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Java Source Code / Java Documentation » Science » weka » weka.attributeSelection

Source Cross Reference

Class Diagram

Java Document (Java Doc)

java.lang .Object

weka.attributeSelection .ASSearch

weka.attributeSelection .RankSearch

RankSearch

public class RankSearch extends ASSearch implements OptionHandler(Code)

RankSearch :

Uses an attribute/subset evaluator to rank all attributes. If a subset evaluator is specified, then a forward selection search is used to generate a ranked list. From the ranked list of attributes, subsets of increasing size are evaluated, ie. The best attribute, the best attribute plus the next best attribute, etc.... The best attribute set is reported. RankSearch is linear in the number of attributes if a simple attribute evaluator is used such as GainRatioAttributeEval.

Valid options are:

 -A <attribute evaluator>
 class name of attribute evaluator to use for ranking. Place any
 evaluator options LAST on the command line following a "--".
 eg.:
 -A weka.attributeSelection.GainRatioAttributeEval ... -- -M
 (default: weka.attributeSelection.GainRatioAttributeEval)

 -S <step size>
 number of attributes to be added from the
 ranking in each iteration (default = 1).

 -R <start point>
 point in the ranking to start evaluating from. 
 (default = 0, ie. the head of the ranking).

 
 Options specific to evaluator weka.attributeSelection.GainRatioAttributeEval:

 -M
 treat missing values as a seperate value.

author:
Mark Hall (mhall@cs.waikato.ac.nz)
version:
$Revision: 1.17 $

Field Summary
protected int	m_add
protected int	m_startPoint
final static long	serialVersionUID

Constructor Summary
public	RankSearch()

Method Summary
public String	attributeEvaluatorTipText()
public ASEvaluation	getAttributeEvaluator() Get the attribute evaluator used to generate the ranking.
public String[]	getOptions() Gets the current settings of WrapperSubsetEval.
public int	getStartPoint()
public int	getStepSize()
public String	globalInfo()
public Enumeration	listOptions() Returns an enumeration describing the available options.
protected void	resetOptions() Reset the search method.
public int[]	search(ASEvaluation ASEval, Instances data) Ranks attributes using the specified attribute evaluator and then searches the ranking using the supplied subset evaluator. Parameters: ASEval - the subset evaluator to guide the search Parameters: data - the training instances.
public void	setAttributeEvaluator(ASEvaluation newEvaluator) Set the attribute evaluator to use for generating the ranking.
public void	setOptions(String[] options) Parses a given list of options.
public void	setStartPoint(int sp)
public void	setStepSize(int ss)
public String	startPointTipText()
public String	stepSizeTipText()
public String	toString()

Field Detail

m_add
protected int m_add(Code)
	add this many attributes in each iteration from the ranking

m_startPoint
protected int m_startPoint(Code)
	start from this point in the ranking

serialVersionUID
final static long serialVersionUID(Code)
	for serialization

Constructor Detail

RankSearch
public RankSearch()(Code)
	Constructor

Method Detail

attributeEvaluatorTipText
public String attributeEvaluatorTipText()(Code)
	Returns the tip text for this property tip text for this property suitable fordisplaying in the explorer/experimenter gui

getAttributeEvaluator
public ASEvaluation getAttributeEvaluator()(Code)
	Get the attribute evaluator used to generate the ranking. the evaluator used to generate the ranking.

getOptions
public String[] getOptions()(Code)
	Gets the current settings of WrapperSubsetEval. an array of strings suitable for passing to setOptions()

getStartPoint
public int getStartPoint()(Code)
	Get the point at which to start evaluating the ranking the position in the ranking to start at

getStepSize
public int getStepSize()(Code)
	Get the number of attributes to add from the rankining in each iteration the number of attributes to add.

globalInfo
public String globalInfo()(Code)
	Returns a string describing this search method a description of the search method suitable fordisplaying in the explorer/experimenter gui

listOptions
public Enumeration listOptions()(Code)
	Returns an enumeration describing the available options. an enumeration of all the available options.

resetOptions
protected void resetOptions()(Code)
	Reset the search method.

search
public int[] search(ASEvaluation ASEval, Instances data) throws Exception(Code)
	Ranks attributes using the specified attribute evaluator and then searches the ranking using the supplied subset evaluator. Parameters: ASEval - the subset evaluator to guide the search Parameters: data - the training instances. an array (not necessarily ordered) of selected attribute indexes throws: Exception - if the search can't be completed

setAttributeEvaluator
public void setAttributeEvaluator(ASEvaluation newEvaluator)(Code)
	Set the attribute evaluator to use for generating the ranking. Parameters: newEvaluator - the attribute evaluator to use.

setOptions

public void setOptions(String[] options) throws Exception(Code)

Parses a given list of options.

Valid options are:

 -A <attribute evaluator>
 class name of attribute evaluator to use for ranking. Place any
 evaluator options LAST on the command line following a "--".
 eg.:
 -A weka.attributeSelection.GainRatioAttributeEval ... -- -M
 (default: weka.attributeSelection.GainRatioAttributeEval)

 -S <step size>
 number of attributes to be added from the
 ranking in each iteration (default = 1).

 -R <start point>
 point in the ranking to start evaluating from. 
 (default = 0, ie. the head of the ranking).

 
 Options specific to evaluator weka.attributeSelection.GainRatioAttributeEval:

 -M
 treat missing values as a seperate value.

Parameters:
options - the list of options as an array of strings
throws:
Exception - if an option is not supported

setStartPoint
public void setStartPoint(int sp)(Code)
	Set the point at which to start evaluating the ranking Parameters: sp - the position in the ranking to start at

setStepSize
public void setStepSize(int ss)(Code)
	Set the number of attributes to add from the rankining in each iteration Parameters: ss - the number of attribes to add.

startPointTipText
public String startPointTipText()(Code)
	Returns the tip text for this property tip text for this property suitable fordisplaying in the explorer/experimenter gui

stepSizeTipText
public String stepSizeTipText()(Code)
	Returns the tip text for this property tip text for this property suitable fordisplaying in the explorer/experimenter gui

toString
public String toString()(Code)
	returns a description of the search as a String a description of the search

Methods inherited from weka.attributeSelection.ASSearch

public static ASSearch forName(String searchName, String[] options) throws Exception(Code)(Java Doc)
public static ASSearch[] makeCopies(ASSearch model, int num) throws Exception(Code)(Java Doc)
abstract public int[] search(ASEvaluation ASEvaluator, Instances data) throws Exception(Code)(Java Doc)

Methods inherited from java.lang.Object

native protected Object clone() throws CloneNotSupportedException(Code)(Java Doc)
public boolean equals(Object obj)(Code)(Java Doc)
protected void finalize() throws Throwable(Code)(Java Doc)
final native public Class getClass()(Code)(Java Doc)
native public int hashCode()(Code)(Java Doc)
final native public void notify()(Code)(Java Doc)
final native public void notifyAll()(Code)(Java Doc)
public String toString()(Code)(Java Doc)
final native public void wait(long timeout) throws InterruptedException(Code)(Java Doc)
final public void wait(long timeout, int nanos) throws InterruptedException(Code)(Java Doc)
final public void wait() throws InterruptedException(Code)(Java Doc)

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