/*
* 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.
*/
/*
* SearchAlgorithm.java
* Copyright (C) 2003 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.bayes.net.search;
import weka.classifiers.bayes.BayesNet;
import weka.classifiers.bayes.net.BIFReader;
import weka.classifiers.bayes.net.ParentSet;
import weka.core.Instances;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import java.io.Serializable;
import java.util.Enumeration;
import java.util.Vector;
/**
* This is the base class for all search algorithms for learning Bayes networks.
* It contains some common code, used by other network structure search algorithms,
* and should not be used by itself.
*
<!-- options-start -->
<!-- options-end -->
*
* @author Remco Bouckaert
* @version $Revision: 6612 $
*/
public class SearchAlgorithm
implements OptionHandler, Serializable, RevisionHandler {
/** for serialization */
static final long serialVersionUID = 6164792240778525312L;
/**
* Holds upper bound on number of parents
*/
protected int m_nMaxNrOfParents = 1;
/**
* determines whether initial structure is an empty graph or a Naive Bayes network
*/
protected boolean m_bInitAsNaiveBayes = true;
/**
* Determines whether after structure is found a MarkovBlanketClassifier correction should be applied
* If this is true, m_bInitAsNaiveBayes is overridden and interpreted as false.
*/
protected boolean m_bMarkovBlanketClassifier = false;
/**
* File name containing initial network structure. This can be used as starting point for structure search
* It will be ignored if not speficied. When specified, it overrides the InitAsNaivBayes flag.
*/
protected String m_sInitalBIFFile;
/** c'tor **/
public SearchAlgorithm() {
} // SearchAlgorithm
/**
* AddArcMakesSense checks whether adding the arc from iAttributeTail to iAttributeHead
* does not already exists and does not introduce a cycle
*
* @param bayesNet
* @param instances
* @param iAttributeHead index of the attribute that becomes head of the arrow
* @param iAttributeTail index of the attribute that becomes tail of the arrow
* @return true if adding arc is allowed, otherwise false
*/
protected boolean addArcMakesSense(
BayesNet bayesNet,
Instances instances,
int iAttributeHead,
int iAttributeTail) {
if (iAttributeHead == iAttributeTail) {
return false;
}
// sanity check: arc should not be in parent set already
if (isArc(bayesNet, iAttributeHead, iAttributeTail)) {
return false;
}
// sanity check: arc should not introduce a cycle
int nNodes = instances.numAttributes();
boolean[] bDone = new boolean[nNodes];
for (int iNode = 0; iNode < nNodes; iNode++) {
bDone[iNode] = false;
}
// check for cycles
bayesNet.getParentSet(iAttributeHead).addParent(iAttributeTail, instances);
for (int iNode = 0; iNode < nNodes; iNode++) {
// find a node for which all parents are 'done'
boolean bFound = false;
for (int iNode2 = 0; !bFound && iNode2 < nNodes; iNode2++) {
if (!bDone[iNode2]) {
boolean bHasNoParents = true;
for (int iParent = 0; iParent < bayesNet.getParentSet(iNode2).getNrOfParents(); iParent++) {
if (!bDone[bayesNet.getParentSet(iNode2).getParent(iParent)]) {
bHasNoParents = false;
}
}
if (bHasNoParents) {
bDone[iNode2] = true;
bFound = true;
}
}
}
if (!bFound) {
bayesNet.getParentSet(iAttributeHead).deleteLastParent(instances);
return false;
}
}
bayesNet.getParentSet(iAttributeHead).deleteLastParent(instances);
return true;
} // AddArcMakesCycle
/**
* reverseArcMakesSense checks whether the arc from iAttributeTail to
* iAttributeHead exists and reversing does not introduce a cycle
*
* @param bayesNet
* @param instances
* @param iAttributeHead index of the attribute that is head of the arrow
* @param iAttributeTail index of the attribute that is tail of the arrow
* @return true if the arc from iAttributeTail to iAttributeHead exists and reversing does not introduce a cycle
*/
protected boolean reverseArcMakesSense(
BayesNet bayesNet,
Instances instances,
int iAttributeHead,
int iAttributeTail) {
if (iAttributeHead == iAttributeTail) {
return false;
}
// sanity check: arc should be in parent set already
if (!isArc(bayesNet, iAttributeHead, iAttributeTail)) {
return false;
}
// sanity check: arc should not introduce a cycle
int nNodes = instances.numAttributes();
boolean[] bDone = new boolean[nNodes];
for (int iNode = 0; iNode < nNodes; iNode++) {
bDone[iNode] = false;
}
// check for cycles
bayesNet.getParentSet(iAttributeTail).addParent(iAttributeHead, instances);
for (int iNode = 0; iNode < nNodes; iNode++) {
// find a node for which all parents are 'done'
boolean bFound = false;
for (int iNode2 = 0; !bFound && iNode2 < nNodes; iNode2++) {
if (!bDone[iNode2]) {
ParentSet parentSet = bayesNet.getParentSet(iNode2);
boolean bHasNoParents = true;
for (int iParent = 0; iParent < parentSet.getNrOfParents(); iParent++) {
if (!bDone[parentSet.getParent(iParent)]) {
// this one has a parent which is not 'done' UNLESS it is the arc to be reversed
if (!(iNode2 == iAttributeHead && parentSet.getParent(iParent) == iAttributeTail)) {
bHasNoParents = false;
}
}
}
if (bHasNoParents) {
bDone[iNode2] = true;
bFound = true;
}
}
}
if (!bFound) {
bayesNet.getParentSet(iAttributeTail).deleteLastParent(instances);
return false;
}
}
bayesNet.getParentSet(iAttributeTail).deleteLastParent(instances);
return true;
} // ReverseArcMakesCycle
/**
* IsArc checks whether the arc from iAttributeTail to iAttributeHead already exists
*
* @param bayesNet
* @param iAttributeHead index of the attribute that becomes head of the arrow
* @param iAttributeTail index of the attribute that becomes tail of the arrow
* @return true if the arc from iAttributeTail to iAttributeHead already exists
*/
protected boolean isArc(BayesNet bayesNet, int iAttributeHead, int iAttributeTail) {
for (int iParent = 0; iParent < bayesNet.getParentSet(iAttributeHead).getNrOfParents(); iParent++) {
if (bayesNet.getParentSet(iAttributeHead).getParent(iParent) == iAttributeTail) {
return true;
}
}
return false;
} // IsArc
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
return new Vector(0).elements();
} // listOption
/**
* Parses a given list of options. <p/>
*
* @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 {
} // setOptions
/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
return new String[0];
} // getOptions
/**
* a string representation of the algorithm
*
* @return a string representation
*/
public String toString() {
return "SearchAlgorithm\n";
} // toString
/**
* buildStructure determines the network structure/graph of the network.
* The default behavior is creating a network where all nodes have the first
* node as its parent (i.e., a BayesNet that behaves like a naive Bayes classifier).
* This method can be overridden by derived classes to restrict the class
* of network structures that are acceptable.
*
* @param bayesNet the network
* @param instances the data to use
* @throws Exception if something goes wrong
*/
public void buildStructure(BayesNet bayesNet, Instances instances) throws Exception {
if (m_sInitalBIFFile != null && !m_sInitalBIFFile.equals("")) {
BIFReader initialNet = new BIFReader().processFile(m_sInitalBIFFile);
for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
int iNode = initialNet.getNode(bayesNet.getNodeName(iAttribute));
for (int iParent = 0; iParent < initialNet.getNrOfParents(iAttribute);iParent++) {
String sParent = initialNet.getNodeName(initialNet.getParent(iNode, iParent));
int nParent = 0;
while (nParent < bayesNet.getNrOfNodes() && !bayesNet.getNodeName(nParent).equals(sParent)) {
nParent++;
}
if (nParent< bayesNet.getNrOfNodes()) {
bayesNet.getParentSet(iAttribute).addParent(nParent, instances);
} else {
System.err.println("Warning: Node " + sParent + " is ignored. It is found in initial network but not in data set.");
}
}
}
} else if (m_bInitAsNaiveBayes) {
int iClass = instances.classIndex();
// initialize parent sets to have arrow from classifier node to
// each of the other nodes
for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
if (iAttribute != iClass) {
bayesNet.getParentSet(iAttribute).addParent(iClass, instances);
}
}
}
search(bayesNet, instances);
if (m_bMarkovBlanketClassifier) {
doMarkovBlanketCorrection(bayesNet, instances);
}
} // buildStructure
/**
*
* @param bayesNet
* @param instances
*/
protected void search(BayesNet bayesNet, Instances instances) throws Exception {
// placeholder with implementation in derived classes
} // search
/**
* for each node in the network make sure it is in the
* Markov blanket of the classifier node, and if not,
* add arrows so that it is. If the node is an ancestor
* of the classifier node, add arrow pointing to the classifier
* node, otherwise, add arrow pointing to attribute node.
*
* @param bayesNet
* @param instances
*/
protected void doMarkovBlanketCorrection(BayesNet bayesNet, Instances instances) {
// Add class node as parent if it is not in the Markov Boundary
int iClass = instances.classIndex();
ParentSet ancestors = new ParentSet();
int nOldSize = 0;
ancestors.addParent(iClass, instances);
while (nOldSize != ancestors.getNrOfParents()) {
nOldSize = ancestors.getNrOfParents();
for (int iNode = 0; iNode < nOldSize; iNode++) {
int iCurrent = ancestors.getParent(iNode);
ParentSet p = bayesNet.getParentSet(iCurrent);
for (int iParent = 0; iParent < p.getNrOfParents(); iParent++) {
if (!ancestors.contains(p.getParent(iParent))) {
ancestors.addParent(p.getParent(iParent), instances);
}
}
}
}
for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
boolean bIsInMarkovBoundary = (iAttribute == iClass)
|| bayesNet.getParentSet(iAttribute).contains(iClass)
|| bayesNet.getParentSet(iClass).contains(iAttribute);
for (int iAttribute2 = 0; !bIsInMarkovBoundary && iAttribute2 < instances.numAttributes(); iAttribute2++) {
bIsInMarkovBoundary =
bayesNet.getParentSet(iAttribute2).contains(iAttribute)
&& bayesNet.getParentSet(iAttribute2).contains(iClass);
}
if (!bIsInMarkovBoundary) {
if (ancestors.contains(iAttribute)) {
if (bayesNet.getParentSet(iClass).getCardinalityOfParents() < 1024) {
bayesNet.getParentSet(iClass).addParent(iAttribute, instances);
} else {
// too bad
}
} else {
bayesNet.getParentSet(iAttribute).addParent(iClass, instances);
}
}
}
} // doMarkovBlanketCorrection
/**
*
* @param bMarkovBlanketClassifier
*/
protected void setMarkovBlanketClassifier(boolean bMarkovBlanketClassifier) {
m_bMarkovBlanketClassifier = bMarkovBlanketClassifier;
}
/**
*
* @return
*/
protected boolean getMarkovBlanketClassifier() {
return m_bMarkovBlanketClassifier;
}
/**
* @return a string to describe the MaxNrOfParentsoption.
*/
public String maxNrOfParentsTipText() {
return "Set the maximum number of parents a node in the Bayes net can have."
+ " When initialized as Naive Bayes, setting this parameter to 1 results in"
+ " a Naive Bayes classifier. When set to 2, a Tree Augmented Bayes Network (TAN)"
+ " is learned, and when set >2, a Bayes Net Augmented Bayes Network (BAN)"
+ " is learned. By setting it to a value much larger than the number of nodes"
+ " in the network (the default of 100000 pretty much guarantees this), no"
+ " restriction on the number of parents is enforced";
} // maxNrOfParentsTipText
/**
* @return a string to describe the InitAsNaiveBayes option.
*/
public String initAsNaiveBayesTipText() {
return "When set to true (default), the initial network used for structure learning"
+ " is a Naive Bayes Network, that is, a network with an arrow from the classifier"
+ " node to each other node. When set to false, an empty network is used as initial"
+ " network structure";
} // initAsNaiveBayesTipText
/**
* @return a string to describe the MarkovBlanketClassifier option.
*/
protected String markovBlanketClassifierTipText() {
return "When set to true (default is false), after a network structure is learned"
+ " a Markov Blanket correction is applied to the network structure. This ensures"
+ " that all nodes in the network are part of the Markov blanket of the classifier"
+ " node.";
} // markovBlanketClassifierTipText
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 6612 $");
}
} // class SearchAlgorithm