NaiveBayesMultinomialText (interruptible-weka 0.1.2 API)

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

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, Classifier, UpdateableBatchProcessor, UpdateableClassifier, Aggregateable<NaiveBayesMultinomialText>, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, WeightedInstancesHandler

public class NaiveBayesMultinomialText
extends AbstractClassifier
implements UpdateableClassifier, UpdateableBatchProcessor, WeightedInstancesHandler, Aggregateable<NaiveBayesMultinomialText>

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.

  -stopwords-handler
  The stopwords handler to use (default Null).

  -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.

  -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).

See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected Instances`	`m_data` The header of the training data
`protected java.util.LinkedHashMap<java.lang.String,weka.classifiers.bayes.NaiveBayesMultinomialText.Count>`	`m_inputVector` Holds the current document vector (LinkedHashMap is more efficient when iterating over EntrySet than HashMap)
`protected double`	`m_leplace` Leplace-like correction factor for zero frequency
`protected double`	`m_lnorm` The L-norm to use
`protected boolean`	`m_lowercaseTokens` Whether or not to convert all tokens to lowercase
`protected double`	`m_minWordP` Only consider dictionary words (features) that occur at least this many times
`protected double`	`m_norm` The length that each document vector should have in the end
`protected boolean`	`m_normalize` normailize document length ?
`protected int`	`m_numModels`
`protected int`	`m_periodicP` The number of training instances at which to periodically prune the dictionary of min frequency words.
`protected double[]`	`m_probOfClass`
`protected java.util.Map<java.lang.Integer,java.util.LinkedHashMap<java.lang.String,weka.classifiers.bayes.NaiveBayesMultinomialText.Count>>`	`m_probOfWordGivenClass`
`protected Stemmer`	`m_stemmer` The stemming algorithm.
`protected StopwordsHandler`	`m_StopwordsHandler` Stopword handler to use.
`protected double`	`m_t` Holds the current instance number
`protected Tokenizer`	`m_tokenizer` The tokenizer to use
`protected boolean`	`m_wordFrequencies` Use word frequencies rather than bag-of-words if true
`protected double[]`	`m_wordsPerClass`

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

NaiveBayesMultinomialText()

Constructors
Constructor and Description
`NaiveBayesMultinomialText()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`NaiveBayesMultinomialText`	`aggregate(NaiveBayesMultinomialText toAggregate)` Aggregate an object with this one
`void`	`batchFinished()` Signal that the training data is finished (for now).
`void`	`buildClassifier(Instances data)` Generates the classifier.
`double[]`	`distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`void`	`finalizeAggregation()` Call to complete the aggregation process.
`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
`java.lang.String[]`	`getOptions()` Gets the current settings of the classifier.
`int`	`getPeriodicPruning()` Get how often to prune the dictionary
`java.lang.String`	`getRevision()` Returns the revision string.
`Stemmer`	`getStemmer()` Returns the current stemming algorithm, null if none is used.
`StopwordsHandler`	`getStopwordsHandler()` Gets the stopwords handler.
`Tokenizer`	`getTokenizer()` Returns the current tokenizer algorithm.
`boolean`	`getUseWordFrequencies()` Get whether to use word frequencies rather than binary bag of words representation.
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`java.lang.String`	`LNormTipText()` Returns the tip text for this property
`java.lang.String`	`lowercaseTokensTipText()` Returns the tip text for this property
`static void`	`main(java.lang.String[] args)` Main method for testing this class.
`java.lang.String`	`minWordFrequencyTipText()` Returns the tip text for this property
`java.lang.String`	`normalizeDocLengthTipText()` Returns the tip text for this property
`java.lang.String`	`normTipText()` Returns the tip text for this property
`java.lang.String`	`periodicPruningTipText()` Returns the tip text for this property
`protected void`	`pruneDictionary(boolean force)`
`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(java.lang.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`	`setStopwordsHandler(StopwordsHandler value)` Sets the stopwords handler to use.
`void`	`setTokenizer(Tokenizer value)` the tokenizer algorithm to use.
`void`	`setUseWordFrequencies(boolean u)` Set whether to use word frequencies rather than binary bag of words representation.
`java.lang.String`	`stemmerTipText()` Returns the tip text for this property.
`java.lang.String`	`stopwordsHandlerTipText()` Returns the tip text for this property.
`protected void`	`tokenizeInstance(Instance instance, boolean updateDictionary)`
`java.lang.String`	`tokenizerTipText()` Returns the tip text for this property.
`java.lang.String`	`toString()` Returns a textual description of this classifier.
`void`	`updateClassifier(Instance instance)` Updates the classifier with the given instance.
`protected void`	`updateClassifier(Instance instance, boolean updateDictionary)`
`java.lang.String`	`useWordFrequenciesTipText()` Returns the tip text for this property

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - m_data
```
protected Instances m_data
```
    The header of the training data
  - m_probOfClass
```
protected double[] m_probOfClass
```
  - m_wordsPerClass
```
protected double[] m_wordsPerClass
```
  - m_probOfWordGivenClass
```
protected java.util.Map<java.lang.Integer,java.util.LinkedHashMap<java.lang.String,weka.classifiers.bayes.NaiveBayesMultinomialText.Count>> m_probOfWordGivenClass
```
  - m_inputVector
```
protected transient java.util.LinkedHashMap<java.lang.String,weka.classifiers.bayes.NaiveBayesMultinomialText.Count> m_inputVector
```
    Holds the current document vector (LinkedHashMap is more efficient when iterating over EntrySet than HashMap)
  - m_StopwordsHandler
```
protected StopwordsHandler m_StopwordsHandler
```
    Stopword handler to use.
  - m_tokenizer
```
protected Tokenizer m_tokenizer
```
    The tokenizer to use
  - m_lowercaseTokens
```
protected boolean m_lowercaseTokens
```
    Whether or not to convert all tokens to lowercase
  - m_stemmer
```
protected Stemmer m_stemmer
```
    The stemming algorithm.
  - m_periodicP
```
protected int m_periodicP
```
    The number of training instances at which to periodically prune the dictionary of min frequency words. Empty or null string indicates don't prune
  - m_minWordP
```
protected double m_minWordP
```
    Only consider dictionary words (features) that occur at least this many times
  - m_wordFrequencies
```
protected boolean m_wordFrequencies
```
    Use word frequencies rather than bag-of-words if true
  - m_normalize
```
protected boolean m_normalize
```
    normailize document length ?
  - m_norm
```
protected double m_norm
```
    The length that each document vector should have in the end
  - m_lnorm
```
protected double m_lnorm
```
    The L-norm to use
  - m_leplace
```
protected double m_leplace
```
    Leplace-like correction factor for zero frequency
  - m_t
```
protected double m_t
```
    Holds the current instance number
  - m_numModels
```
protected int m_numModels
```
- Constructor Detail
  - NaiveBayesMultinomialText
```
public NaiveBayesMultinomialText()
```
- Method Detail
  - globalInfo
```
public java.lang.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 java.lang.Exception
```
    Generates the classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    data - set of instances serving as training data
    
    Throws:
    
    java.lang.Exception - if the classifier has not been generated successfully
  - updateClassifier
```
public void updateClassifier(Instance instance)
                      throws java.lang.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:
    
    java.lang.Exception - if the instance could not be incorporated in the model.
  - updateClassifier
```
protected void updateClassifier(Instance instance,
                                boolean updateDictionary)
                         throws java.lang.Exception
```
    Throws:
    
    java.lang.Exception
  - 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
  - tokenizeInstance
```
protected void tokenizeInstance(Instance instance,
                                boolean updateDictionary)
```
  - pruneDictionary
```
protected void pruneDictionary(boolean force)
```
  - 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 java.lang.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 java.lang.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 java.lang.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.
    
    Returns:
    
    true if word frequencies are to be used.
  - lowercaseTokensTipText
```
public java.lang.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 java.lang.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 java.lang.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.
    
    Returns:
    
    the minimum word frequency to use
  - normalizeDocLengthTipText
```
public java.lang.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 java.lang.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 java.lang.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
  - setStopwordsHandler
```
public void setStopwordsHandler(StopwordsHandler value)
```
    Sets the stopwords handler to use.
    
    Parameters:
    
    value - the stopwords handler, if null, Null is used
  - getStopwordsHandler
```
public StopwordsHandler getStopwordsHandler()
```
    Gets the stopwords handler.
    
    Returns:
    
    the stopwords handler
  - stopwordsHandlerTipText
```
public java.lang.String stopwordsHandlerTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - listOptions
```
public java.util.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(java.lang.String[] options)
                throws java.lang.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.
 
```
```
  -stopwords-handler
  The stopwords handler to use (default Null).
 
```
```
  -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.
 
```
```
  -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).
 
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class AbstractClassifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - getOptions
```
public java.lang.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 java.lang.String toString()
```
    Returns a textual description of this classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a textual description of this 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
  - aggregate
```
public NaiveBayesMultinomialText aggregate(NaiveBayesMultinomialText toAggregate)
                                    throws java.lang.Exception
```
    Description copied from interface: Aggregateable
    
    Aggregate an object with this one
    
    Specified by:
    
    aggregate in interface Aggregateable<NaiveBayesMultinomialText>
    
    Parameters:
    
    toAggregate - the object to aggregate
    
    Returns:
    
    the result of aggregation
    
    Throws:
    
    java.lang.Exception - if the supplied object can't be aggregated for some reason
  - finalizeAggregation
```
public void finalizeAggregation()
                         throws java.lang.Exception
```
    Description copied from interface: Aggregateable
    
    Call to complete the aggregation process. Allows implementers to do any final processing based on how many objects were aggregated.
    
    Specified by:
    
    finalizeAggregation in interface Aggregateable<NaiveBayesMultinomialText>
    
    Throws:
    
    java.lang.Exception - if the aggregation can't be finalized for some reason
  - batchFinished
```
public void batchFinished()
                   throws java.lang.Exception
```
    Description copied from interface: UpdateableBatchProcessor
    
    Signal that the training data is finished (for now).
    
    Specified by:
    
    batchFinished in interface UpdateableBatchProcessor
    
    Throws:
    
    java.lang.Exception - if a problem occurs
  - main
```
public static void main(java.lang.String[] args)
```
    Main method for testing this class.
    
    Parameters:
    
    args - the options

Class NaiveBayesMultinomialText

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Field Detail

m_data

m_probOfClass

m_wordsPerClass

m_probOfWordGivenClass

m_inputVector

m_StopwordsHandler

m_tokenizer

m_lowercaseTokens

m_stemmer

m_periodicP

m_minWordP

m_wordFrequencies

m_normalize

m_norm

m_lnorm

m_leplace

m_t

m_numModels

Constructor Detail

NaiveBayesMultinomialText

Method Detail

globalInfo

getCapabilities

buildClassifier

updateClassifier

updateClassifier

distributionForInstance

tokenizeInstance

pruneDictionary

reset

setStemmer

getStemmer

stemmerTipText

setTokenizer

getTokenizer

tokenizerTipText

useWordFrequenciesTipText

setUseWordFrequencies

getUseWordFrequencies

lowercaseTokensTipText

setLowercaseTokens

getLowercaseTokens

periodicPruningTipText

setPeriodicPruning

getPeriodicPruning

minWordFrequencyTipText

setMinWordFrequency

getMinWordFrequency

normalizeDocLengthTipText

setNormalizeDocLength

getNormalizeDocLength

normTipText

getNorm

setNorm

LNormTipText

getLNorm

setLNorm

setStopwordsHandler

getStopwordsHandler

stopwordsHandlerTipText

listOptions

setOptions

getOptions

toString

getRevision

aggregate

finalizeAggregation

batchFinished

main