public class AttributeSelectedClassifier extends SingleClassifierEnhancer implements OptionHandler, Drawable, AdditionalMeasureProducer, WeightedInstancesHandler
-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).
Modifier and Type | Field and Description |
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protected AttributeSelection |
m_AttributeSelection
The attribute selection object
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protected ASEvaluation |
m_Evaluator
The attribute evaluator to use
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protected double |
m_numAttributesSelected
The number of attributes selected by the attribute selection phase
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protected int |
m_numClasses
The number of class vals in the training data (1 if class is numeric)
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protected Instances |
m_ReducedHeader
The header of the dimensionally reduced data
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protected ASSearch |
m_Search
The search method to use
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protected double |
m_selectionTime
The time taken to select attributes in milliseconds
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protected double |
m_totalTime
The time taken to select attributes AND build the classifier
|
m_Classifier
m_Debug
BayesNet, Newick, NOT_DRAWABLE, TREE
Constructor and Description |
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AttributeSelectedClassifier()
Default constructor.
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Modifier and Type | Method and Description |
---|---|
void |
buildClassifier(Instances data)
Build the classifier on the dimensionally reduced data.
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protected String |
defaultClassifierString()
String describing default classifier.
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double[] |
distributionForInstance(Instance instance)
Classifies a given instance after attribute selection
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Enumeration |
enumerateMeasures()
Returns an enumeration of the additional measure names
|
String |
evaluatorTipText()
Returns the tip text for this property
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Capabilities |
getCapabilities()
Returns default capabilities of the classifier.
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ASEvaluation |
getEvaluator()
Gets the attribute evaluator used
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protected String |
getEvaluatorSpec()
Gets the evaluator specification string, which contains the class name of
the attribute evaluator and any options to it
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double |
getMeasure(String additionalMeasureName)
Returns the value of the named measure
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String[] |
getOptions()
Gets the current settings of the Classifier.
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String |
getRevision()
Returns the revision string.
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ASSearch |
getSearch()
Gets the search method used
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protected String |
getSearchSpec()
Gets the search specification string, which contains the class name of
the search method and any options to it
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String |
globalInfo()
Returns a string describing this search method
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String |
graph()
Returns graph describing the classifier (if possible).
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int |
graphType()
Returns the type of graph this classifier
represents.
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Enumeration |
listOptions()
Returns an enumeration describing the available options.
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static void |
main(String[] argv)
Main method for testing this class.
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double |
measureNumAttributesSelected()
Additional measure --- number of attributes selected
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double |
measureSelectionTime()
Additional measure --- time taken (milliseconds) to select the attributes
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double |
measureTime()
Additional measure --- time taken (milliseconds) to select attributes
and build the classifier
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String |
searchTipText()
Returns the tip text for this property
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void |
setEvaluator(ASEvaluation evaluator)
Sets the attribute evaluator
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void |
setOptions(String[] options)
Parses a given list of options.
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void |
setSearch(ASSearch search)
Sets the search method
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String |
toString()
Output a representation of this classifier
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classifierTipText, getClassifier, getClassifierSpec, setClassifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug
protected AttributeSelection m_AttributeSelection
protected ASEvaluation m_Evaluator
protected ASSearch m_Search
protected Instances m_ReducedHeader
protected int m_numClasses
protected double m_numAttributesSelected
protected double m_selectionTime
protected double m_totalTime
public AttributeSelectedClassifier()
protected String defaultClassifierString()
defaultClassifierString
in class SingleClassifierEnhancer
public String globalInfo()
public Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class SingleClassifierEnhancer
public void setOptions(String[] options) throws Exception
-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).
setOptions
in interface OptionHandler
setOptions
in class SingleClassifierEnhancer
options
- the list of options as an array of stringsException
- if an option is not supportedpublic String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class SingleClassifierEnhancer
public String evaluatorTipText()
public void setEvaluator(ASEvaluation evaluator)
evaluator
- the evaluator with all options set.public ASEvaluation getEvaluator()
protected String getEvaluatorSpec()
public String searchTipText()
public void setSearch(ASSearch search)
search
- the search method with all options set.public ASSearch getSearch()
protected String getSearchSpec()
public Capabilities getCapabilities()
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class SingleClassifierEnhancer
Capabilities
public void buildClassifier(Instances data) throws Exception
buildClassifier
in class Classifier
data
- the training dataException
- if the classifier could not be built successfullypublic double[] distributionForInstance(Instance instance) throws Exception
distributionForInstance
in class Classifier
instance
- the instance to be classifiedException
- if instance could not be classified
successfullypublic int graphType()
public String toString()
public double measureNumAttributesSelected()
public double measureSelectionTime()
public double measureTime()
public Enumeration enumerateMeasures()
enumerateMeasures
in interface AdditionalMeasureProducer
public double getMeasure(String additionalMeasureName)
getMeasure
in interface AdditionalMeasureProducer
additionalMeasureName
- the name of the measure to query for its valueIllegalArgumentException
- if the named measure is not supportedpublic String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class Classifier
public static void main(String[] argv)
argv
- should contain the following arguments:
-t training file [-T test file] [-c class index]Copyright © 2015 University of Waikato, Hamilton, NZ. All rights reserved.