weka.attributeSelection
Class OneRAttributeEval

java.lang.Object
  weka.attributeSelection.ASEvaluation
      weka.attributeSelection.AttributeEvaluator
          weka.attributeSelection.OneRAttributeEval

All Implemented Interfaces:

OptionHandler, java.io.Serializable

public class OneRAttributeEval

extends AttributeEvaluator

implements OptionHandler

Class for Evaluating attributes individually by using the OneR classifier.

-S
Set the seed for cross validation (default = 1).

-F
Set the number of folds for cross validation (default = 10).

-B
Set the minimum number of objects per bucket (passed on to OneR, default = 6).

-D
Use the training data to evaluate attributes rather than cross validation.

Version:

$Revision: 1.14 $

Author:

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

See Also:

Serialized Form

Field Summary

private int	m_classIndex The class index
private boolean	m_evalUsingTrainingData Use training data to evaluate merit rather than x-val
private int	m_folds Number of folds for cross validation
private int	m_minBucketSize Passed on to OneR
private int	m_numAttribs The number of attributes
private int	m_numInstances The number of instances
private int	m_randomSeed Random number seed
private Instances	m_trainInstances The training instances

Constructor Summary

OneRAttributeEval()
Constructor

Method Summary

void	buildEvaluator(Instances data) Initializes a OneRAttribute attribute evaluator.
double	evaluateAttribute(int attribute) evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.
java.lang.String	evalUsingTrainingDataTipText() Returns a string for this option suitable for display in the gui as a tip text
java.lang.String	foldsTipText() Returns a string for this option suitable for display in the gui as a tip text
boolean	getEvalUsingTrainingData() Returns true if the training data is to be used for evaluation
int	getFolds() Get the number of folds used for cross validation
int	getMinimumBucketSize() Get the minimum bucket size used by oneR
java.lang.String[]	getOptions() Gets the current option settings for the OptionHandler.
int	getSeed() Get the random number seed
java.lang.String	globalInfo() Returns a string describing this attribute evaluator
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] args) Main method for testing this class.
java.lang.String	minimumBucketSizeTipText() Returns a string for this option suitable for display in the gui as a tip text
protected void	resetOptions() rests to defaults.
java.lang.String	seedTipText() Returns a string for this option suitable for display in the gui as a tip text
void	setEvalUsingTrainingData(boolean e) Use the training data to evaluate attributes rather than cross validation
void	setFolds(int folds) Set the number of folds to use for cross validation
void	setMinimumBucketSize(int minB) Set the minumum bucket size used by OneR
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setSeed(int seed) Set the random number seed for cross validation
java.lang.String	toString() Return a description of the evaluator

Methods inherited from class weka.attributeSelection.ASEvaluation

forName, makeCopies, postProcess

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_trainInstances

private Instances m_trainInstances

The training instances

m_classIndex

private int m_classIndex

The class index

m_numAttribs

private int m_numAttribs

The number of attributes

m_numInstances

private int m_numInstances

The number of instances

m_randomSeed

private int m_randomSeed

Random number seed

m_folds

private int m_folds

Number of folds for cross validation

m_evalUsingTrainingData

private boolean m_evalUsingTrainingData

Use training data to evaluate merit rather than x-val

m_minBucketSize

private int m_minBucketSize

Passed on to OneR

Constructor Detail

OneRAttributeEval

public OneRAttributeEval()

Constructor

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this attribute evaluator

Returns:

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

seedTipText

public java.lang.String seedTipText()

Returns a string for this option suitable for display in the gui as a tip text

Returns:

a string describing this option

setSeed

public void setSeed(int seed)

Set the random number seed for cross validation

Parameters:

seed - the seed to use

getSeed

public int getSeed()

Get the random number seed

Returns:

an int value

foldsTipText

public java.lang.String foldsTipText()

Returns a string for this option suitable for display in the gui as a tip text

Returns:

a string describing this option

setFolds

public void setFolds(int folds)

Set the number of folds to use for cross validation

Parameters:

folds - the number of folds

getFolds

public int getFolds()

Get the number of folds used for cross validation

Returns:

the number of folds

evalUsingTrainingDataTipText

public java.lang.String evalUsingTrainingDataTipText()

Returns a string for this option suitable for display in the gui as a tip text

Returns:

a string describing this option

setEvalUsingTrainingData

public void setEvalUsingTrainingData(boolean e)

Use the training data to evaluate attributes rather than cross validation

Parameters:

e - true if training data is to be used for evaluation

minimumBucketSizeTipText

public java.lang.String minimumBucketSizeTipText()

Returns a string for this option suitable for display in the gui as a tip text

Returns:

a string describing this option

setMinimumBucketSize

public void setMinimumBucketSize(int minB)

Set the minumum bucket size used by OneR

Parameters:

minB - the minimum bucket size to use

getMinimumBucketSize

public int getMinimumBucketSize()

Get the minimum bucket size used by oneR

Returns:

the minimum bucket size used

getEvalUsingTrainingData

public boolean getEvalUsingTrainingData()

Returns true if the training data is to be used for evaluation

Returns:

true if training data is to be used for evaluation

listOptions

public java.util.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(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options. Valid options are:

-S
Set the seed for cross validation (default = 1).

-F
Set the number of folds for cross validation (default = 10).

-B
Set the minimum number of objects per bucket (passed on to OneR, default = 6).

-D
Use the training data to evaluate attributes rather than cross validation.

Specified by:

setOptions in interface OptionHandler

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()

Description copied from interface: OptionHandler

Gets the current option settings for the OptionHandler.

Specified by:

getOptions in interface OptionHandler

Returns:

the list of current option settings as an array of strings

buildEvaluator

public void buildEvaluator(Instances data)
                    throws java.lang.Exception

Initializes a OneRAttribute attribute evaluator. Discretizes all attributes that are numeric.

Specified by:

buildEvaluator in class ASEvaluation

Parameters:

data - set of instances serving as training data

Throws:

java.lang.Exception - if the evaluator has not been generated successfully

resetOptions

protected void resetOptions()

rests to defaults.

evaluateAttribute

public double evaluateAttribute(int attribute)
                         throws java.lang.Exception

evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.

Specified by:

evaluateAttribute in class AttributeEvaluator

Parameters:

attribute - the index of the attribute to be evaluated

Returns:

the "merit" of the attribute

Throws:

java.lang.Exception - if the attribute could not be evaluated

toString

public java.lang.String toString()

Return a description of the evaluator

Returns:

description as a string

main

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

Main method for testing this class.

Parameters:

args - the options