weka.classifiers.meta

Class RegressionByDiscretization

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.meta.RegressionByDiscretization

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, ConditionalDensityEstimator, IntervalEstimator, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler

public class RegressionByDiscretization
extends SingleClassifierEnhancer
implements IntervalEstimator, ConditionalDensityEstimator

A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized. The predicted value is the expected value of the mean class value for each discretized interval (based on the predicted probabilities for each interval).

Valid options are:

  -B <int>
  Number of bins for equal-width discretization
  (default 10).
 

  -E
  Whether to delete empty bins after discretization
  (default false).
 

  -F
  Use equal-frequency instead of equal-width discretization.
 

  -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 int[]	m_ClassCounts The class counts for each Discretized class interval.
protected double[]	m_ClassMeans The mean values for each Discretized class interval.
protected boolean	m_DeleteEmptyBins Whether to delete empty intervals.
protected Instances	m_DiscretizedHeader Header of discretized data.
protected Discretize	m_Discretizer The discretization filter.
protected UnivariateDensityEstimator	m_Estimator Which estimator to use (default: histogram)
protected boolean	m_MinimizeAbsoluteError Whether to minimize absolute error, rather than squared error.
protected int[]	m_NewTargetValues The converted target values in the training data
protected int	m_NumBins The number of discretization intervals.
protected int[]	m_OldIndexToNewIndex Mapping to convert indices in case empty bins are deleted.
protected double[]	m_OriginalTargetValues The original target values in the training data
protected boolean	m_UseEqualFrequency Use equal-frequency binning

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

Constructor Summary

Constructors

Constructor and Description
RegressionByDiscretization() Default constructor.

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances instances) Generates the classifier.
double	classifyInstance(Instance instance) Returns a predicted class for the test instance.
protected java.lang.String	defaultClassifierString() String describing default classifier.
java.lang.String	deleteEmptyBinsTipText() Returns the tip text for this property
java.lang.String	estimatorTipText() Returns the tip text for this property
Capabilities	getCapabilities() Returns default capabilities of the classifier.
boolean	getDeleteEmptyBins() Gets whether empty bins are deleted.
protected UnivariateDensityEstimator	getDensityEstimator(Instance instance, boolean print) Get density estimator for given instance.
UnivariateDensityEstimator	getEstimator() Get the estimator
boolean	getMinimizeAbsoluteError() Gets whether to min. abs. error
int	getNumBins() Gets the number of bins numeric attributes will be divided into
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.
boolean	getUseEqualFrequency() Get the value of UseEqualFrequency.
java.lang.String	globalInfo() Returns a string describing classifier
java.util.Enumeration<Option>	listOptions() Returns an enumeration describing the available options.
double	logDensity(Instance instance, double value) Returns natural logarithm of density estimate for given value based on given instance.
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	minimizeAbsoluteErrorTipText() Returns the tip text for this property
java.lang.String	numBinsTipText() Returns the tip text for this property
double[][]	predictIntervals(Instance instance, double confidenceLevel) Returns an N * 2 array, where N is the number of prediction intervals.
void	setDeleteEmptyBins(boolean b) Sets whether to delete empty bins.
void	setEstimator(UnivariateDensityEstimator estimator) Set the estimator
void	setMinimizeAbsoluteError(boolean b) Sets whether to min. abs. error.
void	setNumBins(int numBins) Sets the number of bins to divide each selected numeric attribute into
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setUseEqualFrequency(boolean newUseEqualFrequency) Set the value of UseEqualFrequency.
java.lang.String	toString() Returns a description of the classifier.
java.lang.String	useEqualFrequencyTipText() Returns the tip text for this property

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, defaultClassifierOptions, getClassifier, getClassifierSpec, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_Discretizer

protected Discretize m_Discretizer

The discretization filter.

m_NumBins

protected int m_NumBins

The number of discretization intervals.

m_ClassMeans

protected double[] m_ClassMeans

The mean values for each Discretized class interval.

m_ClassCounts

protected int[] m_ClassCounts

The class counts for each Discretized class interval.

m_DeleteEmptyBins

protected boolean m_DeleteEmptyBins

Whether to delete empty intervals.

m_OldIndexToNewIndex

protected int[] m_OldIndexToNewIndex

Mapping to convert indices in case empty bins are deleted.

m_DiscretizedHeader

protected Instances m_DiscretizedHeader

Header of discretized data.

m_UseEqualFrequency

protected boolean m_UseEqualFrequency

Use equal-frequency binning

m_MinimizeAbsoluteError

protected boolean m_MinimizeAbsoluteError

Whether to minimize absolute error, rather than squared error.

m_Estimator

protected UnivariateDensityEstimator m_Estimator

Which estimator to use (default: histogram)

m_OriginalTargetValues

protected double[] m_OriginalTargetValues

The original target values in the training data

m_NewTargetValues

protected int[] m_NewTargetValues

The converted target values in the training data

Constructor Detail

RegressionByDiscretization

public RegressionByDiscretization()

Default constructor.

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

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.

Returns:

the technical information about this class

defaultClassifierString

protected java.lang.String defaultClassifierString()

String describing default classifier.

Overrides:

defaultClassifierString in class SingleClassifierEnhancer

Returns:

the default classifier classname

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 instances)
                     throws java.lang.Exception

Generates the classifier.

Specified by:

buildClassifier in interface Classifier

Parameters:

instances - set of instances serving as training data

Throws:

java.lang.Exception - if the classifier has not been generated successfully

getDensityEstimator

protected UnivariateDensityEstimator getDensityEstimator(Instance instance,
                                                         boolean print)
                                                  throws java.lang.Exception

Get density estimator for given instance.

Parameters:

inst - the instance

Returns:

the univariate density estimator

Throws:

java.lang.Exception - if the estimator can't be computed

predictIntervals

public double[][] predictIntervals(Instance instance,
                                   double confidenceLevel)
                            throws java.lang.Exception

Returns an N * 2 array, where N is the number of prediction intervals. In each row, the first element contains the lower boundary of the corresponding prediction interval and the second element the upper boundary.

Specified by:

predictIntervals in interface IntervalEstimator

Parameters:

inst - the instance to make the prediction for.

confidenceLevel - the percentage of cases that the interval should cover.

Returns:

an array of prediction intervals

Throws:

java.lang.Exception - if the intervals can't be computed

logDensity

public double logDensity(Instance instance,
                         double value)
                  throws java.lang.Exception

Returns natural logarithm of density estimate for given value based on given instance.

Specified by:

logDensity in interface ConditionalDensityEstimator

Parameters:

inst - the instance to make the prediction for.

the - value to make the prediction for.

Returns:

the natural logarithm of the density estimate

Throws:

java.lang.Exception - if the intervals can't be computed

classifyInstance

public double classifyInstance(Instance instance)
                        throws java.lang.Exception

Returns a predicted class for the test instance.

Specified by:

classifyInstance in interface Classifier

Overrides:

classifyInstance in class AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

predicted class value

Throws:

java.lang.Exception - if the prediction couldn't be made

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.

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

numBinsTipText

public java.lang.String numBinsTipText()

Returns the tip text for this property

Returns:

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

getNumBins

public int getNumBins()

Gets the number of bins numeric attributes will be divided into

Returns:

the number of bins.

setNumBins

public void setNumBins(int numBins)

Sets the number of bins to divide each selected numeric attribute into

Parameters:

numBins - the number of bins

deleteEmptyBinsTipText

public java.lang.String deleteEmptyBinsTipText()

Returns the tip text for this property

Returns:

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

getDeleteEmptyBins

public boolean getDeleteEmptyBins()

Gets whether empty bins are deleted.

Returns:

true if empty bins get deleted.

setDeleteEmptyBins

public void setDeleteEmptyBins(boolean b)

Sets whether to delete empty bins.

Parameters:

b - if true, empty bins will be deleted

minimizeAbsoluteErrorTipText

public java.lang.String minimizeAbsoluteErrorTipText()

Returns the tip text for this property

Returns:

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

getMinimizeAbsoluteError

public boolean getMinimizeAbsoluteError()

Gets whether to min. abs. error

Returns:

true if abs. err. is to be minimized

setMinimizeAbsoluteError

public void setMinimizeAbsoluteError(boolean b)

Sets whether to min. abs. error.

Parameters:

b - if true, abs. err. is minimized

useEqualFrequencyTipText

public java.lang.String useEqualFrequencyTipText()

Returns the tip text for this property

Returns:

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

getUseEqualFrequency

public boolean getUseEqualFrequency()

Get the value of UseEqualFrequency.

Returns:

Value of UseEqualFrequency.

setUseEqualFrequency

public void setUseEqualFrequency(boolean newUseEqualFrequency)

Set the value of UseEqualFrequency.

Parameters:

newUseEqualFrequency - Value to assign to UseEqualFrequency.

estimatorTipText

public java.lang.String estimatorTipText()

Returns the tip text for this property

Returns:

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

getEstimator

public UnivariateDensityEstimator getEstimator()

Get the estimator

Returns:

the estimator

setEstimator

public void setEstimator(UnivariateDensityEstimator estimator)

Set the estimator

Parameters:

newEstimator - the estimator to use

toString

public java.lang.String toString()

Returns a description of the classifier.

Overrides:

toString in class java.lang.Object

Returns:

a description of the classifier as a string.

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 - the options