weka.filters.supervised.attribute

Class ClassConditionalProbabilities

java.lang.Object

weka.filters.Filter

weka.filters.SimpleFilter

weka.filters.SimpleBatchFilter

weka.filters.supervised.attribute.ClassConditionalProbabilities

All Implemented Interfaces:

java.io.Serializable, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, WeightedAttributesHandler, WeightedInstancesHandler

public class ClassConditionalProbabilities
extends SimpleBatchFilter
implements WeightedAttributesHandler, WeightedInstancesHandler

Converts the values of nominal and/or numeric attributes into class conditional probabilities. If there are k classes, then k new attributes are created for each of the original ones, giving pr(att val | class k).

Can be useful for converting nominal attributes with a lot of distinct values into something more manageable for learning schemes that can't handle nominal attributes (as opposed to creating binary indicator attributes). For nominal attributes, the user can specify the number values above which an attribute will be converted by this method. Normal distributions are assumed for numeric attributes.

Valid options are:

 -N
  Don't apply this transformation to numeric attributes

 -C
  Don't apply this transformation to nominal attributes

 -min-values <integer>
  Transform nominal attributes with at least this many values.
  -1 means always transform.

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

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

-spread-attribute-weight
  When generating binary attributes, spread weight of old
  attribute across new attributes. Do not give each new attribute the old weight.

Version:

$Revision: $

Author:

Mark Hall (mhall{[at]}pentaho{[dot]}com)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ClassConditionalProbabilities()

Method Summary

Modifier and Type	Method and Description
Capabilities	getCapabilities() Returns the Capabilities of this filter.
NaiveBayes	getEstimator() Get the naive Bayes estimator in use
boolean	getExcludeNominalAttributes() Get whether nominal attributes are to be excluded from the transformation
boolean	getExcludeNumericAttributes() Get whether numeric attributes are being excluded from the transformation
int	getNominalConversionThreshold() Get the minimum number of values a nominal attribute must have in order to be transformed.
Remove	getRemoveFilter() Get the remove filter in use
java.lang.String	getRevision() Returns the revision string.
boolean	getSpreadAttributeWeight() If true, when generating attributes, spread weight of old attribute across new attributes.
java.lang.String	globalInfo() Global help info for this method
boolean	input(Instance inst) Input an instance for filtering.
static void	main(java.lang.String[] args) Main method for testing this class
void	setEstimator(NaiveBayes nb) Set the naive Bayes estimator to use
void	setExcludeNominalAttributes(boolean e) Set whether nominal attributes are to be excluded from the transformation
void	setExcludeNumericAttributes(boolean e) Set whether numeric attributes are being excluded from the transformation
void	setNominalConversionThreshold(int n) Set the minimum number of values a nominal attribute must have in order to be transformed.
void	setRemoveFilter(Remove r)
void	setSpreadAttributeWeight(boolean p) If true, when generating attributes, spread weight of old attribute across new attributes.

Methods inherited from class weka.filters.SimpleBatchFilter

allowAccessToFullInputFormat, batchFinished, input

Methods inherited from class weka.filters.SimpleFilter

setInputFormat

Methods inherited from class weka.filters.Filter

batchFilterFile, debugTipText, doNotCheckCapabilitiesTipText, filterFile, getCapabilities, getCopyOfInputFormat, getDebug, getDoNotCheckCapabilities, getOptions, getOutputFormat, isFirstBatchDone, isNewBatch, isOutputFormatDefined, listOptions, makeCopies, makeCopy, mayRemoveInstanceAfterFirstBatchDone, numPendingOutput, output, outputPeek, postExecution, preExecution, run, runFilter, setDebug, setDoNotCheckCapabilities, setOptions, toString, useFilter, wekaStaticWrapper

Methods inherited from class java.lang.Object

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

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

ClassConditionalProbabilities

public ClassConditionalProbabilities()

Method Detail

main

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

Main method for testing this class

Parameters:

args - args

globalInfo

public java.lang.String globalInfo()

Global help info for this method

Specified by:

globalInfo in class SimpleFilter

Returns:

the global help info

getExcludeNumericAttributes

@OptionMetadata(displayName="Exclude numeric attributes",
                description="Don\'t apply this transformation to numeric attributes",
                commandLineParamName="N",
                commandLineParamIsFlag=true,
                commandLineParamSynopsis="-N",
                displayOrder=1)
public boolean getExcludeNumericAttributes()

Get whether numeric attributes are being excluded from the transformation

Returns:

true if numeric attributes are to be excluded

setExcludeNumericAttributes

public void setExcludeNumericAttributes(boolean e)

Set whether numeric attributes are being excluded from the transformation

Parameters:

e - true if numeric attributes are to be excluded

getExcludeNominalAttributes

@OptionMetadata(displayName="Exclude nominal attributes",
                description="Don\'t apply this transformation to nominal attributes",
                commandLineParamName="C",
                commandLineParamIsFlag=true,
                commandLineParamSynopsis="-C",
                displayOrder=2)
public boolean getExcludeNominalAttributes()

Get whether nominal attributes are to be excluded from the transformation

Returns:

true if nominal attributes are to be excluded

setExcludeNominalAttributes

public void setExcludeNominalAttributes(boolean e)

Set whether nominal attributes are to be excluded from the transformation

Parameters:

e - true if nominal attributes are to be excluded

setSpreadAttributeWeight

@OptionMetadata(displayName="Spread weight across new attributes",
                description="When generating attributes, spread weight of old\nattribute across new attributes. Do not give each new attribute the old weight.",
                commandLineParamName="spread-attribute-weight",
                commandLineParamIsFlag=true,
                commandLineParamSynopsis="-spread-attribute-weight",
                displayOrder=3)
public void setSpreadAttributeWeight(boolean p)

If true, when generating attributes, spread weight of old attribute across new attributes. Do not give each new attribute the old weight.

Parameters:

p - whether weight is spread

getSpreadAttributeWeight

public boolean getSpreadAttributeWeight()

If true, when generating attributes, spread weight of old attribute across new attributes. Do not give each new attribute the old weight.

Returns:

whether weight is spread

getNominalConversionThreshold

@OptionMetadata(displayName="Nominal conversion threshold",
                description="Transform nominal attributes with at least this many values.\n-1 means always transform.",
                commandLineParamName="min-values",
                commandLineParamSynopsis="-min-values <integer>",
                displayOrder=3)
public int getNominalConversionThreshold()

Get the minimum number of values a nominal attribute must have in order to be transformed. -1 indicates no minimum (i.e. transform all nominal attributes)

Returns:

the number of values of a nominal attribute after which the transformation applies

setNominalConversionThreshold

public void setNominalConversionThreshold(int n)

Set the minimum number of values a nominal attribute must have in order to be transformed. -1 indicates no minimum (i.e. transform all nominal attributes)

Parameters:

n - the number of values of a nominal attribute after which the transformation applies

input

public boolean input(Instance inst)
              throws java.lang.Exception

Description copied from class: SimpleBatchFilter

Input an instance for filtering. Filter requires all training instances be read before producing output (calling the method batchFinished() makes the data available). If this instance is part of a new batch, m_NewBatch is set to false.

Overrides:

input in class SimpleBatchFilter

Parameters:

inst - the input instance

Returns:

true if the filtered instance may now be collected with output().

Throws:

java.lang.IllegalStateException - if no input structure has been defined

java.lang.Exception - if something goes wrong

See Also:

SimpleBatchFilter.batchFinished()

getCapabilities

public Capabilities getCapabilities()

Returns the Capabilities of this filter.

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class Filter

Returns:

the capabilities of this object

See Also:

Capabilities

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class Filter

Returns:

the revision

getEstimator

@ProgrammaticProperty
public NaiveBayes getEstimator()

Get the naive Bayes estimator in use

Returns:

the naive Bayes estimator

setEstimator

public void setEstimator(NaiveBayes nb)

Set the naive Bayes estimator to use

Parameters:

nb - the naive Bayes estimator to use

getRemoveFilter

@ProgrammaticProperty
public Remove getRemoveFilter()

Get the remove filter in use

Returns:

setRemoveFilter

public void setRemoveFilter(Remove r)