Package weka.experiment

Class DensityBasedClustererSplitEvaluator

java.lang.Object
weka.experiment.DensityBasedClustererSplitEvaluator
All Implemented Interfaces:
Serializable, AdditionalMeasureProducer, OptionHandler, RevisionHandler, SplitEvaluator
public class DensityBasedClustererSplitEvaluator extends Object implements SplitEvaluator, OptionHandler, AdditionalMeasureProducer, RevisionHandler
A SplitEvaluator that produces results for a density based clusterer.

Valid options are: 

 -no-size
  Skips the determination of sizes (train/test/clusterer)
  (default: sizes are determined)
 
 -W <class name>
  The full class name of the density based clusterer.
  eg: weka.clusterers.EM
 
 Options specific to clusterer weka.clusterers.EM:
 
 -N <num>
  number of clusters. If omitted or -1 specified, then
  cross validation is used to select the number of clusters.
 
 -I <num>
  max iterations.
 (default 100)
 
 -V
  verbose.
 
 -M <num>
  minimum allowable standard deviation for normal density
  computation
  (default 1e-6)
 
 -O
  Display model in old format (good when there are many clusters)
 
 -S <num>
  Random number seed.
  (default 100)
All options after -- will be passed to the clusterer.
Version:
$Revision: 11323 $
Author:
Mark Hall (mhall{[at]}pentaho{[dot]}org
See Also:

  • Constructor Details

    • DensityBasedClustererSplitEvaluator

      public DensityBasedClustererSplitEvaluator()

  • Method Details

    • globalInfo

      public String globalInfo()
      Returns a string describing this split evaluator
      Returns:
      a description of the split evaluator 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
      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 classname
      Specify the full class name of the clusterer to evaluate.

      All option after -- will be passed to the classifier.

      Specified by:
      setOptions in interface OptionHandler
      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
      Returns:
      an array of strings suitable for passing to setOptions

    • setAdditionalMeasures

      public void setAdditionalMeasures(String[] additionalMeasures)
      Set a list of method names for additional measures to look for in Classifiers. This could contain many measures (of which only a subset may be produceable by the current Classifier) if an experiment is the type that iterates over a set of properties.
      Specified by:
      setAdditionalMeasures in interface SplitEvaluator
      Parameters:
      additionalMeasures - a list of method names

    • enumerateMeasures

      public Enumeration<String> enumerateMeasures()
      Returns an enumeration of any additional measure names that might be in the classifier
      Specified by:
      enumerateMeasures in interface AdditionalMeasureProducer
      Returns:
      an enumeration of the measure names

    • getMeasure

      public double getMeasure(String additionalMeasureName)
      Returns the value of the named measure
      Specified by:
      getMeasure in interface AdditionalMeasureProducer
      Parameters:
      additionalMeasureName - the name of the measure to query for its value
      Returns:
      the value of the named measure
      Throws:
      IllegalArgumentException - if the named measure is not supported

    • getKeyTypes

      public Object[] getKeyTypes()
      Gets the data types of each of the key columns produced for a single run. The number of key fields must be constant for a given SplitEvaluator.
      Specified by:
      getKeyTypes in interface SplitEvaluator
      Returns:
      an array containing objects of the type of each key column. The objects should be Strings, or Doubles.

    • getKeyNames

      public String[] getKeyNames()
      Gets the names of each of the key columns produced for a single run. The number of key fields must be constant for a given SplitEvaluator.
      Specified by:
      getKeyNames in interface SplitEvaluator
      Returns:
      an array containing the name of each key column

    • getKey

      public Object[] getKey()
      Gets the key describing the current SplitEvaluator. For example This may contain the name of the classifier used for classifier predictive evaluation. The number of key fields must be constant for a given SplitEvaluator.
      Specified by:
      getKey in interface SplitEvaluator
      Returns:
      an array of objects containing the key.

    • getResultTypes

      public Object[] getResultTypes()
      Gets the data types of each of the result columns produced for a single run. The number of result fields must be constant for a given SplitEvaluator.
      Specified by:
      getResultTypes in interface SplitEvaluator
      Returns:
      an array containing objects of the type of each result column. The objects should be Strings, or Doubles.

    • getResultNames

      public String[] getResultNames()
      Gets the names of each of the result columns produced for a single run. The number of result fields must be constant for a given SplitEvaluator.
      Specified by:
      getResultNames in interface SplitEvaluator
      Returns:
      an array containing the name of each result column

    • getResult

      public Object[] getResult(Instances train, Instances test) throws Exception
      Gets the results for the supplied train and test datasets.
      Specified by:
      getResult in interface SplitEvaluator
      Parameters:
      train - the training Instances.
      test - the testing Instances.
      Returns:
      the results stored in an array. The objects stored in the array may be Strings, Doubles, or null (for the missing value).
      Throws:
      Exception - if a problem occurs while getting the results

    • removeClassColumnTipText

      public String removeClassColumnTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • setRemoveClassColumn

      public void setRemoveClassColumn(boolean r)
      Set whether the class column should be removed from the data.
      Parameters:
      r - true if the class column is to be removed.

    • getRemoveClassColumn

      public boolean getRemoveClassColumn()
      Get whether the class column is to be removed.
      Returns:
      true if the class column is to be removed.

    • clustererTipText

      public String clustererTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getClusterer

      public DensityBasedClusterer getClusterer()
      Get the value of clusterer
      Returns:
      Value of clusterer.

    • setClusterer

      public void setClusterer(DensityBasedClusterer newClusterer)
      Sets the clusterer.
      Parameters:
      newClusterer - the new clusterer to use.

    • getNoSizeDetermination

      public boolean getNoSizeDetermination()
      Returns whether the size determination (train/test/clusterer) is skipped.
      Returns:
      true if size determination skipped

    • setNoSizeDetermination

      public void setNoSizeDetermination(boolean value)
      Sets whether the size determination (train/test/clusterer) is skipped.
      Parameters:
      value - true if to determine sizes

    • noSizeDeterminationTipText

      public String noSizeDeterminationTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • setClustererName

      public void setClustererName(String newClustererName) throws Exception
      Set the Clusterer to use, given it's class name. A new clusterer will be instantiated.
      Parameters:
      newClustererName - the clusterer class name.
      Throws:
      Exception - if the class name is invalid.

    • getRawResultOutput

      public String getRawResultOutput()
      Gets the raw output from the classifier
      Specified by:
      getRawResultOutput in interface SplitEvaluator
      Returns:
      the raw output from the classifier

    • toString

      public String toString()
      Returns a text description of the split evaluator.
      Overrides:
      toString in class Object
      Returns:
      a text description of the split evaluator.

    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Returns:
      the revision