Package weka.attributeSelection
Class OneRAttributeEval
java.lang.Object
weka.attributeSelection.ASEvaluation
weka.attributeSelection.OneRAttributeEval
- All Implemented Interfaces:
Serializable,AttributeEvaluator,CapabilitiesHandler,CapabilitiesIgnorer,CommandlineRunnable,OptionHandler,RevisionHandler
OneRAttributeEval :
Evaluates the worth of an attribute by using the OneR classifier.
Valid options are:
Evaluates the worth of an attribute by using the OneR classifier.
Valid options are:
-S <seed> Random number seed for cross validation (default = 1)
-F <folds> Number of folds for cross validation (default = 10)
-D Use training data for evaluation rather than cross validaton
-B <minimum bucket size> Minimum number of objects in a bucket (passed on to OneR, default = 6)
- Version:
- $Revision: 15520 $
- Author:
- Mark Hall (mhall@cs.waikato.ac.nz)
- See Also:
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoidbuildEvaluator(Instances data) Initializes a OneRAttribute attribute evaluator.doubleevaluateAttribute(int attribute) evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.Returns a string for this option suitable for display in the gui as a tip textReturns a string for this option suitable for display in the gui as a tip textReturns the capabilities of this evaluator.booleanReturns true if the training data is to be used for evaluationintgetFolds()Get the number of folds used for cross validationintGet the minimum bucket size used by oneRString[]returns the current setup.Returns the revision string.intgetSeed()Get the random number seedReturns a string describing this attribute evaluatorReturns an enumeration describing the available options.static voidMain method for testing this class.Returns a string for this option suitable for display in the gui as a tip textint[]postProcess(int[] attributeSet) Provides a chance for a attribute evaluator to do any special post processing of the selected attribute set.Returns a string for this option suitable for display in the gui as a tip textvoidsetEvalUsingTrainingData(boolean e) Use the training data to evaluate attributes rather than cross validationvoidsetFolds(int folds) Set the number of folds to use for cross validationvoidsetMinimumBucketSize(int minB) Set the minumum bucket size used by OneRvoidsetOptions(String[] options) Parses a given list of options.voidsetSeed(int seed) Set the random number seed for cross validationtoString()Return a description of the evaluatorMethods inherited from class weka.attributeSelection.ASEvaluation
clean, doNotCheckCapabilitiesTipText, forName, getDoNotCheckCapabilities, makeCopies, postExecution, preExecution, run, runEvaluator, setDoNotCheckCapabilities
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Constructor Details
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OneRAttributeEval
public OneRAttributeEval()Constructor
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Method Details
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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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seedTipText
Returns a string for this option suitable for display in the gui as a tip text- Returns:
- a string describing this option
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setSeed
public void setSeed(int seed) Set the random number seed for cross validation- Parameters:
seed- the seed to use
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getSeed
public int getSeed()Get the random number seed- Returns:
- an
intvalue
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foldsTipText
Returns a string for this option suitable for display in the gui as a tip text- Returns:
- a string describing this option
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setFolds
public void setFolds(int folds) Set the number of folds to use for cross validation- Parameters:
folds- the number of folds
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getFolds
public int getFolds()Get the number of folds used for cross validation- Returns:
- the number of folds
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evalUsingTrainingDataTipText
Returns a string for this option suitable for display in the gui as a tip text- Returns:
- a string describing this option
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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
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minimumBucketSizeTipText
Returns a string for this option suitable for display in the gui as a tip text- Returns:
- a string describing this option
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setMinimumBucketSize
public void setMinimumBucketSize(int minB) Set the minumum bucket size used by OneR- Parameters:
minB- the minimum bucket size to use
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getMinimumBucketSize
public int getMinimumBucketSize()Get the minimum bucket size used by oneR- Returns:
- the minimum bucket size used
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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
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listOptions
Returns an enumeration describing the available options.- Specified by:
listOptionsin interfaceOptionHandler- Overrides:
listOptionsin classASEvaluation- Returns:
- an enumeration of all the available options.
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setOptions
Parses a given list of options. Valid options are:-S <seed> Random number seed for cross validation (default = 1)
-F <folds> Number of folds for cross validation (default = 10)
-D Use training data for evaluation rather than cross validaton
-B <minimum bucket size> Minimum number of objects in a bucket (passed on to OneR, default = 6)
- Specified by:
setOptionsin interfaceOptionHandler- Overrides:
setOptionsin classASEvaluation- Parameters:
options- the list of options as an array of strings- Throws:
Exception- if an option is not supported
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getOptions
returns the current setup.- Specified by:
getOptionsin interfaceOptionHandler- Overrides:
getOptionsin classASEvaluation- Returns:
- the options of the current setup
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getCapabilities
Returns the capabilities of this evaluator.- Specified by:
getCapabilitiesin interfaceCapabilitiesHandler- Overrides:
getCapabilitiesin classASEvaluation- Returns:
- the capabilities of this evaluator
- See Also:
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buildEvaluator
Initializes a OneRAttribute attribute evaluator. Discretizes all attributes that are numeric.- Specified by:
buildEvaluatorin classASEvaluation- Parameters:
data- set of instances serving as training data- Throws:
Exception- if the evaluator has not been generated successfully
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evaluateAttribute
evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.- Specified by:
evaluateAttributein interfaceAttributeEvaluator- Parameters:
attribute- the index of the attribute to be evaluated- Returns:
- the "merit" of the attribute
- Throws:
Exception- if the attribute could not be evaluated
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toString
Return a description of the evaluator -
getRevision
Returns the revision string.- Specified by:
getRevisionin interfaceRevisionHandler- Overrides:
getRevisionin classASEvaluation- Returns:
- the revision
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postProcess
public int[] postProcess(int[] attributeSet) Description copied from class:ASEvaluationProvides a chance for a attribute evaluator to do any special post processing of the selected attribute set. Can also be used to clean up any data structures post attribute selection.- Overrides:
postProcessin classASEvaluation- Parameters:
attributeSet- the set of attributes found by the search- Returns:
- a possibly ranked list of postprocessed attributes
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main
Main method for testing this class.- Parameters:
args- the options
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