weka.classifiers.bayes.net.BIFReader Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of mini-weka Show documentation
Show all versions of mini-weka Show documentation
The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
The newest version!
/*
* 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
*
* For more details on XML BIF see:
*
* Fabio Cozman, Marek Druzdzel, Daniel Garcia (1998). XML BIF version 0.3. URL
* http://www-2.cs.cmu.edu/~fgcozman/Research/InterchangeFormat/.
*
*
*
* BibTeX:
*
*
* @misc{Cozman1998,
* author = {Fabio Cozman and Marek Druzdzel and Daniel Garcia},
* title = {XML BIF version 0.3},
* year = {1998},
* URL = {http://www-2.cs.cmu.edu/\~fgcozman/Research/InterchangeFormat/}
* }
*
*
*
*
* Valid options are:
*
*
*
* -D
* Do not use ADTree data structure
*
*
*
* -B <BIF file>
* BIF file to compare with
*
*
*
* -Q weka.classifiers.bayes.net.search.SearchAlgorithm
* Search algorithm
*
*
*
* -E weka.classifiers.bayes.net.estimate.SimpleEstimator
* Estimator algorithm
*
*
*
*
* @author Remco Bouckaert ([email protected])
* @version $Revision: 10153 $
*/
public class BIFReader extends BayesNet implements TechnicalInformationHandler {
protected int[] m_nPositionX;
protected int[] m_nPositionY;
private int[] m_order;
/** for serialization */
static final long serialVersionUID = -8358864680379881429L;
/**
* This will return a string describing the classifier.
*
* @return The string.
*/
@Override
public String globalInfo() {
return "Builds a description of a Bayes Net classifier stored in XML "
+ "BIF 0.3 format.\n\n" + "For more details on XML BIF see:\n\n"
+ getTechnicalInformation().toString();
}
/**
* processFile reads a BIFXML file and initializes a Bayes Net
*
* @param sFile name of the file to parse
* @return the BIFReader
* @throws Exception if processing fails
*/
public BIFReader processFile(String sFile) throws Exception {
m_sFile = sFile;
DocumentBuilderFactory factory = DocumentBuilderFactory.newInstance();
factory.setValidating(true);
Document doc = factory.newDocumentBuilder().parse(new File(sFile));
doc.normalize();
buildInstances(doc, sFile);
buildStructure(doc);
return this;
} // processFile
public BIFReader processString(String sStr) throws Exception {
DocumentBuilderFactory factory = DocumentBuilderFactory.newInstance();
factory.setValidating(true);
Document doc = factory.newDocumentBuilder().parse(
new org.xml.sax.InputSource(new StringReader(sStr)));
doc.normalize();
buildInstances(doc, "from-string");
buildStructure(doc);
return this;
} // processString
/** the current filename */
String m_sFile;
/**
* returns the current filename
*
* @return the current filename
*/
public String getFileName() {
return m_sFile;
}
/**
* 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
*/
@Override
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
result = new TechnicalInformation(Type.MISC);
result.setValue(Field.AUTHOR,
"Fabio Cozman and Marek Druzdzel and Daniel Garcia");
result.setValue(Field.YEAR, "1998");
result.setValue(Field.TITLE, "XML BIF version 0.3");
result.setValue(Field.URL,
"http://www-2.cs.cmu.edu/~fgcozman/Research/InterchangeFormat/");
return result;
}
/**
* buildStructure parses the BIF document in the DOM tree contained in the doc
* parameter and specifies the the network structure and probability tables.
* It assumes that buildInstances has been called before
*
* @param doc DOM document containing BIF document in DOM tree
* @throws Exception if building of structure fails
*/
void buildStructure(Document doc) throws Exception {
// Get the name of the network
// initialize conditional distribution tables
m_Distributions = new Estimator[m_Instances.numAttributes()][];
for (int iNode = 0; iNode < m_Instances.numAttributes(); iNode++) {
// find definition that goes with this node
String sName = m_Instances.attribute(iNode).name();
Element definition = getDefinition(doc, sName);
/*
* if (nodelist.getLength() == 0) { throw new
* Exception("No definition found for node " + sName); } if
* (nodelist.getLength() > 1) {
* System.err.println("More than one definition found for node " + sName +
* ". Using first definition."); } Element definition = (Element)
* nodelist.item(0);
*/
// get the parents for this node
// resolve structure
ArrayList nodelist = getParentNodes(definition);
for (int iParent = 0; iParent < nodelist.size(); iParent++) {
Node parentName = nodelist.get(iParent).getFirstChild();
String sParentName = ((CharacterData) (parentName)).getData();
int nParent = getNode(sParentName);
m_ParentSets[iNode].addParent(nParent, m_Instances);
}
// resolve conditional probability table
int nCardinality = m_ParentSets[iNode].getCardinalityOfParents();
int nValues = m_Instances.attribute(iNode).numValues();
m_Distributions[iNode] = new Estimator[nCardinality];
for (int i = 0; i < nCardinality; i++) {
m_Distributions[iNode][i] = new DiscreteEstimatorBayes(nValues, 0.0f);
}
/*
* StringBuffer sTable = new StringBuffer(); for (int iText = 0; iText <
* nodelist.getLength(); iText++) { sTable.append(((CharacterData)
* (nodelist.item(iText))).getData()); sTable.append(' '); }
* StringTokenizer st = new StringTokenizer(sTable.toString());
*/
String sTable = getTable(definition);
StringTokenizer st = new StringTokenizer(sTable.toString());
for (int i = 0; i < nCardinality; i++) {
DiscreteEstimatorBayes d = (DiscreteEstimatorBayes) m_Distributions[iNode][i];
for (int iValue = 0; iValue < nValues; iValue++) {
String sWeight = st.nextToken();
d.addValue(iValue, new Double(sWeight).doubleValue());
}
}
}
} // buildStructure
/**
* synchronizes the node ordering of this Bayes network with those in the
* other network (if possible).
*
* @param other Bayes network to synchronize with
* @throws Exception if nr of attributes differs or not all of the variables
* have the same name.
*/
public void Sync(BayesNet other) throws Exception {
int nAtts = m_Instances.numAttributes();
if (nAtts != other.m_Instances.numAttributes()) {
throw new Exception(
"Cannot synchronize networks: different number of attributes.");
}
m_order = new int[nAtts];
for (int iNode = 0; iNode < nAtts; iNode++) {
String sName = other.getNodeName(iNode);
m_order[getNode(sName)] = iNode;
}
} // Sync
/**
* Returns all TEXT children of the given node in one string. Between the node
* values new lines are inserted.
*
* @param node the node to return the content for
* @return the content of the node
*/
public String getContent(Element node) {
NodeList list;
Node item;
int i;
String result;
result = "";
list = node.getChildNodes();
for (i = 0; i < list.getLength(); i++) {
item = list.item(i);
if (item.getNodeType() == Node.TEXT_NODE) {
result += "\n" + item.getNodeValue();
}
}
return result;
}
/**
* buildInstances parses the BIF document and creates a Bayes Net with its
* nodes specified, but leaves the network structure and probability tables
* empty.
*
* @param doc DOM document containing BIF document in DOM tree
* @param sName default name to give to the Bayes Net. Will be overridden if
* specified in the BIF document.
* @throws Exception if building fails
*/
void buildInstances(Document doc, String sName) throws Exception {
NodeList nodelist;
// Get the name of the network
nodelist = selectAllNames(doc);
if (nodelist.getLength() > 0) {
sName = ((CharacterData) (nodelist.item(0).getFirstChild())).getData();
}
// Process variables
nodelist = selectAllVariables(doc);
int nNodes = nodelist.getLength();
// initialize structure
ArrayList attInfo = new ArrayList(nNodes);
// Initialize
m_nPositionX = new int[nodelist.getLength()];
m_nPositionY = new int[nodelist.getLength()];
// Process variables
for (int iNode = 0; iNode < nodelist.getLength(); iNode++) {
// Get element
ArrayList valueslist;
// Get the name of the network
valueslist = selectOutCome(nodelist.item(iNode));
int nValues = valueslist.size();
// generate value strings
ArrayList nomStrings = new ArrayList(nValues + 1);
for (int iValue = 0; iValue < nValues; iValue++) {
Node node = valueslist.get(iValue).getFirstChild();
String sValue = ((CharacterData) (node)).getData();
if (sValue == null) {
sValue = "Value" + (iValue + 1);
}
nomStrings.add(sValue);
}
ArrayList nodelist2;
// Get the name of the network
nodelist2 = selectName(nodelist.item(iNode));
if (nodelist2.size() == 0) {
throw new Exception("No name specified for variable");
}
String sNodeName = ((CharacterData) (nodelist2.get(0).getFirstChild()))
.getData();
weka.core.Attribute att = new weka.core.Attribute(sNodeName, nomStrings);
attInfo.add(att);
valueslist = selectProperty(nodelist.item(iNode));
nValues = valueslist.size();
// generate value strings
for (int iValue = 0; iValue < nValues; iValue++) {
// parsing for strings of the form "position = (73, 165)"
Node node = valueslist.get(iValue).getFirstChild();
String sValue = ((CharacterData) (node)).getData();
if (sValue.startsWith("position")) {
int i0 = sValue.indexOf('(');
int i1 = sValue.indexOf(',');
int i2 = sValue.indexOf(')');
String sX = sValue.substring(i0 + 1, i1).trim();
String sY = sValue.substring(i1 + 1, i2).trim();
try {
m_nPositionX[iNode] = Integer.parseInt(sX);
m_nPositionY[iNode] = Integer.parseInt(sY);
} catch (NumberFormatException e) {
System.err.println("Wrong number format in position :(" + sX + ","
+ sY + ")");
m_nPositionX[iNode] = 0;
m_nPositionY[iNode] = 0;
}
}
}
}
m_Instances = new Instances(sName, attInfo, 100);
m_Instances.setClassIndex(nNodes - 1);
setUseADTree(false);
initStructure();
} // buildInstances
// /** selectNodeList selects list of nodes from document specified in XPath
// expression
// * @param doc : document (or node) to query
// * @param sXPath : XPath expression
// * @return list of nodes conforming to XPath expression in doc
// * @throws Exception
// */
// private NodeList selectNodeList(Node doc, String sXPath) throws Exception {
// NodeList nodelist = org.apache.xpath.XPathAPI.selectNodeList(doc, sXPath);
// return nodelist;
// } // selectNodeList
NodeList selectAllNames(Document doc) throws Exception {
// NodeList nodelist = selectNodeList(doc, "//NAME");
NodeList nodelist = doc.getElementsByTagName("NAME");
return nodelist;
} // selectAllNames
NodeList selectAllVariables(Document doc) throws Exception {
// NodeList nodelist = selectNodeList(doc, "//VARIABLE");
NodeList nodelist = doc.getElementsByTagName("VARIABLE");
return nodelist;
} // selectAllVariables
Element getDefinition(Document doc, String sName) throws Exception {
// NodeList nodelist = selectNodeList(doc,
// "//DEFINITION[normalize-space(FOR/text())=\"" + sName + "\"]");
NodeList nodelist = doc.getElementsByTagName("DEFINITION");
for (int iNode = 0; iNode < nodelist.getLength(); iNode++) {
Node node = nodelist.item(iNode);
ArrayList list = selectElements(node, "FOR");
if (list.size() > 0) {
Node forNode = list.get(0);
if (getContent((Element) forNode).trim().equals(sName)) {
return (Element) node;
}
}
}
throw new Exception("Could not find definition for ((" + sName + "))");
} // getDefinition
ArrayList getParentNodes(Node definition) throws Exception {
// NodeList nodelist = selectNodeList(definition, "GIVEN");
ArrayList nodelist = selectElements(definition, "GIVEN");
return nodelist;
} // getParentNodes
String getTable(Node definition) throws Exception {
// NodeList nodelist = selectNodeList(definition, "TABLE/text()");
ArrayList nodelist = selectElements(definition, "TABLE");
String sTable = getContent((Element) nodelist.get(0));
sTable = sTable.replaceAll("\\n", " ");
return sTable;
} // getTable
ArrayList selectOutCome(Node item) throws Exception {
// NodeList nodelist = selectNodeList(item, "OUTCOME");
ArrayList nodelist = selectElements(item, "OUTCOME");
return nodelist;
} // selectOutCome
ArrayList selectName(Node item) throws Exception {
// NodeList nodelist = selectNodeList(item, "NAME");
ArrayList nodelist = selectElements(item, "NAME");
return nodelist;
} // selectName
ArrayList selectProperty(Node item) throws Exception {
// NodeList nodelist = selectNodeList(item, "PROPERTY");
ArrayList nodelist = selectElements(item, "PROPERTY");
return nodelist;
} // selectProperty
ArrayList selectElements(Node item, String sElement) throws Exception {
NodeList children = item.getChildNodes();
ArrayList nodelist = new ArrayList();
for (int iNode = 0; iNode < children.getLength(); iNode++) {
Node node = children.item(iNode);
if ((node.getNodeType() == Node.ELEMENT_NODE)
&& node.getNodeName().equals(sElement)) {
nodelist.add(node);
}
}
return nodelist;
} // selectElements
/**
* Count nr of arcs missing from other network compared to current network
* Note that an arc is not 'missing' if it is reversed.
*
* @param other network to compare with
* @return nr of missing arcs
*/
public int missingArcs(BayesNet other) {
try {
Sync(other);
int nMissing = 0;
for (int iAttribute = 0; iAttribute < m_Instances.numAttributes(); iAttribute++) {
for (int iParent = 0; iParent < m_ParentSets[iAttribute]
.getNrOfParents(); iParent++) {
int nParent = m_ParentSets[iAttribute].getParent(iParent);
if (!other.getParentSet(m_order[iAttribute]).contains(
m_order[nParent])
&& !other.getParentSet(m_order[nParent]).contains(
m_order[iAttribute])) {
nMissing++;
}
}
}
return nMissing;
} catch (Exception e) {
System.err.println(e.getMessage());
return 0;
}
} // missingArcs
/**
* Count nr of exta arcs from other network compared to current network Note
* that an arc is not 'extra' if it is reversed.
*
* @param other network to compare with
* @return nr of missing arcs
*/
public int extraArcs(BayesNet other) {
try {
Sync(other);
int nExtra = 0;
for (int iAttribute = 0; iAttribute < m_Instances.numAttributes(); iAttribute++) {
for (int iParent = 0; iParent < other.getParentSet(m_order[iAttribute])
.getNrOfParents(); iParent++) {
int nParent = m_order[other.getParentSet(m_order[iAttribute])
.getParent(iParent)];
if (!m_ParentSets[iAttribute].contains(nParent)
&& !m_ParentSets[nParent].contains(iAttribute)) {
nExtra++;
}
}
}
return nExtra;
} catch (Exception e) {
System.err.println(e.getMessage());
return 0;
}
} // extraArcs
/**
* calculates the divergence between the probability distribution represented
* by this network and that of another, that is, \sum_{x\in X} P(x)log
* P(x)/Q(x) where X is the set of values the nodes in the network can take,
* P(x) the probability of this network for configuration x Q(x) the
* probability of the other network for configuration x
*
* @param other network to compare with
* @return divergence between this and other Bayes Network
*/
public double divergence(BayesNet other) {
try {
Sync(other);
// D: divergence
double D = 0.0;
int nNodes = m_Instances.numAttributes();
int[] nCard = new int[nNodes];
for (int iNode = 0; iNode < nNodes; iNode++) {
nCard[iNode] = m_Instances.attribute(iNode).numValues();
}
// x: holds current configuration of nodes
int[] x = new int[nNodes];
// simply sum over all configurations to calc divergence D
int i = 0;
while (i < nNodes) {
// update configuration
x[i]++;
while (i < nNodes && x[i] == m_Instances.attribute(i).numValues()) {
x[i] = 0;
i++;
if (i < nNodes) {
x[i]++;
}
}
if (i < nNodes) {
i = 0;
// calc P(x) and Q(x)
double P = 1.0;
for (int iNode = 0; iNode < nNodes; iNode++) {
int iCPT = 0;
for (int iParent = 0; iParent < m_ParentSets[iNode]
.getNrOfParents(); iParent++) {
int nParent = m_ParentSets[iNode].getParent(iParent);
iCPT = iCPT * nCard[nParent] + x[nParent];
}
P = P * m_Distributions[iNode][iCPT].getProbability(x[iNode]);
}
double Q = 1.0;
for (int iNode = 0; iNode < nNodes; iNode++) {
int iCPT = 0;
for (int iParent = 0; iParent < other.getParentSet(m_order[iNode])
.getNrOfParents(); iParent++) {
int nParent = m_order[other.getParentSet(m_order[iNode])
.getParent(iParent)];
iCPT = iCPT * nCard[nParent] + x[nParent];
}
Q = Q
* other.m_Distributions[m_order[iNode]][iCPT]
.getProbability(x[iNode]);
}
// update divergence if probabilities are positive
if (P > 0.0 && Q > 0.0) {
D = D + P * Math.log(Q / P);
}
}
}
return D;
} catch (Exception e) {
System.err.println(e.getMessage());
return 0;
}
} // divergence
/**
* Count nr of reversed arcs from other network compared to current network
*
* @param other network to compare with
* @return nr of missing arcs
*/
public int reversedArcs(BayesNet other) {
try {
Sync(other);
int nReversed = 0;
for (int iAttribute = 0; iAttribute < m_Instances.numAttributes(); iAttribute++) {
for (int iParent = 0; iParent < m_ParentSets[iAttribute]
.getNrOfParents(); iParent++) {
int nParent = m_ParentSets[iAttribute].getParent(iParent);
if (!other.getParentSet(m_order[iAttribute]).contains(
m_order[nParent])
&& other.getParentSet(m_order[nParent]).contains(
m_order[iAttribute])) {
nReversed++;
}
}
}
return nReversed;
} catch (Exception e) {
System.err.println(e.getMessage());
return 0;
}
} // reversedArcs
/**
* getNode finds the index of the node with name sNodeName and throws an
* exception if no such node can be found.
*
* @param sNodeName name of the node to get the index from
* @return index of the node with name sNodeName
* @throws Exception if node cannot be found
*/
public int getNode(String sNodeName) throws Exception {
int iNode = 0;
while (iNode < m_Instances.numAttributes()) {
if (m_Instances.attribute(iNode).name().equals(sNodeName)) {
return iNode;
}
iNode++;
}
throw new Exception("Could not find node [[" + sNodeName + "]]");
} // getNode
/**
* the default constructor
*/
public BIFReader() {
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 10153 $");
}
/**
* Loads the file specified as first parameter and prints it to stdout.
*
* @param args the command line parameters
*/
public static void main(String[] args) {
try {
BIFReader br = new BIFReader();
br.processFile(args[0]);
System.out.println(br.toString());
} catch (Throwable t) {
t.printStackTrace();
}
} // main
} // class BIFReader