weka.experiment

Class CostSensitiveClassifierSplitEvaluator

java.lang.Object

weka.experiment.ClassifierSplitEvaluator

weka.experiment.CostSensitiveClassifierSplitEvaluator

All Implemented Interfaces:

java.io.Serializable, AdditionalMeasureProducer, OptionHandler, RevisionHandler, SplitEvaluator

public class CostSensitiveClassifierSplitEvaluator
extends ClassifierSplitEvaluator

SplitEvaluator that produces results for a classification scheme on a nominal class attribute, including weighted misclassification costs.

Valid options are:

 -W <class name>
  The full class name of the classifier.
  eg: weka.classifiers.bayes.NaiveBayes

 -C <index>
  The index of the class for which IR statistics
  are to be output. (default 1)

 -I <index>
  The index of an attribute to output in the
  results. This attribute should identify an
  instance in order to know which instances are
  in the test set of a cross validation. if 0
  no output (default 0).

 -P
  Add target and prediction columns to the result
  for each fold.

 
 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

 -D <directory>
  Name of a directory to search for cost files when loading
  costs on demand (default current directory).

All options after -- will be passed to the classifier.

Version:

$Revision: 7516 $

Author:

Len Trigg (len@reeltwo.com)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
CostSensitiveClassifierSplitEvaluator()

Method Summary

Modifier and Type	Method and Description
java.io.File	getOnDemandDirectory() Returns the directory that will be searched for cost files when loading on demand.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
java.lang.Object[]	getResult(Instances train, Instances test) Gets the results for the supplied train and test datasets.
java.lang.String[]	getResultNames() Gets the names of each of the result columns produced for a single run.
java.lang.Object[]	getResultTypes() Gets the data types of each of the result columns produced for a single run.
java.lang.String	getRevision() Returns the revision string.
java.lang.String	globalInfo() Returns a string describing this split evaluator
java.util.Enumeration	listOptions() Returns an enumeration describing the available options..
java.lang.String	onDemandDirectoryTipText() Returns the tip text for this property
void	setOnDemandDirectory(java.io.File newDir) Sets the directory that will be searched for cost files when loading on demand.
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Returns a text description of the split evaluator.

Methods inherited from class weka.experiment.ClassifierSplitEvaluator

classifierTipText, enumerateMeasures, getAttributeID, getClassForIRStatistics, getClassifier, getKey, getKeyNames, getKeyTypes, getMeasure, getPredTargetColumn, getRawResultOutput, setAdditionalMeasures, setAttributeID, setClassForIRStatistics, setClassifier, setClassifierName, setPredTargetColumn

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

CostSensitiveClassifierSplitEvaluator

public CostSensitiveClassifierSplitEvaluator()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this split evaluator

Overrides:

globalInfo in class ClassifierSplitEvaluator

Returns:

a description of the split evaluator suitable for displaying in the explorer/experimenter gui

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options..

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class ClassifierSplitEvaluator

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:

 -W <class name>
  The full class name of the classifier.
  eg: weka.classifiers.bayes.NaiveBayes

 -C <index>
  The index of the class for which IR statistics
  are to be output. (default 1)

 -I <index>
  The index of an attribute to output in the
  results. This attribute should identify an
  instance in order to know which instances are
  in the test set of a cross validation. if 0
  no output (default 0).

 -P
  Add target and prediction columns to the result
  for each fold.

 
 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

 -D <directory>
  Name of a directory to search for cost files when loading
  costs on demand (default current directory).

All options after -- will be passed to the classifier.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class ClassifierSplitEvaluator

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 ClassifierSplitEvaluator

Returns:

an array of strings suitable for passing to setOptions

onDemandDirectoryTipText

public java.lang.String onDemandDirectoryTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getOnDemandDirectory

public java.io.File getOnDemandDirectory()

Returns the directory that will be searched for cost files when loading on demand.

Returns:

The cost file search directory.

setOnDemandDirectory

public void setOnDemandDirectory(java.io.File newDir)

Sets the directory that will be searched for cost files when loading on demand.

Parameters:

newDir - The cost file search directory.

getResultTypes

public java.lang.Object[] getResultTypes()

Gets the data types of each of the result columns produced for a single run. The number of result fields must be constant for a given SplitEvaluator.

Specified by:

getResultTypes in interface SplitEvaluator

Overrides:

getResultTypes in class ClassifierSplitEvaluator

Returns:

an array containing objects of the type of each result column. The objects should be Strings, or Doubles.

getResultNames

public java.lang.String[] getResultNames()

Gets the names of each of the result columns produced for a single run. The number of result fields must be constant for a given SplitEvaluator.

Specified by:

getResultNames in interface SplitEvaluator

Overrides:

getResultNames in class ClassifierSplitEvaluator

Returns:

an array containing the name of each result column

getResult

public java.lang.Object[] getResult(Instances train,
                                    Instances test)
                             throws java.lang.Exception

Gets the results for the supplied train and test datasets. Now performs a deep copy of the classifier before it is built and evaluated (just in case the classifier is not initialized properly in buildClassifier()).

Specified by:

getResult in interface SplitEvaluator

Overrides:

getResult in class ClassifierSplitEvaluator

Parameters:

train - the training Instances.

test - the testing Instances.

Returns:

the results stored in an array. The objects stored in the array may be Strings, Doubles, or null (for the missing value).

Throws:

java.lang.Exception - if a problem occurs while getting the results

toString

public java.lang.String toString()

Returns a text description of the split evaluator.

Overrides:

toString in class ClassifierSplitEvaluator

Returns:

a text description of the split evaluator.

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class ClassifierSplitEvaluator

Returns:

the revision