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).
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 |
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void |
buildClassifier(Instances data)
Build the classifier on the dimensionally reduced data.
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double[] |
distributionForInstance(Instance instance)
Classifies a given instance after attribute selection
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java.util.Enumeration |
enumerateMeasures()
Returns an enumeration of the additional measure names
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java.lang.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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double |
getMeasure(java.lang.String additionalMeasureName)
Returns the value of the named measure
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java.lang.String[] |
getOptions()
Gets the current settings of the Classifier.
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java.lang.String |
getRevision()
Returns the revision string.
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ASSearch |
getSearch()
Gets the search method used
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java.lang.String |
globalInfo()
Returns a string describing this search method
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java.lang.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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java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options.
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static void |
main(java.lang.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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java.lang.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(java.lang.String[] options)
Parses a given list of options.
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void |
setSearch(ASSearch search)
Sets the search method
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java.lang.String |
toString()
Output a representation of this classifier
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classifierTipText, getClassifier, setClassifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
public AttributeSelectedClassifier()
public java.lang.String globalInfo()
public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class SingleClassifierEnhancer
public void setOptions(java.lang.String[] options) throws java.lang.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 stringsjava.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class SingleClassifierEnhancer
public java.lang.String evaluatorTipText()
public void setEvaluator(ASEvaluation evaluator)
evaluator
- the evaluator with all options set.public ASEvaluation getEvaluator()
public java.lang.String searchTipText()
public void setSearch(ASSearch search)
search
- the search method with all options set.public ASSearch getSearch()
public Capabilities getCapabilities()
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class SingleClassifierEnhancer
Capabilities
public void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in class Classifier
data
- the training datajava.lang.Exception
- if the classifier could not be built successfullypublic double[] distributionForInstance(Instance instance) throws java.lang.Exception
distributionForInstance
in class Classifier
instance
- the instance to be classifiedjava.lang.Exception
- if instance could not be classified
successfullypublic int graphType()
public java.lang.String graph() throws java.lang.Exception
public java.lang.String toString()
toString
in class java.lang.Object
public double measureNumAttributesSelected()
public double measureSelectionTime()
public double measureTime()
public java.util.Enumeration enumerateMeasures()
enumerateMeasures
in interface AdditionalMeasureProducer
public double getMeasure(java.lang.String additionalMeasureName)
getMeasure
in interface AdditionalMeasureProducer
additionalMeasureName
- the name of the measure to query for its valuejava.lang.IllegalArgumentException
- if the named measure is not supportedpublic java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class Classifier
public static void main(java.lang.String[] argv)
argv
- should contain the following arguments:
-t training file [-T test file] [-c class index]