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: 8054 $

Author:

Mark Hall (mhall{[at]}pentaho{[dot]}org

See Also:

Serialized Form

Constructor Summary

DensityBasedClustererSplitEvaluator()

Method Summary

String	clustererTipText() Returns the tip text for this property
Enumeration	enumerateMeasures() Returns an enumeration of any additional measure names that might be in the classifier
DensityBasedClusterer	getClusterer() Get the value of clusterer
Object[]	getKey() Gets the key describing the current SplitEvaluator.
String[]	getKeyNames() Gets the names of each of the key columns produced for a single run.
Object[]	getKeyTypes() Gets the data types of each of the key columns produced for a single run.
double	getMeasure(String additionalMeasureName) Returns the value of the named measure
boolean	getNoSizeDetermination() Returns whether the size determination (train/test/clusterer) is skipped.
String[]	getOptions() Gets the current settings of the Classifier.
String	getRawResultOutput() Gets the raw output from the classifier
boolean	getRemoveClassColumn() Get whether the class column is to be removed.
Object[]	getResult(Instances train, Instances test) Gets the results for the supplied train and test datasets.
String[]	getResultNames() Gets the names of each of the result columns produced for a single run.
Object[]	getResultTypes() Gets the data types of each of the result columns produced for a single run.
String	getRevision() Returns the revision string.
String	globalInfo() Returns a string describing this split evaluator
Enumeration	listOptions() Returns an enumeration describing the available options.
String	noSizeDeterminationTipText() Returns the tip text for this property
String	removeClassColumnTipText() Returns the tip text for this property
void	setAdditionalMeasures(String[] additionalMeasures) Set a list of method names for additional measures to look for in Classifiers.
void	setClusterer(DensityBasedClusterer newClusterer) Sets the clusterer.
void	setClustererName(String newClustererName) Set the Clusterer to use, given it's class name.
void	setNoSizeDetermination(boolean value) Sets whether the size determination (train/test/clusterer) is skipped.
void	setOptions(String[] options) Parses a given list of options.
void	setRemoveClassColumn(boolean r) Set whether the class column should be removed from the data.
String	toString() Returns a text description of the split evaluator.

Methods inherited from class java.lang.Object

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

Constructor Detail

DensityBasedClustererSplitEvaluator

public DensityBasedClustererSplitEvaluator()

Method Detail

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 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 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