RaceSearch

java.lang.Object
- weka.attributeSelection.ASSearch
- - weka.attributeSelection.RaceSearch

All Implemented Interfaces:

java.io.Serializable, RankedOutputSearch, OptionHandler, RevisionHandler, TechnicalInformationHandler
```
public class RaceSearch
extends ASSearch
implements RankedOutputSearch, OptionHandler, TechnicalInformationHandler
```
Races the cross validation error of competing attribute subsets. Use in conjuction with a ClassifierSubsetEval. RaceSearch has four modes:

forward selection races all single attribute additions to a base set (initially no attributes), selects the winner to become the new base set and then iterates until there is no improvement over the base set.

Backward elimination is similar but the initial base set has all attributes included and races all single attribute deletions.

Schemata search is a bit different. Each iteration a series of races are run in parallel. Each race in a set determines whether a particular attribute should be included or not---ie the race is between the attribute being "in" or "out". The other attributes for this race are included or excluded randomly at each point in the evaluation. As soon as one race has a clear winner (ie it has been decided whether a particular attribute should be inor not) then the next set of races begins, using the result of the winning race from the previous iteration as new base set.

Rank race first ranks the attributes using an attribute evaluator and then races the ranking. The race includes no attributes, the top ranked attribute, the top two attributes, the top three attributes, etc.

It is also possible to generate a raked list of attributes through the forward racing process. If generateRanking is set to true then a complete forward race will be run---that is, racing continues until all attributes have been selected. The order that they are added in determines a complete ranking of all the attributes.

Racing uses paired and unpaired t-tests on cross-validation errors of competing subsets. When there is a significant difference between the means of the errors of two competing subsets then the poorer of the two can be eliminated from the race. Similarly, if there is no significant difference between the mean errors of two competing subsets and they are within some threshold of each other, then one can be eliminated from the race.

For more information see:

Andrew W. Moore, Mary S. Lee: Efficient Algorithms for Minimizing Cross Validation Error. In: Eleventh International Conference on Machine Learning, 190-198, 1994.
BibTeX:
```
 @inproceedings{Moore1994,
    author = {Andrew W. Moore and Mary S. Lee},
    booktitle = {Eleventh International Conference on Machine Learning},
    pages = {190-198},
    publisher = {Morgan Kaufmann},
    title = {Efficient Algorithms for Minimizing Cross Validation Error},
    year = {1994}
 }
 
```
Valid options are:
```
 -R <0 = forward | 1 = backward race | 2 = schemata | 3 = rank>
  Type of race to perform.
  (default = 0).
```
```
 -L <significance>
  Significance level for comaparisons
  (default = 0.001(forward/backward/rank)/0.01(schemata)).
```
```
 -T <threshold>
  Threshold for error comparison.
  (default = 0.001).
```
```
 -A <attribute evaluator>
  Attribute ranker to use if doing a 
  rank search. Place any
  evaluator options LAST on 
  the command line following a "--".
  eg. -A weka.attributeSelection.GainRatioAttributeEval ... -- -M.
  (default = GainRatioAttributeEval)
```
```
 -F <0 = 10 fold | 1 = leave-one-out>
  Folds for cross validation
  (default = 0 (1 if schemata race)
```
```
 -Q
  Generate a ranked list of attributes.
  Forces the search to be forward
  and races until all attributes have
  selected, thus producing a ranking.
```
```
 -N <num to select>
  Specify number of attributes to retain from 
  the ranking. Overides -T. Use in conjunction with -Q
```
```
 -J <threshold>
  Specify a theshold by which attributes
  may be discarded from the ranking.
  Use in conjuction with -Q
```
```
 -Z
  Verbose output for monitoring the search.
```
```
 
 Options specific to evaluator weka.attributeSelection.GainRatioAttributeEval:
 
```
```
 -M
  treat missing values as a seperate value.
```
Version:

$Revision: 1.26 $

Author:

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

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type Field and Description

static Tag[] TAGS_SELECTION

static Tag[] XVALTAGS_SELECTION

Fields
Modifier and Type	Field and Description
`static Tag[]`	`TAGS_SELECTION`
`static Tag[]`	`XVALTAGS_SELECTION`

Constructor Summary

Constructors
Constructor and Description

RaceSearch()

Constructors
Constructor and Description
`RaceSearch()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String`	`attributeEvaluatorTipText()` Returns the tip text for this property
`java.lang.String`	`debugTipText()` Returns the tip text for this property
`java.lang.String`	`foldsTypeTipText()` Returns the tip text for this property
`java.lang.String`	`generateRankingTipText()` Returns the tip text for this property
`ASEvaluation`	`getAttributeEvaluator()` Get the attribute evaluator used to generate the ranking.
`int`	`getCalculatedNumToSelect()` Gets the calculated number of attributes to retain.
`boolean`	`getDebug()` Get whether output is to be verbose
`SelectedTag`	`getFoldsType()` Get the xfold type
`boolean`	`getGenerateRanking()` Gets whether ranking has been requested.
`int`	`getNumToSelect()` Gets the number of attributes to be retained.
`java.lang.String[]`	`getOptions()` Gets the current settings of BestFirst.
`SelectedTag`	`getRaceType()` Get the race type
`java.lang.String`	`getRevision()` Returns the revision string.
`double`	`getSelectionThreshold()` Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
`double`	`getSignificanceLevel()` Get the significance level
`TechnicalInformation`	`getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`double`	`getThreshold()` Get the threshold
`java.lang.String`	`globalInfo()` Returns a string describing this search method
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`java.lang.String`	`numToSelectTipText()` Returns the tip text for this property
`java.lang.String`	`raceTypeTipText()` Returns the tip text for this property
`double[][]`	`rankedAttributes()` Returns a X by 2 list of attribute indexes and corresponding evaluations from best (highest) to worst.
`int[]`	`search(ASEvaluation ASEval, Instances data)` Searches the attribute subset space by racing cross validation errors of competing subsets
`java.lang.String`	`selectionThresholdTipText()` Returns the tip text for this property
`void`	`setAttributeEvaluator(ASEvaluation newEvaluator)` Set the attribute evaluator to use for generating the ranking.
`void`	`setDebug(boolean d)` Set whether verbose output should be generated.
`void`	`setFoldsType(SelectedTag d)` Set the xfold type
`void`	`setGenerateRanking(boolean doRank)` Records whether the user has requested a ranked list of attributes.
`void`	`setNumToSelect(int n)` Specify the number of attributes to select from the ranked list (if generating a ranking).
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setRaceType(SelectedTag d)` Set the race type
`void`	`setSelectionThreshold(double threshold)` Set the threshold by which the AttributeSelection module can discard attributes.
`void`	`setSignificanceLevel(double sig)` Sets the significance level to use
`void`	`setThreshold(double t)` Sets the threshold for comparisons
`java.lang.String`	`significanceLevelTipText()` Returns the tip text for this property
`java.lang.String`	`thresholdTipText()` Returns the tip text for this property
`java.lang.String`	`toString()` Returns a string represenation

Methods inherited from class weka.attributeSelection.ASSearch
forName, makeCopies

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - TAGS_SELECTION
```
public static final Tag[] TAGS_SELECTION
```
  - XVALTAGS_SELECTION
```
public static final Tag[] XVALTAGS_SELECTION
```
- Constructor Detail
  - RaceSearch
```
public RaceSearch()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing this search method
    
    Returns:
    
    a description of the search method suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public TechnicalInformation getTechnicalInformation()
```
    Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
    
    Specified by:
    
    getTechnicalInformation in interface TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - raceTypeTipText
```
public java.lang.String raceTypeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setRaceType
```
public void setRaceType(SelectedTag d)
```
    Set the race type
    
    Parameters:
    
    d - the type of race
  - getRaceType
```
public SelectedTag getRaceType()
```
    Get the race type
    
    Returns:
    
    the type of race
  - significanceLevelTipText
```
public java.lang.String significanceLevelTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setSignificanceLevel
```
public void setSignificanceLevel(double sig)
```
    Sets the significance level to use
    
    Parameters:
    
    sig - the significance level
  - getSignificanceLevel
```
public double getSignificanceLevel()
```
    Get the significance level
    
    Returns:
    
    the current significance level
  - thresholdTipText
```
public java.lang.String thresholdTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setThreshold
```
public void setThreshold(double t)
```
    Sets the threshold for comparisons
    
    Specified by:
    
    setThreshold in interface RankedOutputSearch
    
    Parameters:
    
    t - the threshold to use
  - getThreshold
```
public double getThreshold()
```
    Get the threshold
    
    Specified by:
    
    getThreshold in interface RankedOutputSearch
    
    Returns:
    
    the current threshold
  - foldsTypeTipText
```
public java.lang.String foldsTypeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setFoldsType
```
public void setFoldsType(SelectedTag d)
```
    Set the xfold type
    
    Parameters:
    
    d - the type of xval
  - getFoldsType
```
public SelectedTag getFoldsType()
```
    Get the xfold type
    
    Returns:
    
    the type of xval
  - debugTipText
```
public java.lang.String debugTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDebug
```
public void setDebug(boolean d)
```
    Set whether verbose output should be generated.
    
    Parameters:
    
    d - true if output is to be verbose.
  - getDebug
```
public boolean getDebug()
```
    Get whether output is to be verbose
    
    Returns:
    
    true if output will be verbose
  - attributeEvaluatorTipText
```
public java.lang.String attributeEvaluatorTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setAttributeEvaluator
```
public void setAttributeEvaluator(ASEvaluation newEvaluator)
```
    Set the attribute evaluator to use for generating the ranking.
    
    Parameters:
    
    newEvaluator - the attribute evaluator to use.
  - getAttributeEvaluator
```
public ASEvaluation getAttributeEvaluator()
```
    Get the attribute evaluator used to generate the ranking.
    
    Returns:
    
    the evaluator used to generate the ranking.
  - generateRankingTipText
```
public java.lang.String generateRankingTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setGenerateRanking
```
public void setGenerateRanking(boolean doRank)
```
    Records whether the user has requested a ranked list of attributes.
    
    Specified by:
    
    setGenerateRanking in interface RankedOutputSearch
    
    Parameters:
    
    doRank - true if ranking is requested
  - getGenerateRanking
```
public boolean getGenerateRanking()
```
    Gets whether ranking has been requested. This is used by the AttributeSelection module to determine if rankedAttributes() should be called.
    
    Specified by:
    
    getGenerateRanking in interface RankedOutputSearch
    
    Returns:
    
    true if ranking has been requested.
  - numToSelectTipText
```
public java.lang.String numToSelectTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNumToSelect
```
public void setNumToSelect(int n)
```
    Specify the number of attributes to select from the ranked list (if generating a ranking). -1 indicates that all attributes are to be retained.
    
    Specified by:
    
    setNumToSelect in interface RankedOutputSearch
    
    Parameters:
    
    n - the number of attributes to retain
  - getNumToSelect
```
public int getNumToSelect()
```
    Gets the number of attributes to be retained.
    
    Specified by:
    
    getNumToSelect in interface RankedOutputSearch
    
    Returns:
    
    the number of attributes to retain
  - getCalculatedNumToSelect
```
public int getCalculatedNumToSelect()
```
    Gets the calculated number of attributes to retain. This is the actual number of attributes to retain. This is the same as getNumToSelect if the user specifies a number which is not less than zero. Otherwise it should be the number of attributes in the (potentially transformed) data.
    
    Specified by:
    
    getCalculatedNumToSelect in interface RankedOutputSearch
  - selectionThresholdTipText
```
public java.lang.String selectionThresholdTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setSelectionThreshold
```
public void setSelectionThreshold(double threshold)
```
    Set the threshold by which the AttributeSelection module can discard attributes.
    
    Parameters:
    
    threshold - the threshold.
  - getSelectionThreshold
```
public double getSelectionThreshold()
```
    Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
  - 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:
```
 -R <0 = forward | 1 = backward race | 2 = schemata | 3 = rank>
  Type of race to perform.
  (default = 0).
```
```
 -L <significance>
  Significance level for comaparisons
  (default = 0.001(forward/backward/rank)/0.01(schemata)).
```
```
 -T <threshold>
  Threshold for error comparison.
  (default = 0.001).
```
```
 -A <attribute evaluator>
  Attribute ranker to use if doing a 
  rank search. Place any
  evaluator options LAST on 
  the command line following a "--".
  eg. -A weka.attributeSelection.GainRatioAttributeEval ... -- -M.
  (default = GainRatioAttributeEval)
```
```
 -F <0 = 10 fold | 1 = leave-one-out>
  Folds for cross validation
  (default = 0 (1 if schemata race)
```
```
 -Q
  Generate a ranked list of attributes.
  Forces the search to be forward
  and races until all attributes have
  selected, thus producing a ranking.
```
```
 -N <num to select>
  Specify number of attributes to retain from 
  the ranking. Overides -T. Use in conjunction with -Q
```
```
 -J <threshold>
  Specify a theshold by which attributes
  may be discarded from the ranking.
  Use in conjuction with -Q
```
```
 -Z
  Verbose output for monitoring the search.
```
```
 
 Options specific to evaluator weka.attributeSelection.GainRatioAttributeEval:
 
```
```
 -M
  treat missing values as a seperate value.
```
    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()
```
    Gets the current settings of BestFirst.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Returns:
    
    an array of strings suitable for passing to setOptions()
  - search
```
public int[] search(ASEvaluation ASEval,
                    Instances data)
             throws java.lang.Exception
```
    Searches the attribute subset space by racing cross validation errors of competing subsets
    
    Specified by:
    
    search in class ASSearch
    
    Parameters:
    
    ASEval - the attribute evaluator to guide the search
    
    data - the training instances.
    
    Returns:
    
    an array (not necessarily ordered) of selected attribute indexes
    
    Throws:
    
    java.lang.Exception - if the search can't be completed
  - rankedAttributes
```
public double[][] rankedAttributes()
                            throws java.lang.Exception
```
    Description copied from interface: RankedOutputSearch
    
    Returns a X by 2 list of attribute indexes and corresponding evaluations from best (highest) to worst.
    
    Specified by:
    
    rankedAttributes in interface RankedOutputSearch
    
    Returns:
    
    the ranked list of attribute indexes in an array of ints
    
    Throws:
    
    java.lang.Exception - if the ranking can't be produced
  - toString
```
public java.lang.String toString()
```
    Returns a string represenation
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string representation
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class ASSearch
    
    Returns:
    
    the revision

Class RaceSearch

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.attributeSelection.ASSearch

Methods inherited from class java.lang.Object

Field Detail

TAGS_SELECTION

XVALTAGS_SELECTION

Constructor Detail

RaceSearch

Method Detail

globalInfo

getTechnicalInformation

raceTypeTipText

setRaceType

getRaceType

significanceLevelTipText

setSignificanceLevel

getSignificanceLevel

thresholdTipText

setThreshold

getThreshold

foldsTypeTipText

setFoldsType

getFoldsType

debugTipText

setDebug

getDebug

attributeEvaluatorTipText

setAttributeEvaluator

getAttributeEvaluator

generateRankingTipText

setGenerateRanking

getGenerateRanking

numToSelectTipText

setNumToSelect

getNumToSelect

getCalculatedNumToSelect

selectionThresholdTipText

setSelectionThreshold

getSelectionThreshold

listOptions

setOptions

getOptions

search

rankedAttributes

toString

getRevision