/*
* 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 3 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, see <http://www.gnu.org/licenses/>.
*/
/*
* HillClimber.java
* Copyright (C) 2004-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.bayes.net.search.local;
import java.io.Serializable;
import java.util.Enumeration;
import java.util.Vector;
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;
/**
<!-- globalinfo-start -->
* This Bayes Network learning algorithm uses a hill climbing algorithm adding, deleting and reversing arcs. The search is not restricted by an order on the variables (unlike K2). The difference with B and B2 is that this hill climber also considers arrows part of the naive Bayes structure for deletion.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <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 [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: 8034 $
*/
public class HillClimber
extends LocalScoreSearchAlgorithm {
/** for serialization */
static final long serialVersionUID = 4322783593818122403L;
/** the Operation class contains info on operations performed
* on the current Bayesian network.
*/
class Operation
implements Serializable, RevisionHandler {
/** for serialization */
static final long serialVersionUID = -4880888790432547895L;
// constants indicating the type of an operation
final static int OPERATION_ADD = 0;
final static int OPERATION_DEL = 1;
final static int OPERATION_REVERSE = 2;
/**
* c'tor
*/
public Operation() {
}
/** c'tor + initializers
*
* @param nTail
* @param nHead
* @param nOperation
*/
public Operation(int nTail, int nHead, int nOperation) {
m_nHead = nHead;
m_nTail = nTail;
m_nOperation = nOperation;
}
/** compare this operation with another
* @param other operation to compare with
* @return true if operation is the same
*/
public boolean equals(Operation other) {
if (other == null) {
return false;
}
return (( m_nOperation == other.m_nOperation) &&
(m_nHead == other.m_nHead) &&
(m_nTail == other.m_nTail));
} // equals
/** number of the tail node **/
public int m_nTail;
/** number of the head node **/
public int m_nHead;
/** type of operation (ADD, DEL, REVERSE) **/
public int m_nOperation;
/** change of score due to this operation **/
public double m_fDeltaScore = -1E100;
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
} // class Operation
/** cache for remembering the change in score for steps in the search space
*/
class Cache implements RevisionHandler {
/** change in score due to adding an arc **/
double [] [] m_fDeltaScoreAdd;
/** change in score due to deleting an arc **/
double [] [] m_fDeltaScoreDel;
/** c'tor
* @param nNrOfNodes number of nodes in network, used to determine memory size to reserve
*/
Cache(int nNrOfNodes) {
m_fDeltaScoreAdd = new double [nNrOfNodes][nNrOfNodes];
m_fDeltaScoreDel = new double [nNrOfNodes][nNrOfNodes];
}
/** set cache entry
* @param oOperation operation to perform
* @param fValue value to put in cache
*/
public void put(Operation oOperation, double fValue) {
if (oOperation.m_nOperation == Operation.OPERATION_ADD) {
m_fDeltaScoreAdd[oOperation.m_nTail][oOperation.m_nHead] = fValue;
} else {
m_fDeltaScoreDel[oOperation.m_nTail][oOperation.m_nHead] = fValue;
}
} // put
/** get cache entry
* @param oOperation operation to perform
* @return cache value
*/
public double get(Operation oOperation) {
switch(oOperation.m_nOperation) {
case Operation.OPERATION_ADD:
return m_fDeltaScoreAdd[oOperation.m_nTail][oOperation.m_nHead];
case Operation.OPERATION_DEL:
return m_fDeltaScoreDel[oOperation.m_nTail][oOperation.m_nHead];
case Operation.OPERATION_REVERSE:
return m_fDeltaScoreDel[oOperation.m_nTail][oOperation.m_nHead] +
m_fDeltaScoreAdd[oOperation.m_nHead][oOperation.m_nTail];
}
// should never get here
return 0;
} // get
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
} // class Cache
/** cache for storing score differences **/
Cache m_Cache = null;
/** use the arc reversal operator **/
boolean m_bUseArcReversal = false;
/**
* search determines the network structure/graph of the network
* with the Taby algorithm.
*
* @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 {
initCache(bayesNet, instances);
// go do the search
Operation oOperation = getOptimalOperation(bayesNet, instances);
while ((oOperation != null) && (oOperation.m_fDeltaScore > 0)) {
performOperation(bayesNet, instances, oOperation);
oOperation = getOptimalOperation(bayesNet, instances);
}
// free up memory
m_Cache = null;
} // search
/**
* initCache initializes the cache
*
* @param bayesNet Bayes network to be learned
* @param instances data set to learn from
* @throws Exception if something goes wrong
*/
void initCache(BayesNet bayesNet, Instances instances) throws Exception {
// determine base scores
double[] fBaseScores = new double[instances.numAttributes()];
int nNrOfAtts = instances.numAttributes();
m_Cache = new Cache (nNrOfAtts);
for (int iAttribute = 0; iAttribute < nNrOfAtts; iAttribute++) {
updateCache(iAttribute, nNrOfAtts, bayesNet.getParentSet(iAttribute));
}
for (int iAttribute = 0; iAttribute < nNrOfAtts; iAttribute++) {
fBaseScores[iAttribute] = calcNodeScore(iAttribute);
}
for (int iAttributeHead = 0; iAttributeHead < nNrOfAtts; iAttributeHead++) {
for (int iAttributeTail = 0; iAttributeTail < nNrOfAtts; iAttributeTail++) {
if (iAttributeHead != iAttributeTail) {
Operation oOperation = new Operation(iAttributeTail, iAttributeHead, Operation.OPERATION_ADD);
m_Cache.put(oOperation, calcScoreWithExtraParent(iAttributeHead, iAttributeTail) - fBaseScores[iAttributeHead]);
}
}
}
} // initCache
/** check whether the operation is not in the forbidden.
* For base hill climber, there are no restrictions on operations,
* so we always return true.
* @param oOperation operation to be checked
* @return true if operation is not in the tabu list
*/
boolean isNotTabu(Operation oOperation) {
return true;
} // isNotTabu
/**
* getOptimalOperation finds the optimal operation that can be performed
* on the Bayes network that is not in the tabu list.
*
* @param bayesNet Bayes network to apply operation on
* @param instances data set to learn from
* @return optimal operation found
* @throws Exception if something goes wrong
*/
Operation getOptimalOperation(BayesNet bayesNet, Instances instances) throws Exception {
Operation oBestOperation = new Operation();
// Add???
oBestOperation = findBestArcToAdd(bayesNet, instances, oBestOperation);
// Delete???
oBestOperation = findBestArcToDelete(bayesNet, instances, oBestOperation);
// Reverse???
if (getUseArcReversal()) {
oBestOperation = findBestArcToReverse(bayesNet, instances, oBestOperation);
}
// did we find something?
if (oBestOperation.m_fDeltaScore == -1E100) {
return null;
}
return oBestOperation;
} // getOptimalOperation
/**
* performOperation applies an operation
* on the Bayes network and update the cache.
*
* @param bayesNet Bayes network to apply operation on
* @param instances data set to learn from
* @param oOperation operation to perform
* @throws Exception if something goes wrong
*/
void performOperation(BayesNet bayesNet, Instances instances, Operation oOperation) throws Exception {
// perform operation
switch (oOperation.m_nOperation) {
case Operation.OPERATION_ADD:
applyArcAddition(bayesNet, oOperation.m_nHead, oOperation.m_nTail, instances);
if (bayesNet.getDebug()) {
System.out.print("Add " + oOperation.m_nHead + " -> " + oOperation.m_nTail);
}
break;
case Operation.OPERATION_DEL:
applyArcDeletion(bayesNet, oOperation.m_nHead, oOperation.m_nTail, instances);
if (bayesNet.getDebug()) {
System.out.print("Del " + oOperation.m_nHead + " -> " + oOperation.m_nTail);
}
break;
case Operation.OPERATION_REVERSE:
applyArcDeletion(bayesNet, oOperation.m_nHead, oOperation.m_nTail, instances);
applyArcAddition(bayesNet, oOperation.m_nTail, oOperation.m_nHead, instances);
if (bayesNet.getDebug()) {
System.out.print("Rev " + oOperation.m_nHead+ " -> " + oOperation.m_nTail);
}
break;
}
} // performOperation
/**
*
* @param bayesNet
* @param iHead
* @param iTail
* @param instances
*/
void applyArcAddition(BayesNet bayesNet, int iHead, int iTail, Instances instances) {
ParentSet bestParentSet = bayesNet.getParentSet(iHead);
bestParentSet.addParent(iTail, instances);
updateCache(iHead, instances.numAttributes(), bestParentSet);
} // applyArcAddition
/**
*
* @param bayesNet
* @param iHead
* @param iTail
* @param instances
*/
void applyArcDeletion(BayesNet bayesNet, int iHead, int iTail, Instances instances) {
ParentSet bestParentSet = bayesNet.getParentSet(iHead);
bestParentSet.deleteParent(iTail, instances);
updateCache(iHead, instances.numAttributes(), bestParentSet);
} // applyArcAddition
/**
* find best (or least bad) arc addition operation
*
* @param bayesNet Bayes network to add arc to
* @param instances data set
* @param oBestOperation
* @return Operation containing best arc to add, or null if no arc addition is allowed
* (this can happen if any arc addition introduces a cycle, or all parent sets are filled
* up to the maximum nr of parents).
*/
Operation findBestArcToAdd(BayesNet bayesNet, Instances instances, Operation oBestOperation) {
int nNrOfAtts = instances.numAttributes();
// find best arc to add
for (int iAttributeHead = 0; iAttributeHead < nNrOfAtts; iAttributeHead++) {
if (bayesNet.getParentSet(iAttributeHead).getNrOfParents() < m_nMaxNrOfParents) {
for (int iAttributeTail = 0; iAttributeTail < nNrOfAtts; iAttributeTail++) {
if (addArcMakesSense(bayesNet, instances, iAttributeHead, iAttributeTail)) {
Operation oOperation = new Operation(iAttributeTail, iAttributeHead, Operation.OPERATION_ADD);
if (m_Cache.get(oOperation) > oBestOperation.m_fDeltaScore) {
if (isNotTabu(oOperation)) {
oBestOperation = oOperation;
oBestOperation.m_fDeltaScore = m_Cache.get(oOperation);
}
}
}
}
}
}
return oBestOperation;
} // findBestArcToAdd
/**
* find best (or least bad) arc deletion operation
*
* @param bayesNet Bayes network to delete arc from
* @param instances data set
* @param oBestOperation
* @return Operation containing best arc to delete, or null if no deletion can be made
* (happens when there is no arc in the network yet).
*/
Operation findBestArcToDelete(BayesNet bayesNet, Instances instances, Operation oBestOperation) {
int nNrOfAtts = instances.numAttributes();
// find best arc to delete
for (int iNode = 0; iNode < nNrOfAtts; iNode++) {
ParentSet parentSet = bayesNet.getParentSet(iNode);
for (int iParent = 0; iParent < parentSet.getNrOfParents(); iParent++) {
Operation oOperation = new Operation(parentSet.getParent(iParent), iNode, Operation.OPERATION_DEL);
if (m_Cache.get(oOperation) > oBestOperation.m_fDeltaScore) {
if (isNotTabu(oOperation)) {
oBestOperation = oOperation;
oBestOperation.m_fDeltaScore = m_Cache.get(oOperation);
}
}
}
}
return oBestOperation;
} // findBestArcToDelete
/**
* find best (or least bad) arc reversal operation
*
* @param bayesNet Bayes network to reverse arc in
* @param instances data set
* @param oBestOperation
* @return Operation containing best arc to reverse, or null if no reversal is allowed
* (happens if there is no arc in the network yet, or when any such reversal introduces
* a cycle).
*/
Operation findBestArcToReverse(BayesNet bayesNet, Instances instances, Operation oBestOperation) {
int nNrOfAtts = instances.numAttributes();
// find best arc to reverse
for (int iNode = 0; iNode < nNrOfAtts; iNode++) {
ParentSet parentSet = bayesNet.getParentSet(iNode);
for (int iParent = 0; iParent < parentSet.getNrOfParents(); iParent++) {
int iTail = parentSet.getParent(iParent);
// is reversal allowed?
if (reverseArcMakesSense(bayesNet, instances, iNode, iTail) &&
bayesNet.getParentSet(iTail).getNrOfParents() < m_nMaxNrOfParents) {
// go check if reversal results in the best step forward
Operation oOperation = new Operation(parentSet.getParent(iParent), iNode, Operation.OPERATION_REVERSE);
if (m_Cache.get(oOperation) > oBestOperation.m_fDeltaScore) {
if (isNotTabu(oOperation)) {
oBestOperation = oOperation;
oBestOperation.m_fDeltaScore = m_Cache.get(oOperation);
}
}
}
}
}
return oBestOperation;
} // findBestArcToReverse
/**
* update the cache due to change of parent set of a node
*
* @param iAttributeHead node that has its parent set changed
* @param nNrOfAtts number of nodes/attributes in data set
* @param parentSet new parents set of node iAttributeHead
*/
void updateCache(int iAttributeHead, int nNrOfAtts, ParentSet parentSet) {
// update cache entries for arrows heading towards iAttributeHead
double fBaseScore = calcNodeScore(iAttributeHead);
int nNrOfParents = parentSet.getNrOfParents();
for (int iAttributeTail = 0; iAttributeTail < nNrOfAtts; iAttributeTail++) {
if (iAttributeTail != iAttributeHead) {
if (!parentSet.contains(iAttributeTail)) {
// add entries to cache for adding arcs
if (nNrOfParents < m_nMaxNrOfParents) {
Operation oOperation = new Operation(iAttributeTail, iAttributeHead, Operation.OPERATION_ADD);
m_Cache.put(oOperation, calcScoreWithExtraParent(iAttributeHead, iAttributeTail) - fBaseScore);
}
} else {
// add entries to cache for deleting arcs
Operation oOperation = new Operation(iAttributeTail, iAttributeHead, Operation.OPERATION_DEL);
m_Cache.put(oOperation, calcScoreWithMissingParent(iAttributeHead, iAttributeTail) - fBaseScore);
}
}
}
} // updateCache
/**
* Sets the max number of parents
*
* @param nMaxNrOfParents the max number of parents
*/
public void setMaxNrOfParents(int nMaxNrOfParents) {
m_nMaxNrOfParents = nMaxNrOfParents;
}
/**
* Gets the max number of parents.
*
* @return the max number of parents
*/
public int getMaxNrOfParents() {
return m_nMaxNrOfParents;
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(2);
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"));
newVector.addElement(new Option("\tInitial structure is empty (instead of Naive Bayes)", "N", 0, "-N"));
newVector.addElement(new Option("\tInitial structure specified in XML BIF file", "X", 1, "-X"));
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> -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 [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 {
setUseArcReversal(Utils.getFlag('R', options));
setInitAsNaiveBayes (!(Utils.getFlag('N', options)));
m_sInitalBIFFile = Utils.getOption('X', options);
String sMaxNrOfParents = Utils.getOption('P', options);
if (sMaxNrOfParents.length() != 0) {
setMaxNrOfParents(Integer.parseInt(sMaxNrOfParents));
} else {
setMaxNrOfParents(100000);
}
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[9 + superOptions.length];
int current = 0;
if (getUseArcReversal()) {
options[current++] = "-R";
}
if (!getInitAsNaiveBayes()) {
options[current++] = "-N";
}
if (m_sInitalBIFFile!=null && !m_sInitalBIFFile.equals("")) {
options[current++] = "-X";
options[current++] = m_sInitalBIFFile;
}
options[current++] = "-P";
options[current++] = "" + m_nMaxNrOfParents;
// 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
/**
* Sets whether to init as naive bayes
*
* @param bInitAsNaiveBayes whether to init as naive bayes
*/
public void setInitAsNaiveBayes(boolean bInitAsNaiveBayes) {
m_bInitAsNaiveBayes = bInitAsNaiveBayes;
}
/**
* Gets whether to init as naive bayes
*
* @return whether to init as naive bayes
*/
public boolean getInitAsNaiveBayes() {
return m_bInitAsNaiveBayes;
}
/** get use the arc reversal operation
* @return whether the arc reversal operation should be used
*/
public boolean getUseArcReversal() {
return m_bUseArcReversal;
} // getUseArcReversal
/** set use the arc reversal operation
* @param bUseArcReversal whether the arc reversal operation should be used
*/
public void setUseArcReversal(boolean bUseArcReversal) {
m_bUseArcReversal = bUseArcReversal;
} // setUseArcReversal
/**
* This will return a string describing the search algorithm.
* @return The string.
*/
public String globalInfo() {
return "This Bayes Network learning algorithm uses a hill climbing algorithm " +
"adding, deleting and reversing arcs. The search is not restricted by an order " +
"on the variables (unlike K2). The difference with B and B2 is that this hill " +
"climber also considers arrows part of the naive Bayes structure for deletion.";
} // globalInfo
/**
* @return a string to describe the Use Arc Reversal option.
*/
public String useArcReversalTipText() {
return "When set to true, the arc reversal operation is used in the search.";
} // useArcReversalTipText
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
} // HillClimber