weka.classifiers.meta

Class AttributeSelectedClassifier

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.meta.AttributeSelectedClassifier

All Implemented Interfaces:

Serializable, Cloneable, Classifier, AdditionalMeasureProducer, CapabilitiesHandler, Drawable, OptionHandler, RevisionHandler, WeightedInstancesHandler

public class AttributeSelectedClassifier
extends SingleClassifierEnhancer
implements OptionHandler, Drawable, AdditionalMeasureProducer, WeightedInstancesHandler

Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.

Valid options are:

 -E <attribute evaluator specification>
  Full class name of attribute evaluator, followed
  by its options.
  eg: "weka.attributeSelection.CfsSubsetEval -L"
  (default weka.attributeSelection.CfsSubsetEval)

 -S <search method specification>
  Full class name of search method, followed
  by its options.
  eg: "weka.attributeSelection.BestFirst -D 1"
  (default weka.attributeSelection.BestFirst)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.J48)

 
 Options specific to classifier weka.classifiers.trees.J48:
 

 -U
  Use unpruned tree.

 -C <pruning confidence>
  Set confidence threshold for pruning.
  (default 0.25)

 -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 2)

 -R
  Use reduced error pruning.

 -N <number of folds>
  Set number of folds for reduced error
  pruning. One fold is used as pruning set.
  (default 3)

 -B
  Use binary splits only.

 -S
  Don't perform subtree raising.

 -L
  Do not clean up after the tree has been built.

 -A
  Laplace smoothing for predicted probabilities.

 -Q <seed>
  Seed for random data shuffling (default 1).

Version:

$Revision: 9186 $

Author:

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

See Also:

Serialized Form

Field Summary

Fields inherited from interface weka.core.Drawable

BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

Constructors

Constructor and Description
AttributeSelectedClassifier() Default constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	buildClassifier(Instances data) Build the classifier on the dimensionally reduced data.
double[]	distributionForInstance(Instance instance) Classifies a given instance after attribute selection
Enumeration	enumerateMeasures() Returns an enumeration of the additional measure names
String	evaluatorTipText() Returns the tip text for this property
Capabilities	getCapabilities() Returns default capabilities of the classifier.
ASEvaluation	getEvaluator() Gets the attribute evaluator used
double	getMeasure(String additionalMeasureName) Returns the value of the named measure
String[]	getOptions() Gets the current settings of the Classifier.
String	getRevision() Returns the revision string.
ASSearch	getSearch() Gets the search method used
String	globalInfo() Returns a string describing this search method
String	graph() Returns graph describing the classifier (if possible).
int	graphType() Returns the type of graph this classifier represents.
Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(String[] argv) Main method for testing this class.
double	measureNumAttributesSelected() Additional measure --- number of attributes selected
double	measureSelectionTime() Additional measure --- time taken (milliseconds) to select the attributes
double	measureTime() Additional measure --- time taken (milliseconds) to select attributes and build the classifier
String	searchTipText() Returns the tip text for this property
void	setEvaluator(ASEvaluation evaluator) Sets the attribute evaluator
void	setOptions(String[] options) Parses a given list of options.
void	setSearch(ASSearch search) Sets the search method
String	toString() Output a representation of this classifier

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

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

Constructor Detail

AttributeSelectedClassifier

public AttributeSelectedClassifier()

Default constructor.

Method Detail

globalInfo

public String globalInfo()

Returns a string describing this search method

Returns:

a description of the search method 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

Overrides:

listOptions in class SingleClassifierEnhancer

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:

 -E <attribute evaluator specification>
  Full class name of attribute evaluator, followed
  by its options.
  eg: "weka.attributeSelection.CfsSubsetEval -L"
  (default weka.attributeSelection.CfsSubsetEval)

 -S <search method specification>
  Full class name of search method, followed
  by its options.
  eg: "weka.attributeSelection.BestFirst -D 1"
  (default weka.attributeSelection.BestFirst)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.J48)

 
 Options specific to classifier weka.classifiers.trees.J48:
 

 -U
  Use unpruned tree.

 -C <pruning confidence>
  Set confidence threshold for pruning.
  (default 0.25)

 -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 2)

 -R
  Use reduced error pruning.

 -N <number of folds>
  Set number of folds for reduced error
  pruning. One fold is used as pruning set.
  (default 3)

 -B
  Use binary splits only.

 -S
  Don't perform subtree raising.

 -L
  Do not clean up after the tree has been built.

 -A
  Laplace smoothing for predicted probabilities.

 -Q <seed>
  Seed for random data shuffling (default 1).

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class SingleClassifierEnhancer

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

Overrides:

getOptions in class SingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

evaluatorTipText

public String evaluatorTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setEvaluator

public void setEvaluator(ASEvaluation evaluator)

Sets the attribute evaluator

Parameters:

evaluator - the evaluator with all options set.

getEvaluator

public ASEvaluation getEvaluator()

Gets the attribute evaluator used

Returns:

the attribute evaluator

searchTipText

public String searchTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setSearch

public void setSearch(ASSearch search)

Sets the search method

Parameters:

search - the search method with all options set.

getSearch

public ASSearch getSearch()

Gets the search method used

Returns:

the search method

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface Classifier

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class SingleClassifierEnhancer

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

public void buildClassifier(Instances data)
                     throws Exception

Build the classifier on the dimensionally reduced data.

Specified by:

buildClassifier in interface Classifier

Parameters:

data - the training data

Throws:

Exception - if the classifier could not be built successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws Exception

Classifies a given instance after attribute selection

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

the class distribution

Throws:

Exception - if instance could not be classified successfully

graphType

public int graphType()

Returns the type of graph this classifier represents.

Specified by:

graphType in interface Drawable

Returns:

the type of graph

graph

public String graph()
             throws Exception

Returns graph describing the classifier (if possible).

Specified by:

graph in interface Drawable

Returns:

the graph of the classifier in dotty format

Throws:

Exception - if the classifier cannot be graphed

toString

public String toString()

Output a representation of this classifier

Overrides:

toString in class Object

Returns:

a representation of this classifier

measureNumAttributesSelected

public double measureNumAttributesSelected()

Additional measure --- number of attributes selected

Returns:

the number of attributes selected

measureSelectionTime

public double measureSelectionTime()

Additional measure --- time taken (milliseconds) to select the attributes

Returns:

the time taken to select attributes

measureTime

public double measureTime()

Additional measure --- time taken (milliseconds) to select attributes and build the classifier

Returns:

the total time (select attributes + build classifier)

enumerateMeasures

public Enumeration enumerateMeasures()

Returns an enumeration of the additional measure names

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

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AbstractClassifier

Returns:

the revision

main

public static void main(String[] argv)

Main method for testing this class.

Parameters:

argv - should contain the following arguments: -t training file [-T test file] [-c class index]