Package weka.classifiers.mi
Class MIWrapper
- java.lang.Object
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- weka.classifiers.Classifier
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- weka.classifiers.SingleClassifierEnhancer
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- weka.classifiers.mi.MIWrapper
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- All Implemented Interfaces:
java.io.Serializable
,java.lang.Cloneable
,CapabilitiesHandler
,MultiInstanceCapabilitiesHandler
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
public class MIWrapper extends SingleClassifierEnhancer implements MultiInstanceCapabilitiesHandler, OptionHandler, TechnicalInformationHandler
A simple Wrapper method for applying standard propositional learners to multi-instance data.
For more information see:
E. T. Frank, X. Xu (2003). Applying propositional learning algorithms to multi-instance data. Department of Computer Science, University of Waikato, Hamilton, NZ. BibTeX:@techreport{Frank2003, address = {Department of Computer Science, University of Waikato, Hamilton, NZ}, author = {E. T. Frank and X. Xu}, institution = {University of Waikato}, month = {06}, title = {Applying propositional learning algorithms to multi-instance data}, year = {2003} }
Valid options are:-P [1|2|3] The method used in testing: 1.arithmetic average 2.geometric average 3.max probability of positive bag. (default: 1)
-A [0|1|2|3] The type of weight setting for each single-instance: 0.keep the weight to be the same as the original value; 1.weight = 1.0 2.weight = 1.0/Total number of single-instance in the corresponding bag 3. weight = Total number of single-instance / (Total number of bags * Total number of single-instance in the corresponding bag). (default: 3)
-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.rules.ZeroR)
Options specific to classifier weka.classifiers.rules.ZeroR:
-D If set, classifier is run in debug mode and may output additional info to the console
- Version:
- $Revision: 9144 $
- Author:
- Eibe Frank (eibe@cs.waikato.ac.nz), Xin Xu (xx5@cs.waikato.ac.nz)
- See Also:
- Serialized Form
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Field Summary
Fields Modifier and Type Field Description static Tag[]
TAGS_TESTMETHOD
the test methodsstatic int
TESTMETHOD_ARITHMETIC
arithmetic averagestatic int
TESTMETHOD_GEOMETRIC
geometric averagestatic int
TESTMETHOD_MAXPROB
max probability of positive bag
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Constructor Summary
Constructors Constructor Description MIWrapper()
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
buildClassifier(Instances data)
Builds the classifierdouble[]
distributionForInstance(Instance exmp)
Computes the distribution for a given exemplarCapabilities
getCapabilities()
Returns default capabilities of the classifier.SelectedTag
getMethod()
Get the method used in testing.Capabilities
getMultiInstanceCapabilities()
Returns the capabilities of this multi-instance classifier for the relational data.java.lang.String[]
getOptions()
Gets the current settings of the Classifier.java.lang.String
getRevision()
Returns the revision string.TechnicalInformation
getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.SelectedTag
getWeightMethod()
Returns the current weighting method for instances.java.lang.String
globalInfo()
Returns a string describing this filterjava.util.Enumeration
listOptions()
Returns an enumeration describing the available options.static void
main(java.lang.String[] argv)
Main method for testing this class.java.lang.String
methodTipText()
Returns the tip text for this propertyvoid
setMethod(SelectedTag method)
Set the method used in testing.void
setOptions(java.lang.String[] options)
Parses a given list of options.void
setWeightMethod(SelectedTag method)
The new method for weighting the instances.java.lang.String
toString()
Gets a string describing the classifier.java.lang.String
weightMethodTipText()
Returns the tip text for this property-
Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, setClassifier
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Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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Field Detail
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TESTMETHOD_ARITHMETIC
public static final int TESTMETHOD_ARITHMETIC
arithmetic average- See Also:
- Constant Field Values
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TESTMETHOD_GEOMETRIC
public static final int TESTMETHOD_GEOMETRIC
geometric average- See Also:
- Constant Field Values
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TESTMETHOD_MAXPROB
public static final int TESTMETHOD_MAXPROB
max probability of positive bag- See Also:
- Constant Field Values
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TAGS_TESTMETHOD
public static final Tag[] TAGS_TESTMETHOD
the test methods
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Method Detail
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globalInfo
public java.lang.String globalInfo()
Returns a string describing this filter- Returns:
- a description of the filter suitable for displaying in the explorer/experimenter gui
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getTechnicalInformation
public TechnicalInformation getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.- Specified by:
getTechnicalInformation
in interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classSingleClassifierEnhancer
- Returns:
- an enumeration of all the available options.
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setOptions
public void setOptions(java.lang.String[] options) throws java.lang.Exception
Parses a given list of options. Valid options are:-P [1|2|3] The method used in testing: 1.arithmetic average 2.geometric average 3.max probability of positive bag. (default: 1)
-A [0|1|2|3] The type of weight setting for each single-instance: 0.keep the weight to be the same as the original value; 1.weight = 1.0 2.weight = 1.0/Total number of single-instance in the corresponding bag 3. weight = Total number of single-instance / (Total number of bags * Total number of single-instance in the corresponding bag). (default: 3)
-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.rules.ZeroR)
Options specific to classifier weka.classifiers.rules.ZeroR:
-D If set, classifier is run in debug mode and may output additional info to the console
- Specified by:
setOptions
in interfaceOptionHandler
- Overrides:
setOptions
in classSingleClassifierEnhancer
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of the Classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classSingleClassifierEnhancer
- Returns:
- an array of strings suitable for passing to setOptions
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weightMethodTipText
public java.lang.String weightMethodTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setWeightMethod
public void setWeightMethod(SelectedTag method)
The new method for weighting the instances.- Parameters:
method
- the new method
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getWeightMethod
public SelectedTag getWeightMethod()
Returns the current weighting method for instances.- Returns:
- the current weighting method
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methodTipText
public java.lang.String methodTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setMethod
public void setMethod(SelectedTag method)
Set the method used in testing.- Parameters:
method
- the index of method to use.
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getMethod
public SelectedTag getMethod()
Get the method used in testing.- Returns:
- the index of method used in testing.
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getCapabilities
public Capabilities getCapabilities()
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classSingleClassifierEnhancer
- Returns:
- the capabilities of this classifier
- See Also:
Capabilities
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getMultiInstanceCapabilities
public Capabilities getMultiInstanceCapabilities()
Returns the capabilities of this multi-instance classifier for the relational data.- Specified by:
getMultiInstanceCapabilities
in interfaceMultiInstanceCapabilitiesHandler
- Returns:
- the capabilities of this object
- See Also:
Capabilities
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buildClassifier
public void buildClassifier(Instances data) throws java.lang.Exception
Builds the classifier- Specified by:
buildClassifier
in classClassifier
- Parameters:
data
- the training data to be used for generating the boosted classifier.- Throws:
java.lang.Exception
- if the classifier could not be built successfully
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distributionForInstance
public double[] distributionForInstance(Instance exmp) throws java.lang.Exception
Computes the distribution for a given exemplar- Overrides:
distributionForInstance
in classClassifier
- Parameters:
exmp
- the exemplar for which distribution is computed- Returns:
- the distribution
- Throws:
java.lang.Exception
- if the distribution can't be computed successfully
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toString
public java.lang.String toString()
Gets a string describing the classifier.- Overrides:
toString
in classjava.lang.Object
- Returns:
- a string describing the classifer built.
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
public static void main(java.lang.String[] argv)
Main method for testing this class.- Parameters:
argv
- should contain the command line arguments to the scheme (see Evaluation)
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