weka.clusterers

Class CheckClusterer

java.lang.Object

weka.core.Check

weka.core.CheckScheme

weka.clusterers.CheckClusterer

All Implemented Interfaces:

OptionHandler, RevisionHandler

public class CheckClusterer
extends CheckScheme

Class for examining the capabilities and finding problems with clusterers. If you implement a clusterer using the WEKA.libraries, you should run the checks on it to ensure robustness and correct operation. Passing all the tests of this object does not mean bugs in the clusterer don't exist, but this will help find some common ones.

Typical usage:

java weka.clusterers.CheckClusterer -W clusterer_name -- clusterer_options

CheckClusterer reports on the following:

• Clusterer abilities
  • Possible command line options to the clusterer
  • Whether the clusterer can predict nominal, numeric, string, date or relational class attributes.
  • Whether the clusterer can handle numeric predictor attributes
  • Whether the clusterer can handle nominal predictor attributes
  • Whether the clusterer can handle string predictor attributes
  • Whether the clusterer can handle date predictor attributes
  • Whether the clusterer can handle relational predictor attributes
  • Whether the clusterer can handle multi-instance data
  • Whether the clusterer can handle missing predictor values
  • Whether the clusterer can handle instance weights
• Correct functioning
  • Correct initialisation during buildClusterer (i.e. no result changes when buildClusterer called repeatedly)
  • Whether the clusterer alters the data pased to it (number of instances, instance order, instance weights, etc)
• Degenerate cases
  • building clusterer with zero training instances
  • all but one predictor attribute values missing
  • all predictor attribute values missing
  • all but one class values missing
  • all class values missing

Running CheckClusterer with the debug option set will output the training dataset for any failed tests.

The weka.clusterers.AbstractClustererTest uses this class to test all the clusterers. Any changes here, have to be checked in that abstract test class, too.

Valid options are:

 -D
  Turn on debugging output.
 

 -S
  Silent mode - prints nothing to stdout.
 

 -N <num>
  The number of instances in the datasets (default 20).
 

 -nominal <num>
  The number of nominal attributes (default 2).
 

 -nominal-values <num>
  The number of values for nominal attributes (default 1).
 

 -numeric <num>
  The number of numeric attributes (default 1).
 

 -string <num>
  The number of string attributes (default 1).
 

 -date <num>
  The number of date attributes (default 1).
 

 -relational <num>
  The number of relational attributes (default 1).
 

 -num-instances-relational <num>
  The number of instances in relational/bag attributes (default 10).
 

 -words <comma-separated-list>
  The words to use in string attributes.
 

 -word-separators <chars>
  The word separators to use in string attributes.
 

 -W
  Full name of the clusterer analyzed.
  eg: weka.clusterers.SimpleKMeans
  (default weka.clusterers.SimpleKMeans)
 

 Options specific to clusterer weka.clusterers.SimpleKMeans:
 

 -N <num>
  number of clusters.
  (default 2).
 

 -V
  Display std. deviations for centroids.
 

 -M
  Replace missing values with mean/mode.
 

 -S <num>
  Random number seed.
  (default 10)
 

Options after -- are passed to the designated clusterer.

Version:

$Revision: 11451 $

Author:

Len Trigg (trigg@cs.waikato.ac.nz), FracPete (fracpete at waikato dot ac dot nz)

See Also:

TestInstances

Nested Class Summary

Nested classes/interfaces inherited from class weka.core.CheckScheme

CheckScheme.PostProcessor

Constructor Summary

Constructors

Constructor and Description
CheckClusterer() default constructor

Method Summary

Modifier and Type	Method and Description
void	doTests() Begin the tests, reporting results to System.out
Clusterer	getClusterer() Get the clusterer used as the clusterer
java.lang.String[]	getOptions() Gets the current settings of the CheckClusterer.
java.lang.String	getRevision() Returns the revision string.
java.util.Enumeration<Option>	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] args) Test method for this class
void	setClusterer(Clusterer newClusterer) Set the clusterer for testing.
void	setOptions(java.lang.String[] options) Parses a given list of options.

Methods inherited from class weka.core.CheckScheme

attributeTypeToString, getNumDate, getNumInstances, getNumInstancesRelational, getNumNominal, getNumNumeric, getNumRelational, getNumString, getPostProcessor, getWords, getWordSeparators, hasClasspathProblems, setNumDate, setNumInstances, setNumInstancesRelational, setNumNominal, setNumNumeric, setNumRelational, setNumString, setPostProcessor, setWords, setWordSeparators

Methods inherited from class weka.core.Check

getDebug, getSilent, setDebug, setSilent

Methods inherited from class java.lang.Object

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

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

CheckClusterer

public CheckClusterer()

default constructor

Method Detail

listOptions

public java.util.Enumeration<Option> listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class CheckScheme

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:

 -D
  Turn on debugging output.
 

 -S
  Silent mode - prints nothing to stdout.
 

 -N <num>
  The number of instances in the datasets (default 20).
 

 -nominal <num>
  The number of nominal attributes (default 2).
 

 -nominal-values <num>
  The number of values for nominal attributes (default 1).
 

 -numeric <num>
  The number of numeric attributes (default 1).
 

 -string <num>
  The number of string attributes (default 1).
 

 -date <num>
  The number of date attributes (default 1).
 

 -relational <num>
  The number of relational attributes (default 1).
 

 -num-instances-relational <num>
  The number of instances in relational/bag attributes (default 10).
 

 -words <comma-separated-list>
  The words to use in string attributes.
 

 -word-separators <chars>
  The word separators to use in string attributes.
 

 -W
  Full name of the clusterer analyzed.
  eg: weka.clusterers.SimpleKMeans
  (default weka.clusterers.SimpleKMeans)
 

 Options specific to clusterer weka.clusterers.SimpleKMeans:
 

 -N <num>
  number of clusters.
  (default 2).
 

 -V
  Display std. deviations for centroids.
 

 -M
  Replace missing values with mean/mode.
 

 -S <num>
  Random number seed.
  (default 10)
 

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class CheckScheme

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 CheckClusterer.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class CheckScheme

Returns:

an array of strings suitable for passing to setOptions

doTests

public void doTests()

Begin the tests, reporting results to System.out

Specified by:

doTests in class CheckScheme

setClusterer

public void setClusterer(Clusterer newClusterer)

Set the clusterer for testing.

Parameters:

newClusterer - the Clusterer to use.

getClusterer

public Clusterer getClusterer()

Get the clusterer used as the clusterer

Returns:

the clusterer used as the clusterer

getRevision

public java.lang.String getRevision()

Returns the revision string.

Returns:

the revision

main

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

Test method for this class

Parameters:

args - the commandline options