/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* SimulatedAnnealing.java
* Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.bayes.net.search.global;
import weka.classifiers.bayes.BayesNet;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* This Bayes Network learning algorithm uses the general purpose search method of simulated annealing to find a well scoring network structure.<br/>
* <br/>
* For more information see:<br/>
* <br/>
* R.R. Bouckaert (1995). Bayesian Belief Networks: from Construction to Inference. Utrecht, Netherlands.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @phdthesis{Bouckaert1995,
* address = {Utrecht, Netherlands},
* author = {R.R. Bouckaert},
* institution = {University of Utrecht},
* title = {Bayesian Belief Networks: from Construction to Inference},
* year = {1995}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -A <float>
* Start temperature</pre>
*
* <pre> -U <integer>
* Number of runs</pre>
*
* <pre> -D <float>
* Delta temperature</pre>
*
* <pre> -R <seed>
* Random number seed</pre>
*
* <pre> -mbc
* Applies a Markov Blanket correction to the network structure,
* after a network structure is learned. This ensures that all
* nodes in the network are part of the Markov blanket of the
* classifier node.</pre>
*
* <pre> -S [LOO-CV|k-Fold-CV|Cumulative-CV]
* Score type (LOO-CV,k-Fold-CV,Cumulative-CV)</pre>
*
* <pre> -Q
* Use probabilistic or 0/1 scoring.
* (default probabilistic scoring)</pre>
*
<!-- options-end -->
*
* @author Remco Bouckaert (rrb@xm.co.nz)
* @version $Revision: 1.6 $
*/
public class SimulatedAnnealing
extends GlobalScoreSearchAlgorithm
implements TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -5482721887881010916L;
/** start temperature **/
double m_fTStart = 10;
/** change in temperature at every run **/
double m_fDelta = 0.999;
/** number of runs **/
int m_nRuns = 10000;
/** use the arc reversal operator **/
boolean m_bUseArcReversal = false;
/** random number seed **/
int m_nSeed = 1;
/** random number generator **/
Random m_random;
/**
* Returns an instance of a TechnicalInformation object, containing
* detailed information about the technical background of this class,
* e.g., paper reference or book this class is based on.
*
* @return the technical information about this class
*/
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
result = new TechnicalInformation(Type.PHDTHESIS);
result.setValue(Field.AUTHOR, "R.R. Bouckaert");
result.setValue(Field.YEAR, "1995");
result.setValue(Field.TITLE, "Bayesian Belief Networks: from Construction to Inference");
result.setValue(Field.INSTITUTION, "University of Utrecht");
result.setValue(Field.ADDRESS, "Utrecht, Netherlands");
return result;
}
/**
*
* @param bayesNet the bayes net to use
* @param instances the data to use
* @throws Exception if something goes wrong
*/
public void search (BayesNet bayesNet, Instances instances) throws Exception {
m_random = new Random(m_nSeed);
// determine base scores
double fCurrentScore = calcScore(bayesNet);
// keep track of best scoring network
double fBestScore = fCurrentScore;
BayesNet bestBayesNet = new BayesNet();
bestBayesNet.m_Instances = instances;
bestBayesNet.initStructure();
copyParentSets(bestBayesNet, bayesNet);
double fTemp = m_fTStart;
for (int iRun = 0; iRun < m_nRuns; iRun++) {
boolean bRunSucces = false;
double fDeltaScore = 0.0;
while (!bRunSucces) {
// pick two nodes at random
int iTailNode = Math.abs(m_random.nextInt()) % instances.numAttributes();
int iHeadNode = Math.abs(m_random.nextInt()) % instances.numAttributes();
while (iTailNode == iHeadNode) {
iHeadNode = Math.abs(m_random.nextInt()) % instances.numAttributes();
}
if (isArc(bayesNet, iHeadNode, iTailNode)) {
bRunSucces = true;
// either try a delete
bayesNet.getParentSet(iHeadNode).deleteParent(iTailNode, instances);
double fScore = calcScore(bayesNet);
fDeltaScore = fScore - fCurrentScore;
//System.out.println("Try delete " + iTailNode + "->" + iHeadNode + " dScore = " + fDeltaScore);
if (fTemp * Math.log((Math.abs(m_random.nextInt()) % 10000)/10000.0 + 1e-100) < fDeltaScore) {
//System.out.println("success!!!");
fCurrentScore = fScore;
} else {
// roll back
bayesNet.getParentSet(iHeadNode).addParent(iTailNode, instances);
}
} else {
// try to add an arc
if (addArcMakesSense(bayesNet, instances, iHeadNode, iTailNode)) {
bRunSucces = true;
double fScore = calcScoreWithExtraParent(iHeadNode, iTailNode);
fDeltaScore = fScore - fCurrentScore;
//System.out.println("Try add " + iTailNode + "->" + iHeadNode + " dScore = " + fDeltaScore);
if (fTemp * Math.log((Math.abs(m_random.nextInt()) % 10000)/10000.0 + 1e-100) < fDeltaScore) {
//System.out.println("success!!!");
bayesNet.getParentSet(iHeadNode).addParent(iTailNode, instances);
fCurrentScore = fScore;
}
}
}
}
if (fCurrentScore > fBestScore) {
copyParentSets(bestBayesNet, bayesNet);
}
fTemp = fTemp * m_fDelta;
}
copyParentSets(bayesNet, bestBayesNet);
} // buildStructure
/** CopyParentSets copies parent sets of source to dest BayesNet
* @param dest destination network
* @param source source network
*/
void copyParentSets(BayesNet dest, BayesNet source) {
int nNodes = source.getNrOfNodes();
// clear parent set first
for (int iNode = 0; iNode < nNodes; iNode++) {
dest.getParentSet(iNode).copy(source.getParentSet(iNode));
}
} // CopyParentSets
/**
* @return double
*/
public double getDelta() {
return m_fDelta;
}
/**
* @return double
*/
public double getTStart() {
return m_fTStart;
}
/**
* @return int
*/
public int getRuns() {
return m_nRuns;
}
/**
* Sets the m_fDelta.
* @param fDelta The m_fDelta to set
*/
public void setDelta(double fDelta) {
m_fDelta = fDelta;
}
/**
* Sets the m_fTStart.
* @param fTStart The m_fTStart to set
*/
public void setTStart(double fTStart) {
m_fTStart = fTStart;
}
/**
* Sets the m_nRuns.
* @param nRuns The m_nRuns to set
*/
public void setRuns(int nRuns) {
m_nRuns = nRuns;
}
/**
* @return random number seed
*/
public int getSeed() {
return m_nSeed;
} // getSeed
/**
* Sets the random number seed
* @param nSeed The number of the seed to set
*/
public void setSeed(int nSeed) {
m_nSeed = nSeed;
} // setSeed
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(3);
newVector.addElement(new Option("\tStart temperature", "A", 1, "-A <float>"));
newVector.addElement(new Option("\tNumber of runs", "U", 1, "-U <integer>"));
newVector.addElement(new Option("\tDelta temperature", "D", 1, "-D <float>"));
newVector.addElement(new Option("\tRandom number seed", "R", 1, "-R <seed>"));
Enumeration enu = super.listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -A <float>
* Start temperature</pre>
*
* <pre> -U <integer>
* Number of runs</pre>
*
* <pre> -D <float>
* Delta temperature</pre>
*
* <pre> -R <seed>
* Random number seed</pre>
*
* <pre> -mbc
* Applies a Markov Blanket correction to the network structure,
* after a network structure is learned. This ensures that all
* nodes in the network are part of the Markov blanket of the
* classifier node.</pre>
*
* <pre> -S [LOO-CV|k-Fold-CV|Cumulative-CV]
* Score type (LOO-CV,k-Fold-CV,Cumulative-CV)</pre>
*
* <pre> -Q
* Use probabilistic or 0/1 scoring.
* (default probabilistic scoring)</pre>
*
<!-- options-end -->
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String sTStart = Utils.getOption('A', options);
if (sTStart.length() != 0) {
setTStart(Double.parseDouble(sTStart));
}
String sRuns = Utils.getOption('U', options);
if (sRuns.length() != 0) {
setRuns(Integer.parseInt(sRuns));
}
String sDelta = Utils.getOption('D', options);
if (sDelta.length() != 0) {
setDelta(Double.parseDouble(sDelta));
}
String sSeed = Utils.getOption('R', options);
if (sSeed.length() != 0) {
setSeed(Integer.parseInt(sSeed));
}
super.setOptions(options);
}
/**
* Gets the current settings of the search algorithm.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
String[] superOptions = super.getOptions();
String[] options = new String[8 + superOptions.length];
int current = 0;
options[current++] = "-A";
options[current++] = "" + getTStart();
options[current++] = "-U";
options[current++] = "" + getRuns();
options[current++] = "-D";
options[current++] = "" + getDelta();
options[current++] = "-R";
options[current++] = "" + getSeed();
// insert options from parent class
for (int iOption = 0; iOption < superOptions.length; iOption++) {
options[current++] = superOptions[iOption];
}
// Fill up rest with empty strings, not nulls!
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* This will return a string describing the classifier.
* @return The string.
*/
public String globalInfo() {
return
"This Bayes Network learning algorithm uses the general purpose search method "
+ "of simulated annealing to find a well scoring network structure.\n\n"
+ "For more information see:\n\n"
+ getTechnicalInformation().toString();
} // globalInfo
/**
* @return a string to describe the TStart option.
*/
public String TStartTipText() {
return "Sets the start temperature of the simulated annealing search. "+
"The start temperature determines the probability that a step in the 'wrong' direction in the " +
"search space is accepted. The higher the temperature, the higher the probability of acceptance.";
} // TStartTipText
/**
* @return a string to describe the Runs option.
*/
public String runsTipText() {
return "Sets the number of iterations to be performed by the simulated annealing search.";
} // runsTipText
/**
* @return a string to describe the Delta option.
*/
public String deltaTipText() {
return "Sets the factor with which the temperature (and thus the acceptance probability of " +
"steps in the wrong direction in the search space) is decreased in each iteration.";
} // deltaTipText
/**
* @return a string to describe the Seed option.
*/
public String seedTipText() {
return "Initialization value for random number generator." +
" Setting the seed allows replicability of experiments.";
} // seedTipText
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.6 $");
}
} // SimulatedAnnealing