C code with me that scored a discrete network using the
BDe metric, which I parlayed into a Matlab application for scoring and searching for Bayesian
networks in the summer of 1999. After using this for a while, I wanted to make the application
speedier, so I began to reimplement it carefully in C. In the fall of 2000, Tomi Silander was
kind enough to share with me some C and Perl code from the B-Course web site, which I
merged with my own code to produce an application that served as the basis of parts of my
dissertation in the spring of 2001. Upon moving to Duke University in the fall of 2001, I
shared this C code base with a graduate student, Allister Bernard, and a postdoc in Erich
Jarvis’s lab, Dr. Anne Smith. Together, we used this C code to do research leading to a
number of papers, but since the code was neither modular nor well-documented, I started to
imagine a new code base that would be significantly easier for others to use, but also more
efficient than the existing C code. A Duke undergraduate, Daniel Greenblatt, made the first
attempt by porting the code to C++ one semester, but after he graduated that effort was
abandoned. Meanwhile, a student, Jing Yu, used my C code as the basis for her own C++
application, which she and I went on to improve in a number of interesting directions,
including the development of influence scores.
All this background sets the stage for the emergence of Banjo, which was created from scratch
in Java, and designed from the ground up to be modular and efficient, based on all of these
prior experiences. I conceived a plan for a new Java code base, and concocted a number of
schemes for improving the efficiency of network inference. I then hired Jürgen Sladeczek to
produce the implementation, and he and I consulted with Allister Bernard and Jing Yu during
the specification period to ensure that the design was sound. I owe a lot to Jürgen who worked
hard to ensure that the code incorporated good software engineering principles related to
things like interfaces and abstraction, error handling through exceptions, validation of values,
proper file I/O, the use of global constants, and the like. In recent days, Joshua Robinson and
I tried to break the code wherever possible, squashing a number of bugs in the process, but
perhaps some remain—please do contact us if you find any and we’ll do our best to resolve
them in later versions. Jürgen created the initial drafts of the User and Developer Guides, and
these were expanded and improved by Jürgen, Josh, and me. Josh created the website for
Banjo and the non-commercial download mechanism. Finally, I’d like to thank Henry Berger
in the Office of Science and Technology who is helping to coordinate commercial use license
agreements. I hope Banjo serves you well: enjoy!
Regards,
Alex Hartemink
p. 80
Index
Banjo
Core components ...................................................5
Core objects .........................................................24
Executing ...............................................................7
Explanation of components .................................23
Using Compute Cluster .........................................9
Using in Matlab ...................................................47
Using Multiple Threads .........................................8
Banjo Files
banjo.jar .................................................................7
Testing the installed files .......................................7
Bayesian Network
Dynamic ..............................................................16
Searching for static ..............................................11
Bayesian Networks
Finding non-equivalent ........................................45
Cache
Basic ....................................................................77
FastLevel0 ...........................................................77
FastLevel1 ...........................................................77
FastLevel2 ...........................................................77
None ....................................................................77
Compute cluster .........................................................9
Consensus Graph .....................................................43
Cycle checker
default ..................................................................77
dfs ........................................................................77
dfsOrig .................................................................77
dfsWithShmueli ...................................................77
Cycle Checker .........................................................25
Data File
Using variableNames ...........................................10
Data Formats
Observations ........................................................10
Using variablesAreInRows ..................................10
Decider
Metropolis............................................................70
Decider ....................................................................26
default settings .....................................................70
Greedy .................................................................70
Discretization ...........................................................30
Settings ................................................................13
Discretization Policy
i (interval) ............................................................70
q (quantile) ..........................................................70
p. 81
Discretization Report
no .........................................................................71
standard ...............................................................71
withMappedAndOriginalValues ..........................71
withMappedValues ..............................................71
Distributed search ......................................................9
Equivalence Checker ...............................................26
Performance considerations .................................45
Error
‘System’ crash .....................................................60
Cycle in mandatory edges file .............................56
Data of unexpected type ......................................54
Developer-related ................................................56
Display debug info ..............................................57
Insufficient memory ............................................57
Invalid setting choice ...........................................53
Missing value of required setting ........................54
parameters affecting memory use ........................57
Rule violation ......................................................54
Submitting a report ..............................................61
Value out of accepted range ................................54
When ‘expected’..................................................55
When ‘unexpected’..............................................56
Error Reporting........................................................51
Errors
and warnings........................................................55
During post-processing ........................................55
Evaluator
default setting ......................................................69
Evaluator .................................................................25
BDe ......................................................................69
Experimenting with
Cache ...................................................................33
Comparing searchers ...........................................36
Cycle checking ....................................................37
Discretization .......................................................32
Equivalence checking ..........................................37
MaxParentCount ..................................................33
precomputeLogGamma .......................................33
File Formats
Observations file ..................................................65
Settings file ..........................................................62
Getting Started ...........................................................6
Graph
dot output .............................................................41
Generating with dot .............................................39
GraphViz library ......................................................39
Influence Scores ......................................................42
Memory
First things to check.............................................57
Parameters affecting ............................................57
Running out of .....................................................57
N-best networks
nonEquivalent ......................................................72
nonIdenticalThenPruned......................................72
N-best networks
nonIdentical .........................................................72
Observations
Combining multiple files .....................................51
File Format ..........................................................65
Row vs column format ........................................51
Using variable names...........................................52
Options
Accessing via code changes ................................50
Post-processing ....................................................39
Output
Discretization report ............................................13
Explanation ..........................................................15
Explanation for dynamic network .......................21
Graph represetation .............................................16
Graphic representation.........................................22
Memory info ........................................................49
Results for dynamic network ...............................18
Sample for static network ....................................12
Search results .......................................................14
Unique file names ................................................50
Using time stamps ...............................................49
Proposer ...................................................................25
AllLocalMoves ....................................................69
default setting ......................................................69
RandomLocalMove .............................................69
Requirements .............................................................6
Results
Explanation ..........................................................15
Explanation for dynamic network .......................21
file output in XML format ...................................67
Search
Setting up .............................................................28
Tuning memory use .............................................28
Tuning performance ............................................29
Searcher ...................................................................24
default setting ......................................................69
Greedy .................................................................69
SimAnneal ...........................................................69
Skip ......................................................................69
Searching
Feedback ..............................................................11
Setting
computeConsensusGraph ....................................75
computeInfluenceScores ......................................75
coolingFactor .......................................................73
createConsensusGraphAsHtml ............................75
p. 82
createDiscretizationReport ..................................71
createDotOutput ..................................................74
cycleCheckerChoice ............................................77
dataset ..................................................................69
dbnMandatoryIdentityLags .................................71
deciderChoice ......................................................70
defaultMaxParentCount .......................................71
discretizationExceptions ......................................70
discretizationPolicy .............................................70
displayDebugInfo ................................................77
displayMemoryInfo .............................................77
displayStructures .................................................77
dotFileExtension ..................................................74
dotGraphicsFormat ..............................................74
equivalentSampleSize ..........................................71
evalutatorChoice ..................................................69
fileNameForConsensusGraph ..............................75
fileNameForTopGraph ........................................75
fileReportingInterval ...........................................73
fullPathToDotExecutable ....................................74
htmlFileExtension ................................................75
initialStructureFile ...............................................71
initialTemperature ...............................................73
inputDirectory......................................................70
maxAcceptedNetworksBeforeCooling ................73
maxMarkovLag ...................................................71
maxParentCount ..................................................71
maxParentCountForRestart .................................74
maxProposedNetworks ........................................72
maxProposedNetworksBeforeCooling ................73
maxProposedNetworksBeforeRestart ..................74
maxRestarts .........................................................72
maxTime ..............................................................72
minAcceptedNetworksBeforeReanneal ...............73
minMarkovLag ....................................................71
minNetworksBeforeChecking .............................72
minProposedNetworksAfterHighScore ...............73
minProposedNetworksBeforeRestart ..................74
mustBePresentEdgesFile .....................................71
mustNotBePresentEdgesFile ...............................72
nBestNetworks ....................................................72
nBestNetworksAre ..............................................72
notes.....................................................................69
observationsFile ...................................................70
outputDirectory....................................................70
precomputeLogGamma .......................................77
project ..................................................................69
proposerChoice ....................................................69
reannealingTemperature ......................................73
reportFile .............................................................70
restartWithRandomNetwork ................................74
screenReportingInterval ......................................73
searcherChoice ....................................................69
testMode ..............................................................78
timeStampFormat ................................................77
useCache ..............................................................77
user ......................................................................69
variableCount ......................................................70
variableNames .....................................................75
variablesAreInRows ............................................70
XMLinputDirectory .............................................76
XMLinputFiles ....................................................76
XMLoutputDirectory ...........................................76
XMLreportFile ....................................................76
XMLsettingsToExport .........................................76
Setting value
AllLocalMoves ....................................................69
Basic ....................................................................77
BDe ......................................................................69
default for cycle checker......................................77
default for evaluator ............................................69
default for proposer .............................................69
default for searcher ..............................................69
defaults for decider ..............................................70
dfs ........................................................................77
dfsOrig .................................................................77
dfsWithShmueli ...................................................77
FastLevel0 ...........................................................77
FastLevel1 ...........................................................77
FastLevel2 ...........................................................77
Greedy (decider) ..................................................70
Greedy (searcher) ................................................69
i (interval) ............................................................70
inFile (specifying variableNames) .......................11
infile (variableNames) .........................................52
Metropolis............................................................70
p. 83
None ....................................................................77
nonEquivalent ......................................................72
nonIdentical .........................................................72
nonIdenticalThenPruned......................................72
q (quantile) ..........................................................70
RandomLocalMove .............................................69
SimAnneal ...........................................................69
Skip ......................................................................69
stackTrace ............................................................57
time format ..........................................................72
Settings
Component options ..............................................27
Discretization .......................................................30
Display debug info ..............................................57
File format ...........................................................62
Missing or invalid parameter ...............................53
Names and values ................................................69
Using variableNames ...........................................10
Using variableNames ...........................................52
Using variablesAreInRows ..................................10
Using XMLinputDirectory ..................................10
using XMLinputFiles...........................................10
Using XMLoutputDirectory ..................................9
Using XMLreportFile ............................................9
Using XMLsettingsToExport ................................9
Structure File
File format, dynamic............................................66
File format, static .................................................65
Time Stamps ............................................................49
XML format
sample output file ................................................67