Package weka.classifiers.bayes

Class BayesNet

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.bayes.BayesNet

All Implemented Interfaces:

Serializable, Cloneable, Classifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, Drawable, OptionHandler, RevisionHandler, WeightedInstancesHandler

Direct Known Subclasses:

BIFReader, EditableBayesNet

public class BayesNet
extends AbstractClassifier
implements OptionHandler, WeightedInstancesHandler, Drawable, AdditionalMeasureProducer

Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. Provides datastructures (network structure, conditional probability distributions, etc.) and facilities common to Bayes Network learning algorithms like K2 and B.

For more information see:

http://sourceforge.net/projects/weka/files/documentation/WekaManual-3-7-0.pdf /download

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
 

Version:

$Revision: 15519 $

Author:

Remco Bouckaert (rrb@xm.co.nz)

See Also:

• Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
Estimator[][]	m_Distributions	The attribute estimators containing CPTs.
Instances	m_Instances	The dataset header for the purposes of printing out a semi-intelligible model

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Fields inherited from interface weka.core.Drawable

BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

Constructors

Constructor	Description
BayesNet()

Method Summary

Modifier and Type	Method	Description
String	BIFFileTipText()
void	buildClassifier(Instances instances)	Generates the classifier.
void	buildStructure()	buildStructure determines the network structure/graph of the network.
double[]	countsForInstance(Instance instance)	Calculates the counts for Dirichlet distribution for the class membership probabilities for the given test instance.
double[]	distributionForInstance(Instance instance)	Calculates the class membership probabilities for the given test instance.
Enumeration<String>	enumerateMeasures()	Returns an enumeration of the measure names.
void	estimateCPTs()	estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
String	estimatorTipText()	This will return a string describing the BayesNetEstimator.
ADNode	getADTree()	get ADTree strucrture containing efficient representation of counts.
String	getBIFFile()	Get name of network in BIF file to compare with
String	getBIFHeader()
Capabilities	getCapabilities()	Returns default capabilities of the classifier.
int	getCardinality(int iNode)	get number of values a node can take
Estimator[][]	getDistributions()	Get full set of estimators.
BayesNetEstimator	getEstimator()	Get the BayesNetEstimator used for calculating the CPTs
double	getMeasure(String measureName)	Returns the value of the named measure
String	getName()	get name of the Bayes network
String	getNodeName(int iNode)	get name of a node in the Bayes network
String	getNodeValue(int iNode, int iValue)	get name of a particular value of a node
int	getNrOfNodes()	get number of nodes in the Bayes network
int	getNrOfParents(int iNode)	get number of parents of a node in the network structure
int	getNumInstances()	Returns the number of instances the model was built from.
String[]	getOptions()	Gets the current settings of the classifier.
int	getParent(int iNode, int iParent)	get node index of a parent of a node in the network structure
int	getParentCardinality(int iNode)	get number of values the collection of parents of a node can take
ParentSet	getParentSet(int iNode)	get the parent set of a node
ParentSet[]	getParentSets()	Get full set of parent sets.
double	getProbability(int iNode, int iParent, int iValue)	get particular probability of the conditional probability distribtion of a node given its parents.
String	getRevision()	Returns the revision string.
SearchAlgorithm	getSearchAlgorithm()	Get the SearchAlgorithm used as the search algorithm
boolean	getUseADTree()	Method declaration
String	globalInfo()	This will return a string describing the classifier.
String	graph()	Returns a BayesNet graph in XMLBIF ver 0.3 format.
int	graphType()	Returns the type of graph this classifier represents.
void	initCPTs()	initializes the conditional probabilities
void	initStructure()	Init structure initializes the structure to an empty graph or a Naive Bayes graph (depending on the -N flag).
Enumeration<Option>	listOptions()	Returns an enumeration describing the available options
static void	main(String[] argv)	Main method for testing this class.
double	measureAICScore()
double	measureBayesScore()
double	measureBDeuScore()
double	measureDivergence()
double	measureEntropyScore()
double	measureExtraArcs()
double	measureMDLScore()
double	measureMissingArcs()
double	measureReversedArcs()
static String[]	partitionOptions(String[] options)	Returns the secondary set of options (if any) contained in the supplied options array.
String	searchAlgorithmTipText()
void	setBIFFile(String sBIFFile)	Set name of network in BIF file to compare with
void	setEstimator(BayesNetEstimator newBayesNetEstimator)	Set the Estimator Algorithm used in calculating the CPTs
void	setOptions(String[] options)	Parses a given list of options.
void	setSearchAlgorithm(SearchAlgorithm newSearchAlgorithm)	Set the SearchAlgorithm used in searching for network structures.
void	setUseADTree(boolean bUseADTree)	Set whether ADTree structure is used or not
String	toString()	Returns a description of the classifier.
String	toXMLBIF03()	Returns a description of the classifier in XML BIF 0.3 format.
void	updateClassifier(Instance instance)	Updates the classifier with the given instance.
String	useADTreeTipText()

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Details

m_Distributions

public Estimator[][] m_Distributions

The attribute estimators containing CPTs.

m_Instances

public Instances m_Instances

The dataset header for the purposes of printing out a semi-intelligible model

Constructor Details

BayesNet

public BayesNet()

Method Details

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface CapabilitiesHandler

Specified by:

getCapabilities in interface Classifier

Overrides:

getCapabilities in class AbstractClassifier

Returns:

the capabilities of this classifier

See Also:

• Capabilities

buildClassifier

public void buildClassifier(Instances instances)
                     throws Exception

Generates the classifier.

Specified by:

buildClassifier in interface Classifier

Parameters:

instances - set of instances serving as training data

Throws:

Exception - if the classifier has not been generated successfully

getNumInstances

public int getNumInstances()

Returns the number of instances the model was built from.

initStructure

public void initStructure()
                   throws Exception

Init structure initializes the structure to an empty graph or a Naive Bayes graph (depending on the -N flag).

Throws:

Exception - in case of an error

buildStructure

public void buildStructure()
                    throws Exception

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.

Throws:

Exception - in case of an error

estimateCPTs

public void estimateCPTs()
                  throws Exception

estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.

Throws:

Exception - in case of an error

initCPTs

public void initCPTs()
              throws Exception

initializes the conditional probabilities

Throws:

Exception - in case of an error

updateClassifier

public void updateClassifier(Instance instance)
                      throws Exception

Updates the classifier with the given instance.

Parameters:

instance - the new training instance to include in the model

Throws:

Exception - if the instance could not be incorporated in the model.

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws Exception

Calculates the class membership probabilities for the given test instance.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

predicted class probability distribution

Throws:

Exception - if there is a problem generating the prediction

countsForInstance

public double[] countsForInstance(Instance instance)
                           throws Exception

Calculates the counts for Dirichlet distribution for the class membership probabilities for the given test instance.

Parameters:

instance - the instance to be classified

Returns:

counts for Dirichlet distribution for class probability

Throws:

Exception - if there is a problem generating the prediction

listOptions

public Enumeration<Option> listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class AbstractClassifier

Returns:

an enumeration of all the available options

setOptions

public void setOptions(String[] options)
                throws Exception

Parses a given list of options.

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
 

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class AbstractClassifier

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

partitionOptions

public static String[] partitionOptions(String[] options)

Returns the secondary set of options (if any) contained in the supplied options array. The secondary set is defined to be any options after the first "--" but before the "-E". These options are removed from the original options array.

Parameters:

options - the input array of options

Returns:

the array of secondary options

getOptions

public String[] getOptions()

Gets the current settings of the classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class AbstractClassifier

Returns:

an array of strings suitable for passing to setOptions

setSearchAlgorithm

public void setSearchAlgorithm(SearchAlgorithm newSearchAlgorithm)

Set the SearchAlgorithm used in searching for network structures.

Parameters:

newSearchAlgorithm - the SearchAlgorithm to use.

getSearchAlgorithm

public SearchAlgorithm getSearchAlgorithm()

Get the SearchAlgorithm used as the search algorithm

Returns:

the SearchAlgorithm used as the search algorithm

setEstimator

public void setEstimator(BayesNetEstimator newBayesNetEstimator)

Set the Estimator Algorithm used in calculating the CPTs

Parameters:

newBayesNetEstimator - the Estimator to use.

getEstimator

public BayesNetEstimator getEstimator()

Get the BayesNetEstimator used for calculating the CPTs

Returns:

the BayesNetEstimator used.

setUseADTree

public void setUseADTree(boolean bUseADTree)

Set whether ADTree structure is used or not

Parameters:

bUseADTree - true if an ADTree structure is used

getUseADTree

public boolean getUseADTree()

Method declaration

Returns:

whether ADTree structure is used or not

setBIFFile

public void setBIFFile(String sBIFFile)

Set name of network in BIF file to compare with

Parameters:

sBIFFile - the name of the BIF file

getBIFFile

public String getBIFFile()

Get name of network in BIF file to compare with

Returns:

BIF file name

toString

public String toString()

Returns a description of the classifier.

Overrides:

toString in class Object

Returns:

a description of the classifier as a string.

graphType

public int graphType()

Returns the type of graph this classifier represents.

Specified by:

graphType in interface Drawable

Returns:

Drawable.TREE

graph

public String graph()
             throws Exception

Returns a BayesNet graph in XMLBIF ver 0.3 format.

Specified by:

graph in interface Drawable

Returns:

String representing this BayesNet in XMLBIF ver 0.3

Throws:

Exception - in case BIF generation fails

getBIFHeader

public String getBIFHeader()

toXMLBIF03

public String toXMLBIF03()

Returns a description of the classifier in XML BIF 0.3 format. See http://www-2.cs.cmu.edu/~fgcozman/Research/InterchangeFormat/ for details on XML BIF.

Returns:

an XML BIF 0.3 description of the classifier as a string.

useADTreeTipText

public String useADTreeTipText()

Returns:

a string to describe the UseADTreeoption.

searchAlgorithmTipText

public String searchAlgorithmTipText()

Returns:

a string to describe the SearchAlgorithm.

estimatorTipText

public String estimatorTipText()

This will return a string describing the BayesNetEstimator.

Returns:

The string.

BIFFileTipText

public String BIFFileTipText()

Returns:

a string to describe the BIFFile.

globalInfo

public String globalInfo()

This will return a string describing the classifier.

Returns:

The string.

main

public static void main(String[] argv)

Main method for testing this class.

Parameters:

argv - the options

getName

public String getName()

get name of the Bayes network

Returns:

name of the Bayes net

getNrOfNodes

public int getNrOfNodes()

get number of nodes in the Bayes network

Returns:

number of nodes

getNodeName

public String getNodeName(int iNode)

get name of a node in the Bayes network

Parameters:

iNode - index of the node

Returns:

name of the specified node

getCardinality

public int getCardinality(int iNode)

get number of values a node can take

Parameters:

iNode - index of the node

Returns:

cardinality of the specified node

getNodeValue

public String getNodeValue(int iNode,
 int iValue)

get name of a particular value of a node

Parameters:

iNode - index of the node

iValue - index of the value

Returns:

cardinality of the specified node

getNrOfParents

public int getNrOfParents(int iNode)

get number of parents of a node in the network structure

Parameters:

iNode - index of the node

Returns:

number of parents of the specified node

getParent

public int getParent(int iNode,
 int iParent)

get node index of a parent of a node in the network structure

Parameters:

iNode - index of the node

iParent - index of the parents, e.g., 0 is the first parent, 1 the second parent, etc.

Returns:

node index of the iParent's parent of the specified node

getParentSets

public ParentSet[] getParentSets()

Get full set of parent sets.

Returns:

parent sets;

getDistributions

public Estimator[][] getDistributions()

Get full set of estimators.

Returns:

estimators;

getParentCardinality

public int getParentCardinality(int iNode)

get number of values the collection of parents of a node can take

Parameters:

iNode - index of the node

Returns:

cardinality of the parent set of the specified node

getProbability

public double getProbability(int iNode,
 int iParent,
 int iValue)

get particular probability of the conditional probability distribtion of a node given its parents.

Parameters:

iNode - index of the node

iParent - index of the parent set, 0 <= iParent <= getParentCardinality(iNode)

iValue - index of the value, 0 <= iValue <= getCardinality(iNode)

Returns:

probability

getParentSet

public ParentSet getParentSet(int iNode)

get the parent set of a node

Parameters:

iNode - index of the node

Returns:

Parent set of the specified node.

getADTree

public ADNode getADTree()

get ADTree strucrture containing efficient representation of counts.

Returns:

ADTree strucrture

enumerateMeasures

public Enumeration<String> enumerateMeasures()

Returns an enumeration of the measure names. Additional measures must follow the naming convention of starting with "measure", eg. double measureBlah()

Specified by:

enumerateMeasures in interface AdditionalMeasureProducer

Returns:

an enumeration of the measure names

measureExtraArcs

public double measureExtraArcs()

measureMissingArcs

public double measureMissingArcs()

measureReversedArcs

public double measureReversedArcs()

measureDivergence

public double measureDivergence()

measureBayesScore

public double measureBayesScore()

measureBDeuScore

public double measureBDeuScore()

measureMDLScore

public double measureMDLScore()

measureAICScore

public double measureAICScore()

measureEntropyScore

public double measureEntropyScore()

getMeasure

public double getMeasure(String measureName)

Returns the value of the named measure

Specified by:

getMeasure in interface AdditionalMeasureProducer

Parameters:

measureName - the name of the measure to query for its value

Returns:

the value of the named measure

Throws:

IllegalArgumentException - if the named measure is not supported

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AbstractClassifier

Returns:

the revision