/*
* 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.
*/
/*
* RepeatedHillClimber.java
* Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.bayes.net.search.global;
import weka.classifiers.bayes.BayesNet;
import weka.classifiers.bayes.net.ParentSet;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.Utils;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* This Bayes Network learning algorithm repeatedly uses hill climbing starting with a randomly generated network structure and return the best structure of the various runs.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -U <integer>
* Number of runs</pre>
*
* <pre> -A <seed>
* Random number seed</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: 1.6 $
*/
public class RepeatedHillClimber
extends HillClimber {
/** for serialization */
static final long serialVersionUID = -7359197180460703069L;
/** number of runs **/
int m_nRuns = 10;
/** random number seed **/
int m_nSeed = 1;
/** random number generator **/
Random m_random;
/**
* search determines the network structure/graph of the network
* with the repeated hill climbing.
*
* @param bayesNet the network to use
* @param instances the data to use
* @throws Exception if something goes wrong
**/
protected void search(BayesNet bayesNet, Instances instances) throws Exception {
m_random = new Random(getSeed());
// 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++) {
// generate random nework
generateRandomNet(bayesNet, instances);
// search
super.search(bayesNet, instances);
// calculate score
fCurrentScore = calcScore(bayesNet);
// keep track of best network seen so far
if (fCurrentScore > fBestScore) {
fBestScore = fCurrentScore;
copyParentSets(bestBayesNet, bayesNet);
}
}
// restore current network to best network
copyParentSets(bayesNet, bestBayesNet);
// free up memory
bestBayesNet = null;
} // search
/**
*
* @param bayesNet
* @param instances
*/
void generateRandomNet(BayesNet bayesNet, Instances instances) {
int nNodes = instances.numAttributes();
// clear network
for (int iNode = 0; iNode < nNodes; iNode++) {
ParentSet parentSet = bayesNet.getParentSet(iNode);
while (parentSet.getNrOfParents() > 0) {
parentSet.deleteLastParent(instances);
}
}
// initialize as naive Bayes?
if (getInitAsNaiveBayes()) {
int iClass = instances.classIndex();
// initialize parent sets to have arrow from classifier node to
// each of the other nodes
for (int iNode = 0; iNode < nNodes; iNode++) {
if (iNode != iClass) {
bayesNet.getParentSet(iNode).addParent(iClass, instances);
}
}
}
// insert random arcs
int nNrOfAttempts = m_random.nextInt(nNodes * nNodes);
for (int iAttempt = 0; iAttempt < nNrOfAttempts; iAttempt++) {
int iTail = m_random.nextInt(nNodes);
int iHead = m_random.nextInt(nNodes);
if (bayesNet.getParentSet(iHead).getNrOfParents() < getMaxNrOfParents() &&
addArcMakesSense(bayesNet, instances, iHead, iTail)) {
bayesNet.getParentSet(iHead).addParent(iTail, instances);
}
}
} // generateRandomNet
/**
* 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
/**
* Returns the number of runs
*
* @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
/**
* Returns the random seed
*
* @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(4);
newVector.addElement(new Option("\tNumber of runs", "U", 1, "-U <integer>"));
newVector.addElement(new Option("\tRandom number seed", "A", 1, "-A <seed>"));
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> -U <integer>
* Number of runs</pre>
*
* <pre> -A <seed>
* Random number seed</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 sRuns = Utils.getOption('U', options);
if (sRuns.length() != 0) {
setRuns(Integer.parseInt(sRuns));
}
String sSeed = Utils.getOption('A', options);
if (sSeed.length() != 0) {
setSeed(Integer.parseInt(sSeed));
}
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++] = "-U";
options[current++] = "" + getRuns();
options[current++] = "-A";
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;
} // getOptions
/**
* This will return a string describing the classifier.
*
* @return The string.
*/
public String globalInfo() {
return "This Bayes Network learning algorithm repeatedly uses hill climbing starting " +
"with a randomly generated network structure and return the best structure of the " +
"various runs.";
} // globalInfo
/**
* @return a string to describe the Runs option.
*/
public String runsTipText() {
return "Sets the number of times hill climbing is performed.";
} // runsTipText
/**
* @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 $");
}
}