weka.classifiers.bayes
Class NaiveBayesMultinomialText

java.lang.Object
  weka.classifiers.AbstractClassifier
      weka.classifiers.bayes.NaiveBayesMultinomialText

All Implemented Interfaces:

Serializable, Cloneable, Classifier, UpdateableClassifier, CapabilitiesHandler, OptionHandler, RevisionHandler, WeightedInstancesHandler

public class NaiveBayesMultinomialText
extends AbstractClassifier
implements UpdateableClassifier, WeightedInstancesHandler

Multinomial naive bayes for text data. Operates directly (and only) on String attributes. Other types of input attributes are accepted but ignored during training and classification

Valid options are:

 -W
  Use word frequencies instead of binary bag of words.

 -P <# instances>
  How often to prune the dictionary of low frequency words (default = 0, i.e. don't prune)

 -M <double>
  Minimum word frequency. Words with less than this frequence are ignored.
  If periodic pruning is turned on then this is also used to determine which
  words to remove from the dictionary (default = 3).

 -normalize
  Normalize document length (use in conjunction with -norm and -lnorm)

 -norm <num>
  Specify the norm that each instance must have (default 1.0)

 -lnorm <num>
  Specify L-norm to use (default 2.0)

 -lowercase
  Convert all tokens to lowercase before adding to the dictionary.

 -stoplist
  Ignore words that are in the stoplist.

 -stopwords <file>
  A file containing stopwords to override the default ones.
  Using this option automatically sets the flag ('-stoplist') to use the
  stoplist if the file exists.
  Format: one stopword per line, lines starting with '#'
  are interpreted as comments and ignored.

 -tokenizer <spec>
  The tokenizing algorihtm (classname plus parameters) to use.
  (default: weka.core.tokenizers.WordTokenizer)

 -stemmer <spec>
  The stemmering algorihtm (classname plus parameters) to use.

Author:

Mark Hall (mhall{[at]}pentaho{[dot]}com), Andrew Golightly (acg4@cs.waikato.ac.nz), Bernhard Pfahringer (bernhard@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

NaiveBayesMultinomialText()

Method Summary

void	buildClassifier(Instances data) 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.
double	getLNorm() Get the L Norm used.
boolean	getLowercaseTokens() Get whether to convert all tokens to lowercase
double	getMinWordFrequency() Get the minimum word frequency.
double	getNorm() Get the instance's Norm.
boolean	getNormalizeDocLength() Get whether to normalize the length of each document
String[]	getOptions() Gets the current settings of the classifier.
int	getPeriodicPruning() Get how often to prune the dictionary
String	getRevision() Returns the revision string.
Stemmer	getStemmer() Returns the current stemming algorithm, null if none is used.
File	getStopwords() returns the file used for obtaining the stopwords, if the file represents a directory then the default ones are used.
Tokenizer	getTokenizer() Returns the current tokenizer algorithm.
boolean	getUseStopList() Get whether to ignore all words that are on the stoplist.
boolean	getUseWordFrequencies() Get whether to use word frequencies rather than binary bag of words representation.
String	globalInfo() Returns a string describing classifier
Enumeration<Option>	listOptions() Returns an enumeration describing the available options.
String	LNormTipText() Returns the tip text for this property
String	lowercaseTokensTipText() Returns the tip text for this property
static void	main(String[] args) Main method for testing this class.
String	minWordFrequencyTipText() Returns the tip text for this property
String	normalizeDocLengthTipText() Returns the tip text for this property
String	normTipText() Returns the tip text for this property
String	periodicPruningTipText() Returns the tip text for this property
void	reset() Reset the classifier.
void	setLNorm(double newLNorm) Set the L-norm to used
void	setLowercaseTokens(boolean l) Set whether to convert all tokens to lowercase
void	setMinWordFrequency(double minFreq) Set the minimum word frequency.
void	setNorm(double newNorm) Set the norm of the instances
void	setNormalizeDocLength(boolean norm) Set whether to normalize the length of each document
void	setOptions(String[] options) Parses a given list of options.
void	setPeriodicPruning(int p) Set how often to prune the dictionary
void	setStemmer(Stemmer value) the stemming algorithm to use, null means no stemming at all (i.e., the NullStemmer is used).
void	setStopwords(File value) sets the file containing the stopwords, null or a directory unset the stopwords.
void	setTokenizer(Tokenizer value) the tokenizer algorithm to use.
void	setUseStopList(boolean u) Set whether to ignore all words that are on the stoplist.
void	setUseWordFrequencies(boolean u) Set whether to use word frequencies rather than binary bag of words representation.
String	stemmerTipText() Returns the tip text for this property.
String	stopwordsTipText() Returns the tip text for this property.
String	tokenizerTipText() Returns the tip text for this property.
String	toString() Returns a textual description of this classifier.
void	updateClassifier(Instance instance) Updates the classifier with the given instance.
String	useStopListTipText() Returns the tip text for this property
String	useWordFrequenciesTipText() Returns the tip text for this property

Methods inherited from class weka.classifiers.AbstractClassifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

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

Constructor Detail

NaiveBayesMultinomialText

public NaiveBayesMultinomialText()

Method Detail

globalInfo

public String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

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

buildClassifier

public void buildClassifier(Instances data)
                     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

updateClassifier

public void updateClassifier(Instance instance)
                      throws Exception

Updates the classifier with the given instance.

Specified by:

updateClassifier in interface UpdateableClassifier

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

reset

public void reset()

Reset the classifier.

setStemmer

public void setStemmer(Stemmer value)

the stemming algorithm to use, null means no stemming at all (i.e., the NullStemmer is used).

Parameters:

value - the configured stemming algorithm, or null

See Also:

NullStemmer

getStemmer

public Stemmer getStemmer()

Returns the current stemming algorithm, null if none is used.

Returns:

the current stemming algorithm, null if none set

stemmerTipText

public String stemmerTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setTokenizer

public void setTokenizer(Tokenizer value)

the tokenizer algorithm to use.

Parameters:

value - the configured tokenizing algorithm

getTokenizer

public Tokenizer getTokenizer()

Returns the current tokenizer algorithm.

Returns:

the current tokenizer algorithm

tokenizerTipText

public String tokenizerTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

useWordFrequenciesTipText

public String useWordFrequenciesTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setUseWordFrequencies

public void setUseWordFrequencies(boolean u)

Set whether to use word frequencies rather than binary bag of words representation.

Parameters:

u - true if word frequencies are to be used.

getUseWordFrequencies

public boolean getUseWordFrequencies()

Get whether to use word frequencies rather than binary bag of words representation.

Parameters:

u - true if word frequencies are to be used.

lowercaseTokensTipText

public String lowercaseTokensTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setLowercaseTokens

public void setLowercaseTokens(boolean l)

Set whether to convert all tokens to lowercase

Parameters:

l - true if all tokens are to be converted to lowercase

getLowercaseTokens

public boolean getLowercaseTokens()

Get whether to convert all tokens to lowercase

Returns:

true true if all tokens are to be converted to lowercase

periodicPruningTipText

public String periodicPruningTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setPeriodicPruning

public void setPeriodicPruning(int p)

Set how often to prune the dictionary

Parameters:

p - how often to prune

getPeriodicPruning

public int getPeriodicPruning()

Get how often to prune the dictionary

Returns:

how often to prune the dictionary

minWordFrequencyTipText

public String minWordFrequencyTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setMinWordFrequency

public void setMinWordFrequency(double minFreq)

Set the minimum word frequency. Words that don't occur at least min freq times are ignored when updating weights. If periodic pruning is turned on, then min frequency is used when removing words from the dictionary.

Parameters:

minFreq - the minimum word frequency to use

getMinWordFrequency

public double getMinWordFrequency()

Get the minimum word frequency. Words that don't occur at least min freq times are ignored when updating weights. If periodic pruning is turned on, then min frequency is used when removing words from the dictionary.

Parameters:

return - the minimum word frequency to use

normalizeDocLengthTipText

public String normalizeDocLengthTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setNormalizeDocLength

public void setNormalizeDocLength(boolean norm)

Set whether to normalize the length of each document

Parameters:

norm - true if document lengths is to be normalized

getNormalizeDocLength

public boolean getNormalizeDocLength()

Get whether to normalize the length of each document

Returns:

true if document lengths is to be normalized

normTipText

public String normTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getNorm

public double getNorm()

Get the instance's Norm.

Returns:

the Norm

setNorm

public void setNorm(double newNorm)

Set the norm of the instances

Parameters:

newNorm - the norm to wich the instances must be set

LNormTipText

public String LNormTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getLNorm

public double getLNorm()

Get the L Norm used.

Returns:

the L-norm used

setLNorm

public void setLNorm(double newLNorm)

Set the L-norm to used

Parameters:

newLNorm - the L-norm

useStopListTipText

public String useStopListTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setUseStopList

public void setUseStopList(boolean u)

Set whether to ignore all words that are on the stoplist.

Parameters:

u - true to ignore all words on the stoplist.

getUseStopList

public boolean getUseStopList()

Get whether to ignore all words that are on the stoplist.

Returns:

true to ignore all words on the stoplist.

setStopwords

public void setStopwords(File value)

sets the file containing the stopwords, null or a directory unset the stopwords. If the file exists, it automatically turns on the flag to use the stoplist.

Parameters:

value - the file containing the stopwords

getStopwords

public File getStopwords()

returns the file used for obtaining the stopwords, if the file represents a directory then the default ones are used.

Returns:

the file containing the stopwords

stopwordsTipText

public String stopwordsTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

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:

 -W
  Use word frequencies instead of binary bag of words.

 -P <# instances>
  How often to prune the dictionary of low frequency words (default = 0, i.e. don't prune)

 -M <double>
  Minimum word frequency. Words with less than this frequence are ignored.
  If periodic pruning is turned on then this is also used to determine which
  words to remove from the dictionary (default = 3).

 -normalize
  Normalize document length (use in conjunction with -norm and -lnorm)

 -norm <num>
  Specify the norm that each instance must have (default 1.0)

 -lnorm <num>
  Specify L-norm to use (default 2.0)

 -lowercase
  Convert all tokens to lowercase before adding to the dictionary.

 -stoplist
  Ignore words that are in the stoplist.

 -stopwords <file>
  A file containing stopwords to override the default ones.
  Using this option automatically sets the flag ('-stoplist') to use the
  stoplist if the file exists.
  Format: one stopword per line, lines starting with '#'
  are interpreted as comments and ignored.

 -tokenizer <spec>
  The tokenizing algorihtm (classname plus parameters) to use.
  (default: weka.core.tokenizers.WordTokenizer)

 -stemmer <spec>
  The stemmering algorihtm (classname plus parameters) to use.

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

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

toString

public String toString()

Returns a textual description of this classifier.

Overrides:

toString in class Object

Returns:

a textual description of this classifier.

getRevision

public 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(String[] args)

Main method for testing this class.

Parameters:

args - the options