weka.classifiers.meta

Class FilteredClassifier

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.meta.FilteredClassifier

All Implemented Interfaces:

Serializable, Cloneable, Classifier, CapabilitiesHandler, Drawable, OptionHandler, PartitionGenerator, RevisionHandler

public class FilteredClassifier
extends SingleClassifierEnhancer
implements Drawable, PartitionGenerator

Class for running an arbitrary classifier on data that has been passed through an arbitrary filter. Like the classifier, the structure of the filter is based exclusively on the training data and test instances will be processed by the filter without changing their structure.

Valid options are:

 -F <filter specification>
  Full class name of filter to use, followed
  by filter options.
  eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"

 -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: 9117 $

Author:

Len Trigg (trigg@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
FilteredClassifier() Default constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	buildClassifier(Instances data) Build the classifier on the filtered data.
double[]	distributionForInstance(Instance instance) Classifies a given instance after filtering.
String	filterTipText() Returns the tip text for this property
void	generatePartition(Instances data) Builds the classifier to generate a partition.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
Filter	getFilter() Gets the filter used.
double[]	getMembershipValues(Instance inst) Computes an array that has a value for each element in the partition.
String[]	getOptions() Gets the current settings of the Classifier.
String	getRevision() Returns the revision string.
String	globalInfo() Returns a string describing this classifier
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.
int	numElements() Returns the number of elements in the partition.
void	setFilter(Filter filter) Sets the filter
void	setOptions(String[] options) Parses a given list of options.
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

FilteredClassifier

public FilteredClassifier()

Default constructor.

Method Detail

globalInfo

public String globalInfo()

Returns a string describing this classifier

Returns:

a description of the classifier suitable for displaying in the explorer/experimenter gui

graphType

public int graphType()

Returns the type of graph this classifier represents.

Specified by:

graphType in interface Drawable

Returns:

the graph type of this classifier

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

generatePartition

public void generatePartition(Instances data)
                       throws Exception

Builds the classifier to generate a partition. (If the base classifier supports this.)

Specified by:

generatePartition in interface PartitionGenerator

Throws:

Exception

getMembershipValues

public double[] getMembershipValues(Instance inst)
                             throws Exception

Computes an array that has a value for each element in the partition. (If the base classifier supports this.)

Specified by:

getMembershipValues in interface PartitionGenerator

Throws:

Exception

numElements

public int numElements()
                throws Exception

Returns the number of elements in the partition. (If the base classifier supports this.)

Specified by:

numElements in interface PartitionGenerator

Throws:

Exception

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:

 -F <filter specification>
  Full class name of filter to use, followed
  by filter options.
  eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"

 -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

filterTipText

public String filterTipText()

Returns the tip text for this property

Returns:

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

setFilter

public void setFilter(Filter filter)

Sets the filter

Parameters:

filter - the filter with all options set.

getFilter

public Filter getFilter()

Gets the filter used.

Returns:

the filter

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 filtered 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 filtering.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

the class distribution for the given instance

Throws:

Exception - if instance could not be classified successfully

toString

public String toString()

Output a representation of this classifier

Overrides:

toString in class Object

Returns:

a representation of this classifier

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]