ClassifierAttributeEval

java.lang.Object
- weka.attributeSelection.ASEvaluation
- - weka.attributeSelection.ClassifierAttributeEval

All Implemented Interfaces:: java.io.Serializable, AttributeEvaluator, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler

public class ClassifierAttributeEval
extends ASEvaluation
implements AttributeEvaluator, OptionHandler

ClassifierAttributeEval :

Evaluates the worth of an attribute by using a user-specified classifier.

Valid options are:

 -L
  Evaluate an attribute by measuring the impact of leaving it out
  from the full set instead of considering its worth in isolation

 -execution-slots <integer>
  Number of attributes to evaluate in parallel.
  Default = 1 (i.e. no parallelism)

 -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%))

 -E <acc | rmse | mae | f-meas | auc | auprc>
  Performance evaluation measure to use for selecting attributes.
  (Default = accuracy for discrete class and rmse for numeric class)

 -IRclass <label | index>
  Optional class value (label or 1-based index) to use in conjunction with
  IR statistics (f-meas, auc or auprc). Omitting this option will use
  the class-weighted average.

 
 Options specific to scheme weka.classifiers.rules.ZeroR:

 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).

Version:: $Revision: 14195 $
Author:: Mark Hall (mhall@cs.waikato.ac.nz), FracPete (fracpete at waikato dot ac dot nz)
See Also:: Serialized Form

Constructor Summary

Constructors
Constructor and Description

ClassifierAttributeEval()
Constructor.

Constructors
Constructor and Description
`ClassifierAttributeEval()` Constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildEvaluator(Instances data)` Initializes a ClassifierAttribute attribute evaluator.
`java.lang.String`	`classifierTipText()` Returns the tip text for this property
`double`	`evaluateAttribute(int attribute)` Evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.
`java.lang.String`	`evaluationMeasureTipText()` Returns the tip text for this property
`java.lang.String`	`foldsTipText()` Returns the tip text for this property
`Capabilities`	`getCapabilities()` Returns the capabilities of this evaluator.
`Classifier`	`getClassifier()` Get the classifier used as the base learner.
`SelectedTag`	`getEvaluationMeasure()` Gets the currently set performance evaluation measure used for selecting attributes for the decision table
`int`	`getFolds()` Get the number of folds used for accuracy estimation
`java.lang.String`	`getIRClassValue()` Get the class value (label or index) to use with IR metric evaluation of subsets.
`boolean`	`getLeaveOneAttributeOut()` Get whether to evaluate the merit of an attribute based on the impact of leaving it out from the full set instead of considering its worth in isolation
`int`	`getNumToEvaluateInParallel()` Get the number of attributes to evaluate in parallel
`java.lang.String[]`	`getOptions()` returns the current setup.
`java.lang.String`	`getRevision()` Returns the revision string.
`int`	`getSeed()` Get the random number seed used for cross validation
`double`	`getThreshold()` Get the value of the threshold
`java.lang.String`	`globalInfo()` Returns a string describing this attribute evaluator.
`java.lang.String`	`IRClassValueTipText()` Returns the tip text for this property
`java.lang.String`	`leaveOneAttributeOutTipText()` Tip text for this property
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] args)` Main method for executing this class.
`java.lang.String`	`numToEvaluateInParallelTipText()` Tip text for this property.
`java.lang.String`	`seedTipText()` Returns the tip text for this property
`void`	`setClassifier(Classifier newClassifier)` Set the classifier to use for accuracy estimation
`void`	`setEvaluationMeasure(SelectedTag newMethod)` Sets the performance evaluation measure to use for selecting attributes for the decision table
`void`	`setFolds(int f)` Set the number of folds to use for accuracy estimation
`void`	`setIRClassValue(java.lang.String val)` Set the class value (label or index) to use with IR metric evaluation of subsets.
`void`	`setLeaveOneAttributeOut(boolean l)` Set whether to evaluate the merit of an attribute based on the impact of leaving it out from the full set instead of considering its worth in isolation
`void`	`setNumToEvaluateInParallel(int n)` Set the number of attributes to evaluate in parallel
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setSeed(int s)` Set the seed to use for cross validation
`void`	`setThreshold(double t)` Set the value of the threshold for repeating cross validation
`java.lang.String`	`thresholdTipText()` Returns the tip text for this property
`java.lang.String`	`toString()` Return a description of the evaluator.

Methods inherited from class weka.attributeSelection.ASEvaluation
clean, doNotCheckCapabilitiesTipText, forName, getDoNotCheckCapabilities, makeCopies, postExecution, postProcess, preExecution, run, runEvaluator, setDoNotCheckCapabilities

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

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Constructor Detail
  - ClassifierAttributeEval
```
public ClassifierAttributeEval()
```
    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
  - listOptions
```
public java.util.Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class ASEvaluation
    
    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:
```
 -L
  Evaluate an attribute by measuring the impact of leaving it out
  from the full set instead of considering its worth in isolation
```
```
 -execution-slots <integer>
  Number of attributes to evaluate in parallel.
  Default = 1 (i.e. no parallelism)
```
```
 -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%))
```
```
 -E <acc | rmse | mae | f-meas | auc | auprc>
  Performance evaluation measure to use for selecting attributes.
  (Default = accuracy for discrete class and rmse for numeric class)
```
```
 -IRclass <label | index>
  Optional class value (label or 1-based index) to use in conjunction with
  IR statistics (f-meas, auc or auprc). Omitting this option will use
  the class-weighted average.
```
```
 
 Options specific to scheme weka.classifiers.rules.ZeroR:
 
```
```
 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console
```
```
 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class ASEvaluation
    
    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()
```
    returns the current setup.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class ASEvaluation
    
    Returns:
    
    the options of the current setup
  - leaveOneAttributeOutTipText
```
public java.lang.String leaveOneAttributeOutTipText()
```
    Tip text for this property
    
    Returns:
    
    the tip text for this property
  - setLeaveOneAttributeOut
```
public void setLeaveOneAttributeOut(boolean l)
```
    Set whether to evaluate the merit of an attribute based on the impact of leaving it out from the full set instead of considering its worth in isolation
    
    Parameters:
    
    l - true if each attribute should be evaluated by measuring the impact of leaving it out from the full set
  - getLeaveOneAttributeOut
```
public boolean getLeaveOneAttributeOut()
```
    Get whether to evaluate the merit of an attribute based on the impact of leaving it out from the full set instead of considering its worth in isolation
    
    Returns:
    
    true if each attribute should be evaluated by measuring the impact of leaving it out from the full set
  - numToEvaluateInParallelTipText
```
public java.lang.String numToEvaluateInParallelTipText()
```
    Tip text for this property.
    
    Returns:
    
    the tip text for this property
  - setNumToEvaluateInParallel
```
public void setNumToEvaluateInParallel(int n)
```
    Set the number of attributes to evaluate in parallel
    
    Parameters:
    
    n - the number of attributes to evaluate in parallel
  - getNumToEvaluateInParallel
```
public int getNumToEvaluateInParallel()
```
    Get the number of attributes to evaluate in parallel
    
    Returns:
    
    the number of attributes to evaluate in parallel
  - setIRClassValue
```
public void setIRClassValue(java.lang.String val)
```
    Set the class value (label or index) to use with IR metric evaluation of subsets. Leaving this unset will result in the class weighted average for the IR metric being used.
    
    Parameters:
    
    val - the class label or 1-based index of the class label to use when evaluating subsets with an IR metric
  - getIRClassValue
```
public java.lang.String getIRClassValue()
```
    Get the class value (label or index) to use with IR metric evaluation of subsets. Leaving this unset will result in the class weighted average for the IR metric being used.
    
    Returns:
    
    the class label or 1-based index of the class label to use when evaluating subsets with an IR metric
  - IRClassValueTipText
```
public java.lang.String IRClassValueTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - evaluationMeasureTipText
```
public java.lang.String evaluationMeasureTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getEvaluationMeasure
```
public SelectedTag getEvaluationMeasure()
```
    Gets the currently set performance evaluation measure used for selecting attributes for the decision table
    
    Returns:
    
    the performance evaluation measure
  - setEvaluationMeasure
```
public void setEvaluationMeasure(SelectedTag newMethod)
```
    Sets the performance evaluation measure to use for selecting attributes for the decision table
    
    Parameters:
    
    newMethod - the new performance evaluation metric to use
  - 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)
```
    Set the value of the threshold for repeating cross validation
    
    Parameters:
    
    t - the value of the threshold
  - getThreshold
```
public double getThreshold()
```
    Get the value of the threshold
    
    Returns:
    
    the threshold as a double
  - 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
  - setFolds
```
public void setFolds(int f)
```
    Set the number of folds to use for accuracy estimation
    
    Parameters:
    
    f - the number of folds
  - getFolds
```
public int getFolds()
```
    Get the number of folds used for accuracy estimation
    
    Returns:
    
    the number of folds
  - 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
  - setSeed
```
public void setSeed(int s)
```
    Set the seed to use for cross validation
    
    Parameters:
    
    s - the seed
  - getSeed
```
public int getSeed()
```
    Get the random number seed used for cross validation
    
    Returns:
    
    the seed
  - 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
  - setClassifier
```
public void setClassifier(Classifier newClassifier)
```
    Set the classifier to use for accuracy estimation
    
    Parameters:
    
    newClassifier - the Classifier to use.
  - getClassifier
```
public Classifier getClassifier()
```
    Get the classifier used as the base learner.
    
    Returns:
    
    the classifier used as the classifier
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns the capabilities of this evaluator.
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class ASEvaluation
    
    Returns:
    
    the capabilities of this evaluator
    
    See Also:
    
    Capabilities
  - buildEvaluator
```
public void buildEvaluator(Instances data)
                    throws java.lang.Exception
```
    Initializes a ClassifierAttribute attribute evaluator.
    
    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
  - 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 interface AttributeEvaluator
    
    Parameters:
    
    attribute - the index of the attribute to be evaluated
    
    Returns:
    
    the evaluation
    
    Throws:
    
    java.lang.Exception - if the attribute could not be evaluated
  - toString
```
public java.lang.String toString()
```
    Return a description of the evaluator.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    description as a string
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class ASEvaluation
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] args)
```
    Main method for executing this class.
    
    Parameters:
    
    args - the options

Class ClassifierAttributeEval

Constructor Summary

Method Summary

Methods inherited from class weka.attributeSelection.ASEvaluation

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

ClassifierAttributeEval

Method Detail

globalInfo

listOptions

setOptions

getOptions

leaveOneAttributeOutTipText

setLeaveOneAttributeOut

getLeaveOneAttributeOut

numToEvaluateInParallelTipText

setNumToEvaluateInParallel

getNumToEvaluateInParallel

setIRClassValue

getIRClassValue

IRClassValueTipText

evaluationMeasureTipText

getEvaluationMeasure

setEvaluationMeasure

thresholdTipText

setThreshold

getThreshold

foldsTipText

setFolds

getFolds

seedTipText

setSeed

getSeed

classifierTipText

setClassifier

getClassifier

getCapabilities

buildEvaluator

evaluateAttribute

toString

getRevision

main