weka.classifiers.bayes

Class NaiveBayesMultinomial

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.bayes.NaiveBayesMultinomial

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

Direct Known Subclasses:

NaiveBayesMultinomialUpdateable

public class NaiveBayesMultinomial
extends AbstractClassifier
implements WeightedInstancesHandler, TechnicalInformationHandler

Class for building and using a multinomial Naive Bayes classifier. For more information see,

Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.

The core equation for this classifier:

P[Ci|D] = (P[D|Ci] x P[Ci]) / P[D] (Bayes rule)

where Ci is class i and D is a document.

BibTeX:

 @inproceedings{Mccallum1998,
    author = {Andrew Mccallum and Kamal Nigam},
    booktitle = {AAAI-98 Workshop on 'Learning for Text Categorization'},
    title = {A Comparison of Event Models for Naive Bayes Text Classification},
    year = {1998}
 }
 

Valid options are:

-output-debug-info
If set, classifier is run in debug mode and may output additional info to the console.

-do-not-check-capabilities
If set, classifier capabilities are not checked before classifier is built (use with caution).

-num-decimal-laces
The number of decimal places for the output of numbers in the model.

-batch-size
The desired batch size for batch prediction.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected Instances	m_headerInfo copy of header information for use in toString method
protected int	m_numAttributes number of unique words
protected int	m_numClasses number of class values
protected double[]	m_probOfClass the probability of a class (i.e.
protected double[][]	m_probOfWordGivenClass probability that a word (w) exists in a class (H) (i.e.

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
NaiveBayesMultinomial()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances instances) Generates the classifier.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
java.lang.String	getRevision() Returns the revision string.
TechnicalInformation	getTechnicalInformation() 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.
java.lang.String	globalInfo() Returns a string describing this classifier
protected void	initializeClassifier(Instances instances) Sets up the classifier before any actual instances are processed.
static void	main(java.lang.String[] argv) Main method for testing this class.
protected double	probOfDocGivenClass(Instance inst, int classIndex) log(N!)
java.lang.String	toString() Returns a string representation of the classifier.

Methods inherited from class weka.classifiers.AbstractClassifier

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

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_probOfWordGivenClass

protected double[][] m_probOfWordGivenClass

probability that a word (w) exists in a class (H) (i.e. Pr[w|H]) The matrix is in the this format: probOfWordGivenClass[class][wordAttribute] NOTE: the values are actually the log of Pr[w|H]

m_probOfClass

protected double[] m_probOfClass

the probability of a class (i.e. Pr[H]).

m_numAttributes

protected int m_numAttributes

number of unique words

m_numClasses

protected int m_numClasses

number of class values

m_headerInfo

protected Instances m_headerInfo

copy of header information for use in toString method

Constructor Detail

NaiveBayesMultinomial

public NaiveBayesMultinomial()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this classifier

Returns:

a description of the classifier suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()

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.

Specified by:

getTechnicalInformation in interface TechnicalInformationHandler

Returns:

the technical information about this class

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface Classifier

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class AbstractClassifier

Returns:

the capabilities of this classifier

See Also:

Capabilities

initializeClassifier

protected void initializeClassifier(Instances instances)
                             throws java.lang.Exception

Sets up the classifier before any actual instances are processed.

Throws:

java.lang.Exception

buildClassifier

public void buildClassifier(Instances instances)
                     throws java.lang.Exception

Generates the classifier.

Specified by:

buildClassifier in interface Classifier

Parameters:

instances - set of instances serving as training data

Throws:

java.lang.Exception - if the classifier has not been generated successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.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:

java.lang.Exception - if there is a problem generating the prediction

probOfDocGivenClass

protected double probOfDocGivenClass(Instance inst,
                                     int classIndex)

log(N!) + (sum for all the words i)(log(Pi^ni) - log(ni!)) where N is the total number of words Pi is the probability of obtaining word i ni is the number of times the word at index i occurs in the document Actually, this method just computes (sum for all the words i)(log(Pi^ni) because the factorials are irrelevant when posterior class probabilities are computed.

Parameters:

inst - The instance to be classified

classIndex - The index of the class we are calculating the probability with respect to

Returns:

The log of the probability of the document occuring given the class

toString

public java.lang.String toString()

Returns a string representation of the classifier.

Overrides:

toString in class java.lang.Object

Returns:

a string representation of the classifier

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AbstractClassifier

Returns:

the revision

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - the options