/*
* 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.
*/
/*
* GeneticSearch.java
* Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.bayes.net.search.local;
import weka.classifiers.bayes.BayesNet;
import weka.classifiers.bayes.net.ParentSet;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionHandler;
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 uses genetic search for finding a well scoring Bayes network structure. Genetic search works by having a population of Bayes network structures and allow them to mutate and apply cross over to get offspring. The best network structure found during the process is returned.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -L <integer>
* Population size</pre>
*
* <pre> -A <integer>
* Descendant population size</pre>
*
* <pre> -U <integer>
* Number of runs</pre>
*
* <pre> -M
* Use mutation.
* (default true)</pre>
*
* <pre> -C
* Use cross-over.
* (default true)</pre>
*
* <pre> -O
* Use tournament selection (true) or maximum subpopulatin (false).
* (default false)</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 [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
* Score type (BAYES, BDeu, MDL, ENTROPY and AIC)</pre>
*
<!-- options-end -->
*
* @author Remco Bouckaert (rrb@xm.co.nz)
* @version $Revision: 1.5 $
*/
public class GeneticSearch
extends LocalScoreSearchAlgorithm {
/** for serialization */
static final long serialVersionUID = -7037070678911459757L;
/** number of runs **/
int m_nRuns = 10;
/** size of population **/
int m_nPopulationSize = 10;
/** size of descendant population **/
int m_nDescendantPopulationSize = 100;
/** use cross-over? **/
boolean m_bUseCrossOver = true;
/** use mutation? **/
boolean m_bUseMutation = true;
/** use tournament selection or take best sub-population **/
boolean m_bUseTournamentSelection = false;
/** random number seed **/
int m_nSeed = 1;
/** random number generator **/
Random m_random = null;
/** used in BayesNetRepresentation for efficiently determining
* whether a number is square
*/
static boolean [] g_bIsSquare;
class BayesNetRepresentation implements RevisionHandler {
/** number of nodes in network **/
int m_nNodes = 0;
/** bit representation of parent sets
* m_bits[iTail + iHead * m_nNodes] represents arc iTail->iHead
*/
boolean [] m_bits;
/** score of represented network structure **/
double m_fScore = 0.0f;
/**
* return score of represented network structure
*
* @return the score
*/
public double getScore() {
return m_fScore;
} // getScore
/**
* c'tor
*
* @param nNodes the number of nodes
*/
BayesNetRepresentation (int nNodes) {
m_nNodes = nNodes;
} // c'tor
/** initialize with a random structure by randomly placing
* m_nNodes arcs.
*/
public void randomInit() {
do {
m_bits = new boolean [m_nNodes * m_nNodes];
for (int i = 0; i < m_nNodes; i++) {
int iPos;
do {
iPos = m_random.nextInt(m_nNodes * m_nNodes);
} while (isSquare(iPos));
m_bits[iPos] = true;
}
} while (hasCycles());
calcScore();
}
/** calculate score of current network representation
* As a side effect, the parent sets are set
*/
void calcScore() {
// clear current network
for (int iNode = 0; iNode < m_nNodes; iNode++) {
ParentSet parentSet = m_BayesNet.getParentSet(iNode);
while (parentSet.getNrOfParents() > 0) {
parentSet.deleteLastParent(m_BayesNet.m_Instances);
}
}
// insert arrows
for (int iNode = 0; iNode < m_nNodes; iNode++) {
ParentSet parentSet = m_BayesNet.getParentSet(iNode);
for (int iNode2 = 0; iNode2 < m_nNodes; iNode2++) {
if (m_bits[iNode2 + iNode * m_nNodes]) {
parentSet.addParent(iNode2, m_BayesNet.m_Instances);
}
}
}
// calc score
m_fScore = 0.0;
for (int iNode = 0; iNode < m_nNodes; iNode++) {
m_fScore += calcNodeScore(iNode);
}
} // calcScore
/** check whether there are cycles in the network
*
* @return true if a cycle is found, false otherwise
*/
public boolean hasCycles() {
// check for cycles
boolean[] bDone = new boolean[m_nNodes];
for (int iNode = 0; iNode < m_nNodes; iNode++) {
// find a node for which all parents are 'done'
boolean bFound = false;
for (int iNode2 = 0; !bFound && iNode2 < m_nNodes; iNode2++) {
if (!bDone[iNode2]) {
boolean bHasNoParents = true;
for (int iParent = 0; iParent < m_nNodes; iParent++) {
if (m_bits[iParent + iNode2 * m_nNodes] && !bDone[iParent]) {
bHasNoParents = false;
}
}
if (bHasNoParents) {
bDone[iNode2] = true;
bFound = true;
}
}
}
if (!bFound) {
return true;
}
}
return false;
} // hasCycles
/** create clone of current object
* @return cloned object
*/
BayesNetRepresentation copy() {
BayesNetRepresentation b = new BayesNetRepresentation(m_nNodes);
b.m_bits = new boolean [m_bits.length];
for (int i = 0; i < m_nNodes * m_nNodes; i++) {
b.m_bits[i] = m_bits[i];
}
b.m_fScore = m_fScore;
return b;
} // copy
/** Apply mutation operation to BayesNet
* Calculate score and as a side effect sets BayesNet parent sets.
*/
void mutate() {
// flip a bit
do {
int iBit;
do {
iBit = m_random.nextInt(m_nNodes * m_nNodes);
} while (isSquare(iBit));
m_bits[iBit] = !m_bits[iBit];
} while (hasCycles());
calcScore();
} // mutate
/** Apply cross-over operation to BayesNet
* Calculate score and as a side effect sets BayesNet parent sets.
* @param other BayesNetRepresentation to cross over with
*/
void crossOver(BayesNetRepresentation other) {
boolean [] bits = new boolean [m_bits.length];
for (int i = 0; i < m_bits.length; i++) {
bits[i] = m_bits[i];
}
int iCrossOverPoint = m_bits.length;
do {
// restore to original state
for (int i = iCrossOverPoint; i < m_bits.length; i++) {
m_bits[i] = bits[i];
}
// take all bits from cross-over points onwards
iCrossOverPoint = m_random.nextInt(m_bits.length);
for (int i = iCrossOverPoint; i < m_bits.length; i++) {
m_bits[i] = other.m_bits[i];
}
} while (hasCycles());
calcScore();
} // crossOver
/** check if number is square and initialize g_bIsSquare structure
* if necessary
* @param nNum number to check (should be below m_nNodes * m_nNodes)
* @return true if number is square
*/
boolean isSquare(int nNum) {
if (g_bIsSquare == null || g_bIsSquare.length < nNum) {
g_bIsSquare = new boolean [m_nNodes * m_nNodes];
for (int i = 0; i < m_nNodes; i++) {
g_bIsSquare[i * m_nNodes + i] = true;
}
}
return g_bIsSquare[nNum];
} // isSquare
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.5 $");
}
} // class BayesNetRepresentation
/**
* search determines the network structure/graph of the network
* with a genetic search algorithm.
*
* @param bayesNet the network to use
* @param instances the data to use
* @throws Exception if population size doesn fit or neither cross-over or mutation was chosen
*/
protected void search(BayesNet bayesNet, Instances instances) throws Exception {
// sanity check
if (getDescendantPopulationSize() < getPopulationSize()) {
throw new Exception ("Descendant PopulationSize should be at least Population Size");
}
if (!getUseCrossOver() && !getUseMutation()) {
throw new Exception ("At least one of mutation or cross-over should be used");
}
m_random = new Random(m_nSeed);
// keeps track of best structure found so far
BayesNet bestBayesNet;
// keeps track of score pf best structure found so far
double fBestScore = 0.0;
for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
fBestScore += calcNodeScore(iAttribute);
}
// initialize bestBayesNet
bestBayesNet = new BayesNet();
bestBayesNet.m_Instances = instances;
bestBayesNet.initStructure();
copyParentSets(bestBayesNet, bayesNet);
// initialize population
BayesNetRepresentation [] population = new BayesNetRepresentation [getPopulationSize()];
for (int i = 0; i < getPopulationSize(); i++) {
population[i] = new BayesNetRepresentation (instances.numAttributes());
population[i].randomInit();
if (population[i].getScore() > fBestScore) {
copyParentSets(bestBayesNet, bayesNet);
fBestScore = population[i].getScore();
}
}
// go do the search
for (int iRun = 0; iRun < m_nRuns; iRun++) {
// create descendants
BayesNetRepresentation [] descendantPopulation = new BayesNetRepresentation [getDescendantPopulationSize()];
for (int i = 0; i < getDescendantPopulationSize(); i++) {
descendantPopulation[i] = population[m_random.nextInt(getPopulationSize())].copy();
if (getUseMutation()) {
if (getUseCrossOver() && m_random.nextBoolean()) {
descendantPopulation[i].crossOver(population[m_random.nextInt(getPopulationSize())]);
} else {
descendantPopulation[i].mutate();
}
} else {
// use crossover
descendantPopulation[i].crossOver(population[m_random.nextInt(getPopulationSize())]);
}
if (descendantPopulation[i].getScore() > fBestScore) {
copyParentSets(bestBayesNet, bayesNet);
fBestScore = descendantPopulation[i].getScore();
}
}
// select new population
boolean [] bSelected = new boolean [getDescendantPopulationSize()];
for (int i = 0; i < getPopulationSize(); i++) {
int iSelected = 0;
if (m_bUseTournamentSelection) {
// use tournament selection
iSelected = m_random.nextInt(getDescendantPopulationSize());
while (bSelected[iSelected]) {
iSelected = (iSelected + 1) % getDescendantPopulationSize();
}
int iSelected2 = m_random.nextInt(getDescendantPopulationSize());
while (bSelected[iSelected2]) {
iSelected2 = (iSelected2 + 1) % getDescendantPopulationSize();
}
if (descendantPopulation[iSelected2].getScore() > descendantPopulation[iSelected].getScore()) {
iSelected = iSelected2;
}
} else {
// find best scoring network in population
while (bSelected[iSelected]) {
iSelected++;
}
double fScore = descendantPopulation[iSelected].getScore();
for (int j = 0; j < getDescendantPopulationSize(); j++) {
if (!bSelected[j] && descendantPopulation[j].getScore() > fScore) {
fScore = descendantPopulation[j].getScore();
iSelected = j;
}
}
}
population[i] = descendantPopulation[iSelected];
bSelected[iSelected] = true;
}
}
// 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
/**
* @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 an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(7);
newVector.addElement(new Option("\tPopulation size", "L", 1, "-L <integer>"));
newVector.addElement(new Option("\tDescendant population size", "A", 1, "-A <integer>"));
newVector.addElement(new Option("\tNumber of runs", "U", 1, "-U <integer>"));
newVector.addElement(new Option("\tUse mutation.\n\t(default true)", "M", 0, "-M"));
newVector.addElement(new Option("\tUse cross-over.\n\t(default true)", "C", 0, "-C"));
newVector.addElement(new Option("\tUse tournament selection (true) or maximum subpopulatin (false).\n\t(default false)", "O", 0, "-O"));
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();
} // listOptions
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -L <integer>
* Population size</pre>
*
* <pre> -A <integer>
* Descendant population size</pre>
*
* <pre> -U <integer>
* Number of runs</pre>
*
* <pre> -M
* Use mutation.
* (default true)</pre>
*
* <pre> -C
* Use cross-over.
* (default true)</pre>
*
* <pre> -O
* Use tournament selection (true) or maximum subpopulatin (false).
* (default false)</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 [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
* Score type (BAYES, BDeu, MDL, ENTROPY and AIC)</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 sPopulationSize = Utils.getOption('L', options);
if (sPopulationSize.length() != 0) {
setPopulationSize(Integer.parseInt(sPopulationSize));
}
String sDescendantPopulationSize = Utils.getOption('A', options);
if (sDescendantPopulationSize.length() != 0) {
setDescendantPopulationSize(Integer.parseInt(sDescendantPopulationSize));
}
String sRuns = Utils.getOption('U', options);
if (sRuns.length() != 0) {
setRuns(Integer.parseInt(sRuns));
}
String sSeed = Utils.getOption('R', options);
if (sSeed.length() != 0) {
setSeed(Integer.parseInt(sSeed));
}
setUseMutation(Utils.getFlag('M', options));
setUseCrossOver(Utils.getFlag('C', options));
setUseTournamentSelection(Utils.getFlag('O', options));
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[11 + superOptions.length];
int current = 0;
options[current++] = "-L";
options[current++] = "" + getPopulationSize();
options[current++] = "-A";
options[current++] = "" + getDescendantPopulationSize();
options[current++] = "-U";
options[current++] = "" + getRuns();
options[current++] = "-R";
options[current++] = "" + getSeed();
if (getUseMutation()) {
options[current++] = "-M";
}
if (getUseCrossOver()) {
options[current++] = "-C";
}
if (getUseTournamentSelection()) {
options[current++] = "-O";
}
// 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
/**
* @return whether cross-over is used
*/
public boolean getUseCrossOver() {
return m_bUseCrossOver;
}
/**
* @return whether mutation is used
*/
public boolean getUseMutation() {
return m_bUseMutation;
}
/**
* @return descendant population size
*/
public int getDescendantPopulationSize() {
return m_nDescendantPopulationSize;
}
/**
* @return population size
*/
public int getPopulationSize() {
return m_nPopulationSize;
}
/**
* @param bUseCrossOver sets whether cross-over is used
*/
public void setUseCrossOver(boolean bUseCrossOver) {
m_bUseCrossOver = bUseCrossOver;
}
/**
* @param bUseMutation sets whether mutation is used
*/
public void setUseMutation(boolean bUseMutation) {
m_bUseMutation = bUseMutation;
}
/**
* @return whether Tournament Selection (true) or Maximum Sub-Population (false) should be used
*/
public boolean getUseTournamentSelection() {
return m_bUseTournamentSelection;
}
/**
* @param bUseTournamentSelection sets whether Tournament Selection or Maximum Sub-Population should be used
*/
public void setUseTournamentSelection(boolean bUseTournamentSelection) {
m_bUseTournamentSelection = bUseTournamentSelection;
}
/**
* @param iDescendantPopulationSize sets descendant population size
*/
public void setDescendantPopulationSize(int iDescendantPopulationSize) {
m_nDescendantPopulationSize = iDescendantPopulationSize;
}
/**
* @param iPopulationSize sets population size
*/
public void setPopulationSize(int iPopulationSize) {
m_nPopulationSize = iPopulationSize;
}
/**
* @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
/**
* This will return a string describing the classifier.
* @return The string.
*/
public String globalInfo() {
return "This Bayes Network learning algorithm uses genetic search for finding a well scoring " +
"Bayes network structure. Genetic search works by having a population of Bayes network structures " +
"and allow them to mutate and apply cross over to get offspring. The best network structure " +
"found during the process is returned.";
} // globalInfo
/**
* @return a string to describe the Runs option.
*/
public String runsTipText() {
return "Sets the number of generations of Bayes network structure populations.";
} // 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
/**
* @return a string to describe the Population Size option.
*/
public String populationSizeTipText() {
return "Sets the size of the population of network structures that is selected each generation.";
} // populationSizeTipText
/**
* @return a string to describe the Descendant Population Size option.
*/
public String descendantPopulationSizeTipText() {
return "Sets the size of the population of descendants that is created each generation.";
} // descendantPopulationSizeTipText
/**
* @return a string to describe the Use Mutation option.
*/
public String useMutationTipText() {
return "Determines whether mutation is allowed. Mutation flips a bit in the bit " +
"representation of the network structure. At least one of mutation or cross-over " +
"should be used.";
} // useMutationTipText
/**
* @return a string to describe the Use Cross-Over option.
*/
public String useCrossOverTipText() {
return "Determines whether cross-over is allowed. Cross over combined the bit " +
"representations of network structure by taking a random first k bits of one" +
"and adding the remainder of the other. At least one of mutation or cross-over " +
"should be used.";
} // useCrossOverTipText
/**
* @return a string to describe the Use Tournament Selection option.
*/
public String useTournamentSelectionTipText() {
return "Determines the method of selecting a population. When set to true, tournament " +
"selection is used (pick two at random and the highest is allowed to continue). " +
"When set to false, the top scoring network structures are selected.";
} // useTournamentSelectionTipText
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.5 $");
}
} // GeneticSearch