weka.clusterers

Class ClusterEvaluation

java.lang.Object

weka.clusterers.ClusterEvaluation

All Implemented Interfaces:

Serializable, RevisionHandler

public class ClusterEvaluation
extends Object
implements Serializable, RevisionHandler

Class for evaluating clustering models.

Valid options are:

-t name of the training file
Specify the training file.

-T name of the test file
Specify the test file to apply clusterer to.

-d name of file to save clustering model to
Specify output file.

-l name of file to load clustering model from
Specifiy input file.

-p attribute range
Output predictions. Predictions are for the training file if only the training file is specified, otherwise they are for the test file. The range specifies attribute values to be output with the predictions. Use '-p 0' for none.

-x num folds
Set the number of folds for a cross validation of the training data. Cross validation can only be done for distribution clusterers and will be performed if the test file is missing.

-s num
Sets the seed for randomizing the data for cross-validation.

-c class
Set the class attribute. If set, then class based evaluation of clustering is performed.

-g name of graph file
Outputs the graph representation of the clusterer to the file. Only for clusterer that implemented the weka.core.Drawable interface.

Version:

$Revision: 8034 $

Author:

Mark Hall (mhall@cs.waikato.ac.nz)

See Also:

Drawable, Serialized Form

Constructor Summary

Constructors

Constructor and Description
ClusterEvaluation() Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
String	clusterResultsToString() return the results of clustering.
static double	crossValidateModel(DensityBasedClusterer clusterer, Instances data, int numFolds, Random random) Perform a cross-validation for DensityBasedClusterer on a set of instances.
static String	crossValidateModel(String clustererString, Instances data, int numFolds, String[] options, Random random) Performs a cross-validation for a DensityBasedClusterer clusterer on a set of instances.
boolean	equals(Object obj) Tests whether the current evaluation object is equal to another evaluation object
static String	evaluateClusterer(Clusterer clusterer, String[] options) Evaluates a clusterer with the options given in an array of strings.
void	evaluateClusterer(Instances test) Evaluate the clusterer on a set of instances.
void	evaluateClusterer(Instances test, String testFileName) Evaluate the clusterer on a set of instances.
void	evaluateClusterer(Instances test, String testFileName, boolean outputModel) Evaluate the clusterer on a set of instances.
int[]	getClassesToClusters() Return the array (ordered by cluster number) of minimum error class to cluster mappings
double[]	getClusterAssignments() Return an array of cluster assignments corresponding to the most recent set of instances clustered.
double	getLogLikelihood() Return the log likelihood corresponding to the most recent set of instances clustered.
int	getNumClusters() Return the number of clusters found for the most recent call to evaluateClusterer
String	getRevision() Returns the revision string.
static void	main(String[] args) Main method for testing this class.
static void	mapClasses(int numClusters, int lev, int[][] counts, int[] clusterTotals, double[] current, double[] best, int error) Finds the minimum error mapping of classes to clusters.
void	setClusterer(Clusterer clusterer) set the clusterer

Methods inherited from class java.lang.Object

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

Constructor Detail

ClusterEvaluation

public ClusterEvaluation()

Constructor. Sets defaults for each member variable. Default Clusterer is EM.

Method Detail

setClusterer

public void setClusterer(Clusterer clusterer)

set the clusterer

Parameters:

clusterer - the clusterer to use

clusterResultsToString

public String clusterResultsToString()

return the results of clustering.

Returns:

a string detailing the results of clustering a data set

getNumClusters

public int getNumClusters()

Return the number of clusters found for the most recent call to evaluateClusterer

Returns:

the number of clusters found

getClusterAssignments

public double[] getClusterAssignments()

Return an array of cluster assignments corresponding to the most recent set of instances clustered.

Returns:

an array of cluster assignments

getClassesToClusters

public int[] getClassesToClusters()

Return the array (ordered by cluster number) of minimum error class to cluster mappings

Returns:

an array of class to cluster mappings

getLogLikelihood

public double getLogLikelihood()

Return the log likelihood corresponding to the most recent set of instances clustered.

Returns:

a double value

evaluateClusterer

public void evaluateClusterer(Instances test)
                       throws Exception

Evaluate the clusterer on a set of instances. Calculates clustering statistics and stores cluster assigments for the instances in m_clusterAssignments

Parameters:

test - the set of instances to cluster

Throws:

Exception - if something goes wrong

evaluateClusterer

public void evaluateClusterer(Instances test,
                     String testFileName)
                       throws Exception

Evaluate the clusterer on a set of instances. Calculates clustering statistics and stores cluster assigments for the instances in m_clusterAssignments

Parameters:

test - the set of instances to cluster

testFileName - the name of the test file for incremental testing, if "" or null then not used

Throws:

Exception - if something goes wrong

evaluateClusterer

public void evaluateClusterer(Instances test,
                     String testFileName,
                     boolean outputModel)
                       throws Exception

Evaluate the clusterer on a set of instances. Calculates clustering statistics and stores cluster assigments for the instances in m_clusterAssignments

Parameters:

test - the set of instances to cluster

testFileName - the name of the test file for incremental testing, if "" or null then not used

outputModel - true if the clustering model is to be output as well as the stats

Throws:

Exception - if something goes wrong

mapClasses

public static void mapClasses(int numClusters,
              int lev,
              int[][] counts,
              int[] clusterTotals,
              double[] current,
              double[] best,
              int error)

Finds the minimum error mapping of classes to clusters. Recursively considers all possible class to cluster assignments.

Parameters:

numClusters - the number of clusters

lev - the cluster being processed

counts - the counts of classes in clusters

clusterTotals - the total number of examples in each cluster

current - the current path through the class to cluster assignment tree

best - the best assignment path seen

error - accumulates the error for a particular path

evaluateClusterer

public static String evaluateClusterer(Clusterer clusterer,
                       String[] options)
                                throws Exception

Evaluates a clusterer with the options given in an array of strings. It takes the string indicated by "-t" as training file, the string indicated by "-T" as test file. If the test file is missing, a stratified ten-fold cross-validation is performed (distribution clusterers only). Using "-x" you can change the number of folds to be used, and using "-s" the random seed. If the "-p" option is present it outputs the classification for each test instance. If you provide the name of an object file using "-l", a clusterer will be loaded from the given file. If you provide the name of an object file using "-d", the clusterer built from the training data will be saved to the given file.

Parameters:

clusterer - machine learning clusterer

options - the array of string containing the options

Returns:

a string describing the results

Throws:

Exception - if model could not be evaluated successfully

crossValidateModel

public static double crossValidateModel(DensityBasedClusterer clusterer,
                        Instances data,
                        int numFolds,
                        Random random)
                                 throws Exception

Perform a cross-validation for DensityBasedClusterer on a set of instances.

Parameters:

clusterer - the clusterer to use

data - the training data

numFolds - number of folds of cross validation to perform

random - random number seed for cross-validation

Returns:

the cross-validated log-likelihood

Throws:

Exception - if an error occurs

crossValidateModel

public static String crossValidateModel(String clustererString,
                        Instances data,
                        int numFolds,
                        String[] options,
                        Random random)
                                 throws Exception

Performs a cross-validation for a DensityBasedClusterer clusterer on a set of instances.

Parameters:

clustererString - a string naming the class of the clusterer

data - the data on which the cross-validation is to be performed

numFolds - the number of folds for the cross-validation

options - the options to the clusterer

random - a random number generator

Returns:

a string containing the cross validated log likelihood

Throws:

Exception - if a clusterer could not be generated

equals

public boolean equals(Object obj)

Tests whether the current evaluation object is equal to another evaluation object

Overrides:

equals in class Object

Parameters:

obj - the object to compare against

Returns:

true if the two objects are equal

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Returns:

the revision

main

public static void main(String[] args)

Main method for testing this class.

Parameters:

args - the options