Package 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:
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:

  • Constructor Details

    • ClassConditionalProbabilities

      public ClassConditionalProbabilities()

  • Method Details

    • main

      public static void main(String[] args)
      Main method for testing this class
      Parameters:
      args - args

    • globalInfo

      public 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 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:
      IllegalStateException - if no input structure has been defined
      Exception - if something goes wrong
      See Also:

    • 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:

    • getRevision

      public 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)