Package weka.attributeSelection
Class WrapperSubsetEval
- java.lang.Object
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- weka.attributeSelection.ASEvaluation
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- weka.attributeSelection.WrapperSubsetEval
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- All Implemented Interfaces:
java.io.Serializable
,SubsetEvaluator
,CapabilitiesHandler
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
public class WrapperSubsetEval extends ASEvaluation implements SubsetEvaluator, OptionHandler, TechnicalInformationHandler
WrapperSubsetEval:
Evaluates attribute sets by using a learning scheme. Cross validation is used to estimate the accuracy of the learning scheme for a set of attributes.
For more information see:
Ron Kohavi, George H. John (1997). Wrappers for feature subset selection. Artificial Intelligence. 97(1-2):273-324. BibTeX:@article{Kohavi1997, author = {Ron Kohavi and George H. John}, journal = {Artificial Intelligence}, note = {Special issue on relevance}, number = {1-2}, pages = {273-324}, title = {Wrappers for feature subset selection}, volume = {97}, year = {1997}, ISSN = {0004-3702} }
Valid options are:-B <base learner> class name of base learner to use for accuracy estimation. Place any classifier options LAST on the command line following a "--". eg.: -B weka.classifiers.bayes.NaiveBayes ... -- -K (default: weka.classifiers.rules.ZeroR)
-F <num> number of cross validation folds to use for estimating accuracy. (default=5)
-R <seed> Seed for cross validation accuracy testimation. (default = 1)
-T <num> threshold by which to execute another cross validation (standard deviation---expressed as a percentage of the mean). (default: 0.01 (1%))
Options specific to scheme weka.classifiers.rules.ZeroR:
-D If set, classifier is run in debug mode and may output additional info to the console
- Version:
- $Revision: 11851 $
- Author:
- Mark Hall (mhall@cs.waikato.ac.nz)
- See Also:
- Serialized Form
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Constructor Summary
Constructors Constructor Description WrapperSubsetEval()
Constructor.
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
buildEvaluator(Instances data)
Generates a attribute evaluator.java.lang.String
classifierTipText()
Returns the tip text for this propertyvoid
clean()
Tells the evaluator that the attribute selection process is complete.double
evaluateSubset(java.util.BitSet subset)
Evaluates a subset of attributesjava.lang.String
foldsTipText()
Returns the tip text for this propertyCapabilities
getCapabilities()
Returns the capabilities of this evaluator.Classifier
getClassifier()
Get the classifier used as the base learner.int
getFolds()
Get the number of folds used for accuracy estimationjava.lang.String[]
getOptions()
Gets the current settings of WrapperSubsetEval.java.lang.String
getRevision()
Returns the revision string.int
getSeed()
Get the random number seed used for cross validationTechnicalInformation
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 value of the thresholdjava.lang.String
globalInfo()
Returns a string describing this attribute evaluatorjava.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
seedTipText()
Returns the tip text for this propertyvoid
setClassifier(Classifier newClassifier)
Set the classifier to use for accuracy estimationvoid
setFolds(int f)
Set the number of folds to use for accuracy estimationvoid
setOptions(java.lang.String[] options)
Parses a given list of options.void
setSeed(int s)
Set the seed to use for cross validationvoid
setThreshold(double t)
Set the value of the threshold for repeating cross validationjava.lang.String
thresholdTipText()
Returns the tip text for this propertyjava.lang.String
toString()
Returns a string describing the wrapper-
Methods inherited from class weka.attributeSelection.ASEvaluation
forName, makeCopies, postProcess
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Method Detail
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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
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Returns:
- an enumeration of all the available options.
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setOptions
public void setOptions(java.lang.String[] options) throws java.lang.Exception
Parses a given list of options. Valid options are:-B <base learner> class name of base learner to use for accuracy estimation. Place any classifier options LAST on the command line following a "--". eg.: -B weka.classifiers.bayes.NaiveBayes ... -- -K (default: weka.classifiers.rules.ZeroR)
-F <num> number of cross validation folds to use for estimating accuracy. (default=5)
-R <seed> Seed for cross validation accuracy testimation. (default = 1)
-T <num> threshold by which to execute another cross validation (standard deviation---expressed as a percentage of the mean). (default: 0.01 (1%))
Options specific to scheme weka.classifiers.rules.ZeroR:
-D If set, classifier is run in debug mode and may output additional info to the console
- Specified by:
setOptions
in interfaceOptionHandler
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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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
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setThreshold
public void setThreshold(double t)
Set the value of the threshold for repeating cross validation- Parameters:
t
- the value of the threshold
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getThreshold
public double getThreshold()
Get the value of the threshold- Returns:
- the threshold as a double
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foldsTipText
public java.lang.String foldsTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setFolds
public void setFolds(int f)
Set the number of folds to use for accuracy estimation- Parameters:
f
- the number of folds
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getFolds
public int getFolds()
Get the number of folds used for accuracy estimation- Returns:
- the number of folds
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seedTipText
public java.lang.String seedTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setSeed
public void setSeed(int s)
Set the seed to use for cross validation- Parameters:
s
- the seed
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getSeed
public int getSeed()
Get the random number seed used for cross validation- Returns:
- the seed
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classifierTipText
public java.lang.String classifierTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setClassifier
public void setClassifier(Classifier newClassifier)
Set the classifier to use for accuracy estimation- Parameters:
newClassifier
- the Classifier to use.
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getClassifier
public Classifier getClassifier()
Get the classifier used as the base learner.- Returns:
- the classifier used as the classifier
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of WrapperSubsetEval.- Specified by:
getOptions
in interfaceOptionHandler
- Returns:
- an array of strings suitable for passing to setOptions()
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getCapabilities
public Capabilities getCapabilities()
Returns the capabilities of this evaluator.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classASEvaluation
- Returns:
- the capabilities of this evaluator
- See Also:
Capabilities
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buildEvaluator
public void buildEvaluator(Instances data) throws java.lang.Exception
Generates a attribute evaluator. Has to initialize all fields of the evaluator that are not being set via options.- Specified by:
buildEvaluator
in classASEvaluation
- Parameters:
data
- set of instances serving as training data- Throws:
java.lang.Exception
- if the evaluator has not been generated successfully
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evaluateSubset
public double evaluateSubset(java.util.BitSet subset) throws java.lang.Exception
Evaluates a subset of attributes- Specified by:
evaluateSubset
in interfaceSubsetEvaluator
- Parameters:
subset
- a bitset representing the attribute subset to be evaluated- Returns:
- the error rate
- Throws:
java.lang.Exception
- if the subset could not be evaluated
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toString
public java.lang.String toString()
Returns a string describing the wrapper- Overrides:
toString
in classjava.lang.Object
- Returns:
- the description as a string
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classASEvaluation
- Returns:
- the revision
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clean
public void clean()
Description copied from class:ASEvaluation
Tells the evaluator that the attribute selection process is complete. It can then clean up data structures, references to training data as necessary in order to save memory- Overrides:
clean
in classASEvaluation
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main
public static void main(java.lang.String[] args)
Main method for testing this class.- Parameters:
args
- the options
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