/*
* 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.
*/
/*
* TabuSearch.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.Vector;
/**
<!-- globalinfo-start -->
* This Bayes Network learning algorithm uses tabu search for finding a well scoring Bayes network structure. Tabu search is hill climbing till an optimum is reached. The following step is the least worst possible step. The last X steps are kept in a list and none of the steps in this so called tabu list is considered in taking the next step. The best network found in this traversal is returned.<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> -L <integer>
* Tabu list length</pre>
*
* <pre> -U <integer>
* Number of runs</pre>
*
* <pre> -P <nr of parents>
* Maximum number of parents</pre>
*
* <pre> -R
* Use arc reversal operation.
* (default false)</pre>
*
* <pre> -P <nr of parents>
* Maximum number of parents</pre>
*
* <pre> -R
* Use arc reversal operation.
* (default false)</pre>
*
* <pre> -N
* Initial structure is empty (instead of Naive Bayes)</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: 1.5 $
*/
public class TabuSearch
extends HillClimber
implements TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = 1176705618756672292L;
/** number of runs **/
int m_nRuns = 10;
/** size of tabu list **/
int m_nTabuList = 5;
/** the actual tabu list **/
Operation[] m_oTabuList = null;
/**
* 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;
}
/**
* search determines the network structure/graph of the network
* with the Tabu search algorithm.
*
* @param bayesNet the network to use
* @param instances the instances to use
* @throws Exception if something goes wrong
*/
protected void search(BayesNet bayesNet, Instances instances) throws Exception {
m_oTabuList = new Operation[m_nTabuList];
int iCurrentTabuList = 0;
// keeps track of score pf best structure found so far
double fBestScore;
double fCurrentScore = calcScore(bayesNet);
// keeps track of best structure found so far
BayesNet bestBayesNet;
// initialize bestBayesNet
fBestScore = fCurrentScore;
bestBayesNet = new BayesNet();
bestBayesNet.m_Instances = instances;
bestBayesNet.initStructure();
copyParentSets(bestBayesNet, bayesNet);
// go do the search
for (int iRun = 0; iRun < m_nRuns; iRun++) {
Operation oOperation = getOptimalOperation(bayesNet, instances);
performOperation(bayesNet, instances, oOperation);
// sanity check
if (oOperation == null) {
throw new Exception("Panic: could not find any step to make. Tabu list too long?");
}
// update tabu list
m_oTabuList[iCurrentTabuList] = oOperation;
iCurrentTabuList = (iCurrentTabuList + 1) % m_nTabuList;
fCurrentScore += oOperation.m_fScore;
// keep track of best network seen so far
if (fCurrentScore > fBestScore) {
fBestScore = fCurrentScore;
copyParentSets(bestBayesNet, bayesNet);
}
if (bayesNet.getDebug()) {
printTabuList();
}
}
// restore current network to best network
copyParentSets(bayesNet, bestBayesNet);
// free up memory
bestBayesNet = null;
} // search
/** 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
/** check whether the operation is not in the tabu list
* @param oOperation operation to be checked
* @return true if operation is not in the tabu list
*/
boolean isNotTabu(Operation oOperation) {
for (int iTabu = 0; iTabu < m_nTabuList; iTabu++) {
if (oOperation.equals(m_oTabuList[iTabu])) {
return false;
}
}
return true;
} // isNotTabu
/** print tabu list for debugging purposes.
*/
void printTabuList() {
for (int i = 0; i < m_nTabuList; i++) {
Operation o = m_oTabuList[i];
if (o != null) {
if (o.m_nOperation == 0) {System.out.print(" +(");} else {System.out.print(" -(");}
System.out.print(o.m_nTail + "->" + o.m_nHead + ")");
}
}
System.out.println();
} // printTabuList
/**
* @return number of runs
*/
public int getRuns() {
return m_nRuns;
} // getRuns
/**
* Sets the number of runs
* @param nRuns The number of runs to set
*/
public void setRuns(int nRuns) {
m_nRuns = nRuns;
} // setRuns
/**
* @return the Tabu List length
*/
public int getTabuList() {
return m_nTabuList;
} // getTabuList
/**
* Sets the Tabu List length.
* @param nTabuList The nTabuList to set
*/
public void setTabuList(int nTabuList) {
m_nTabuList = nTabuList;
} // setTabuList
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(4);
newVector.addElement(new Option("\tTabu list length", "L", 1, "-L <integer>"));
newVector.addElement(new Option("\tNumber of runs", "U", 1, "-U <integer>"));
newVector.addElement(new Option("\tMaximum number of parents", "P", 1, "-P <nr of parents>"));
newVector.addElement(new Option("\tUse arc reversal operation.\n\t(default false)", "R", 0, "-R"));
Enumeration enu = super.listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
return newVector.elements();
} // listOptions
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -L <integer>
* Tabu list length</pre>
*
* <pre> -U <integer>
* Number of runs</pre>
*
* <pre> -P <nr of parents>
* Maximum number of parents</pre>
*
* <pre> -R
* Use arc reversal operation.
* (default false)</pre>
*
* <pre> -P <nr of parents>
* Maximum number of parents</pre>
*
* <pre> -R
* Use arc reversal operation.
* (default false)</pre>
*
* <pre> -N
* Initial structure is empty (instead of Naive Bayes)</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 sTabuList = Utils.getOption('L', options);
if (sTabuList.length() != 0) {
setTabuList(Integer.parseInt(sTabuList));
}
String sRuns = Utils.getOption('U', options);
if (sRuns.length() != 0) {
setRuns(Integer.parseInt(sRuns));
}
super.setOptions(options);
} // setOptions
/**
* 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[7 + superOptions.length];
int current = 0;
options[current++] = "-L";
options[current++] = "" + getTabuList();
options[current++] = "-U";
options[current++] = "" + getRuns();
// 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;
} // getOptions
/**
* This will return a string describing the classifier.
* @return The string.
*/
public String globalInfo() {
return "This Bayes Network learning algorithm uses tabu search for finding a well scoring " +
"Bayes network structure. Tabu search is hill climbing till an optimum is reached. The " +
"following step is the least worst possible step. The last X steps are kept in a list and " +
"none of the steps in this so called tabu list is considered in taking the next step. " +
"The best network found in this traversal is returned.\n\n"
+ "For more information see:\n\n"
+ getTechnicalInformation().toString();
} // globalInfo
/**
* @return a string to describe the Runs option.
*/
public String runsTipText() {
return "Sets the number of steps to be performed.";
} // runsTipText
/**
* @return a string to describe the TabuList option.
*/
public String tabuListTipText() {
return "Sets the length of the tabu list.";
} // tabuListTipText
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.5 $");
}
} // TabuSearch