AttributeSelectedClassifier (interruptible-weka 0.1.2 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - weka.classifiers.meta.AttributeSelectedClassifier

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, Classifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, Drawable, OptionHandler, RevisionHandler, WeightedInstancesHandler

public class AttributeSelectedClassifier
extends SingleClassifierEnhancer
implements OptionHandler, Drawable, AdditionalMeasureProducer, WeightedInstancesHandler

Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.

Valid options are:

  -E <attribute evaluator specification>
  Full class name of attribute evaluator, followed
  by its options.
  eg: "weka.attributeSelection.CfsSubsetEval -L"
  (default weka.attributeSelection.CfsSubsetEval)

  -S <search method specification>
  Full class name of search method, followed
  by its options.
  eg: "weka.attributeSelection.BestFirst -D 1"
  (default weka.attributeSelection.BestFirst)

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

  -W
  Full name of base classifier.
  (default: weka.classifiers.trees.J48)


 Options specific to classifier weka.classifiers.trees.J48:

  -U
  Use unpruned tree.

  -C <pruning confidence>
  Set confidence threshold for pruning.
  (default 0.25)

  -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 2)

  -R
  Use reduced error pruning.

  -N <number of folds>
  Set number of folds for reduced error
  pruning. One fold is used as pruning set.
  (default 3)

  -B
  Use binary splits only.

  -S
  Don't perform subtree raising.

  -L
  Do not clean up after the tree has been built.

  -A
  Laplace smoothing for predicted probabilities.

  -Q <seed>
  Seed for random data shuffling (default 1).

See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected AttributeSelection`	`m_AttributeSelection` The attribute selection object
`protected ASEvaluation`	`m_Evaluator` The attribute evaluator to use
`protected double`	`m_numAttributesSelected` The number of attributes selected by the attribute selection phase
`protected int`	`m_numClasses` The number of class vals in the training data (1 if class is numeric)
`protected Instances`	`m_ReducedHeader` The header of the dimensionally reduced data
`protected ASSearch`	`m_Search` The search method to use
`protected double`	`m_selectionTime` The time taken to select attributes in milliseconds
`protected double`	`m_totalTime` The time taken to select attributes AND build the classifier

Fields inherited from class weka.classifiers.SingleClassifierEnhancer
m_Classifier

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Fields inherited from interface weka.core.Drawable
BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

Constructors
Constructor and Description

AttributeSelectedClassifier()
Default constructor.

Constructors
Constructor and Description
`AttributeSelectedClassifier()` Default constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String`	`batchSizeTipText()` Tool tip text for this property
`void`	`buildClassifier(Instances data)` Build the classifier on the dimensionally reduced data.
`protected java.lang.String`	`defaultClassifierString()` String describing default classifier.
`double[]`	`distributionForInstance(Instance instance)` Classifies a given instance after attribute selection
`double[][]`	`distributionsForInstances(Instances insts)` Batch scoring method.
`java.util.Enumeration<java.lang.String>`	`enumerateMeasures()` Returns an enumeration of the additional measure names
`java.lang.String`	`evaluatorTipText()` Returns the tip text for this property
`java.lang.String`	`getBatchSize()` Gets the preferred batch size from the base learner if it implements BatchPredictor.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`ASEvaluation`	`getEvaluator()` Gets the attribute evaluator used
`protected java.lang.String`	`getEvaluatorSpec()` Gets the evaluator specification string, which contains the class name of the attribute evaluator and any options to it
`double`	`getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`java.lang.String`	`getRevision()` Returns the revision string.
`ASSearch`	`getSearch()` Gets the search method used
`protected java.lang.String`	`getSearchSpec()` Gets the search specification string, which contains the class name of the search method and any options to it
`java.lang.String`	`globalInfo()` Returns a string describing this search method
`java.lang.String`	`graph()` Returns graph describing the classifier (if possible).
`int`	`graphType()` Returns the type of graph this classifier represents.
`boolean`	`implementsMoreEfficientBatchPrediction()` Returns true if the base classifier implements BatchPredictor and is able to generate batch predictions efficiently
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`double`	`measureNumAttributesSelected()` Additional measure --- number of attributes selected
`double`	`measureSelectionTime()` Additional measure --- time taken (milliseconds) to select the attributes
`double`	`measureTime()` Additional measure --- time taken (milliseconds) to select attributes and build the classifier
`java.lang.String`	`searchTipText()` Returns the tip text for this property
`void`	`setBatchSize(java.lang.String size)` Set the batch size to use.
`void`	`setEvaluator(ASEvaluation evaluator)` Sets the attribute evaluator
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setSearch(ASSearch search)` Sets the search method
`java.lang.String`	`toString()` Output a representation of this classifier

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, defaultClassifierOptions, getClassifier, getClassifierSpec, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier
classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

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

- Field Detail
  - m_AttributeSelection
```
protected AttributeSelection m_AttributeSelection
```
    The attribute selection object
  - m_Evaluator
```
protected ASEvaluation m_Evaluator
```
    The attribute evaluator to use
  - m_Search
```
protected ASSearch m_Search
```
    The search method to use
  - m_ReducedHeader
```
protected Instances m_ReducedHeader
```
    The header of the dimensionally reduced data
  - m_numClasses
```
protected int m_numClasses
```
    The number of class vals in the training data (1 if class is numeric)
  - m_numAttributesSelected
```
protected double m_numAttributesSelected
```
    The number of attributes selected by the attribute selection phase
  - m_selectionTime
```
protected double m_selectionTime
```
    The time taken to select attributes in milliseconds
  - m_totalTime
```
protected double m_totalTime
```
    The time taken to select attributes AND build the classifier
- Constructor Detail
  - AttributeSelectedClassifier
```
public AttributeSelectedClassifier()
```
    Default constructor.
- Method Detail
  - defaultClassifierString
```
protected java.lang.String defaultClassifierString()
```
    String describing default classifier.
    
    Overrides:
    
    defaultClassifierString in class SingleClassifierEnhancer
    
    Returns:
    
    the default classifier classname
  - 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
  - listOptions
```
public java.util.Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class SingleClassifierEnhancer
    
    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:
```
  -E <attribute evaluator specification>
  Full class name of attribute evaluator, followed
  by its options.
  eg: "weka.attributeSelection.CfsSubsetEval -L"
  (default weka.attributeSelection.CfsSubsetEval)
 
```
```
  -S <search method specification>
  Full class name of search method, followed
  by its options.
  eg: "weka.attributeSelection.BestFirst -D 1"
  (default weka.attributeSelection.BestFirst)
 
```
```
  -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 
```
```
  -W
  Full name of base classifier.
  (default: weka.classifiers.trees.J48)
 
```
```
  
 Options specific to classifier weka.classifiers.trees.J48:
 
```
```
  -U
  Use unpruned tree.
 
```
```
  -C <pruning confidence>
  Set confidence threshold for pruning.
  (default 0.25)
 
```
```
  -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 2)
 
```
```
  -R
  Use reduced error pruning.
 
```
```
  -N <number of folds>
  Set number of folds for reduced error
  pruning. One fold is used as pruning set.
  (default 3)
 
```
```
  -B
  Use binary splits only.
 
```
```
  -S
  Don't perform subtree raising.
 
```
```
  -L
  Do not clean up after the tree has been built.
 
```
```
  -A
  Laplace smoothing for predicted probabilities.
 
```
```
  -Q <seed>
  Seed for random data shuffling (default 1).
 
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class SingleClassifierEnhancer
    
    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 the Classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class SingleClassifierEnhancer
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - evaluatorTipText
```
public java.lang.String evaluatorTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setEvaluator
```
public void setEvaluator(ASEvaluation evaluator)
```
    Sets the attribute evaluator
    
    Parameters:
    
    evaluator - the evaluator with all options set.
  - getEvaluator
```
public ASEvaluation getEvaluator()
```
    Gets the attribute evaluator used
    
    Returns:
    
    the attribute evaluator
  - getEvaluatorSpec
```
protected java.lang.String getEvaluatorSpec()
```
    Gets the evaluator specification string, which contains the class name of the attribute evaluator and any options to it
    
    Returns:
    
    the evaluator string.
  - searchTipText
```
public java.lang.String searchTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setSearch
```
public void setSearch(ASSearch search)
```
    Sets the search method
    
    Parameters:
    
    search - the search method with all options set.
  - getSearch
```
public ASSearch getSearch()
```
    Gets the search method used
    
    Returns:
    
    the search method
  - getSearchSpec
```
protected java.lang.String getSearchSpec()
```
    Gets the search specification string, which contains the class name of the search method and any options to it
    
    Returns:
    
    the search string.
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface Classifier
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class SingleClassifierEnhancer
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws java.lang.Exception
```
    Build the classifier on the dimensionally reduced data.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    data - the training data
    
    Throws:
    
    java.lang.Exception - if the classifier could not be built successfully
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
```
    Classifies a given instance after attribute selection
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    the class distribution
    
    Throws:
    
    java.lang.Exception - if instance could not be classified successfully
  - batchSizeTipText
```
public java.lang.String batchSizeTipText()
```
    Tool tip text for this property
    
    Overrides:
    
    batchSizeTipText in class AbstractClassifier
    
    Returns:
    
    the tool tip for this property
  - setBatchSize
```
public void setBatchSize(java.lang.String size)
```
    Set the batch size to use. Gets passed through to the base learner if it implements BatchPredictor. Otherwise it is just ignored.
    
    Specified by:
    
    setBatchSize in interface BatchPredictor
    
    Overrides:
    
    setBatchSize in class AbstractClassifier
    
    Parameters:
    
    size - the batch size to use
  - getBatchSize
```
public java.lang.String getBatchSize()
```
    Gets the preferred batch size from the base learner if it implements BatchPredictor. Returns 1 as the preferred batch size otherwise.
    
    Specified by:
    
    getBatchSize in interface BatchPredictor
    
    Overrides:
    
    getBatchSize in class AbstractClassifier
    
    Returns:
    
    the batch size to use
  - distributionsForInstances
```
public double[][] distributionsForInstances(Instances insts)
                                     throws java.lang.Exception
```
    Batch scoring method. Calls the appropriate method for the base learner if it implements BatchPredictor. Otherwise it simply calls the distributionForInstance() method repeatedly.
    
    Specified by:
    
    distributionsForInstances in interface BatchPredictor
    
    Overrides:
    
    distributionsForInstances in class AbstractClassifier
    
    Parameters:
    
    insts - the instances to get predictions for
    
    Returns:
    
    an array of probability distributions, one for each instance
    
    Throws:
    
    java.lang.Exception - if a problem occurs
  - implementsMoreEfficientBatchPrediction
```
public boolean implementsMoreEfficientBatchPrediction()
```
    Returns true if the base classifier implements BatchPredictor and is able to generate batch predictions efficiently
    
    Specified by:
    
    implementsMoreEfficientBatchPrediction in interface BatchPredictor
    
    Overrides:
    
    implementsMoreEfficientBatchPrediction in class AbstractClassifier
    
    Returns:
    
    true if the base classifier can generate batch predictions efficiently
  - graphType
```
public int graphType()
```
    Returns the type of graph this classifier represents.
    
    Specified by:
    
    graphType in interface Drawable
    
    Returns:
    
    the type of graph
  - graph
```
public java.lang.String graph()
                       throws java.lang.Exception
```
    Returns graph describing the classifier (if possible).
    
    Specified by:
    
    graph in interface Drawable
    
    Returns:
    
    the graph of the classifier in dotty format
    
    Throws:
    
    java.lang.Exception - if the classifier cannot be graphed
  - toString
```
public java.lang.String toString()
```
    Output a representation of this classifier
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a representation of this classifier
  - measureNumAttributesSelected
```
public double measureNumAttributesSelected()
```
    Additional measure --- number of attributes selected
    
    Returns:
    
    the number of attributes selected
  - measureSelectionTime
```
public double measureSelectionTime()
```
    Additional measure --- time taken (milliseconds) to select the attributes
    
    Returns:
    
    the time taken to select attributes
  - measureTime
```
public double measureTime()
```
    Additional measure --- time taken (milliseconds) to select attributes and build the classifier
    
    Returns:
    
    the total time (select attributes + build classifier)
  - enumerateMeasures
```
public java.util.Enumeration<java.lang.String> enumerateMeasures()
```
    Returns an enumeration of the additional measure names
    
    Specified by:
    
    enumerateMeasures in interface AdditionalMeasureProducer
    
    Returns:
    
    an enumeration of the measure names
  - getMeasure
```
public double getMeasure(java.lang.String additionalMeasureName)
```
    Returns the value of the named measure
    
    Specified by:
    
    getMeasure in interface AdditionalMeasureProducer
    
    Parameters:
    
    additionalMeasureName - the name of the measure to query for its value
    
    Returns:
    
    the value of the named measure
    
    Throws:
    
    java.lang.IllegalArgumentException - if the named measure is not supported
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - should contain the following arguments: -t training file [-T test file] [-c class index]

Class AttributeSelectedClassifier

Field Summary

Fields inherited from class weka.classifiers.SingleClassifierEnhancer

Fields inherited from class weka.classifiers.AbstractClassifier

Fields inherited from interface weka.core.Drawable

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Field Detail

m_AttributeSelection

m_Evaluator

m_Search

m_ReducedHeader

m_numClasses

m_numAttributesSelected

m_selectionTime

m_totalTime

Constructor Detail

AttributeSelectedClassifier

Method Detail

defaultClassifierString

globalInfo

listOptions

setOptions

getOptions

evaluatorTipText

setEvaluator

getEvaluator

getEvaluatorSpec

searchTipText

setSearch

getSearch

getSearchSpec

getCapabilities

buildClassifier

distributionForInstance

batchSizeTipText

setBatchSize

getBatchSize

distributionsForInstances

implementsMoreEfficientBatchPrediction

graphType

graph

toString

measureNumAttributesSelected

measureSelectionTime

measureTime

enumerateMeasures

getMeasure

getRevision

main