weka.classifiers.functions
Class PaceRegression

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.functions.PaceRegression

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable, WeightedInstancesHandler

public class PaceRegression

extends Classifier

implements OptionHandler, WeightedInstancesHandler

Class for building pace regression linear models and using them for prediction.

Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions.

The current work of the pace regression theory, and therefore also this implementation, do not handle:

- missing values
- non-binary nominal attributes
- the case that n - k is small where n is number of instances and k is number of coefficients (the threshold used in this implmentation is 20)

Valid options are:

-D
Produce debugging output.

-E estimator
The estimator can be one of the following:

• eb -- Empirical Bayes estimator for noraml mixture (default)
  
• nested -- Optimal nested model selector for normal mixture
  
• subset -- Optimal subset selector for normal mixture
  
• pace2 -- PACE2 for Chi-square mixture
  
• pace4 -- PACE4 for Chi-square mixture
  
• pace6 -- PACE6 for Chi-square mixture
  
• ols -- Ordinary least squares estimator
  
• aic -- AIC estimator
  
• bic -- BIC estimator
  
• ric -- RIC estimator
  
• olsc -- Ordinary least squares subset selector with a threshold
  

-S Threshold for the olsc estimator

REFERENCES

Wang, Y. (2000). "A new approach to fitting linear models in high dimensional spaces." PhD Thesis. Department of Computer Science, University of Waikato, New Zealand.

Wang, Y. and Witten, I. H. (2002). "Modeling for optimal probability prediction." Proceedings of ICML'2002. Sydney.

Version:

$Revision: 1.1 $

Author:

Yong Wang (yongwang@cs.waikato.ac.nz), Gabi Schmidberger (gabi@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

static Tag[]

TAGS_ESTIMATOR

Constructor Summary

PaceRegression()

Method Summary

void	buildClassifier(Instances data) Builds a pace regression model for the given data.
boolean	checkForMissing(Instance instance, Instances model) Checks if an instance has a missing value.
boolean	checkForMissing(Instances data) Checks if instances have a missing value.
boolean	checkForNonBinary(Instances data) Checks if any of the nominal attributes is non-binary.
double	classifyInstance(Instance instance) Classifies the given instance using the linear regression function.
double[]	coefficients() Returns the coefficients for this linear model.
java.lang.String	debugTipText() Returns the tip text for this property
java.lang.String	estimatorTipText() Returns the tip text for this property
boolean	getDebug() Controls whether debugging output will be printed
SelectedTag	getEstimator() Gets the estimator
java.lang.String[]	getOptions() Gets the current settings of the classifier.
double	getThreshold() Gets the threshold for olsc estimator
java.lang.String	globalInfo() Returns a string describing this classifier
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Generates a linear regression function predictor.
int	numParameters() Get the number of coefficients used in the model
void	setDebug(boolean debug) Controls whether debugging output will be printed
void	setEstimator(SelectedTag estimator) Sets the estimator.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setThreshold(double newThreshold) Set threshold for the olsc estimator
java.lang.String	thresholdTipText() Returns the tip text for this property
java.lang.String	toString() Outputs the linear regression model as a string.

Methods inherited from class weka.classifiers.Classifier

distributionForInstance, forName, makeCopies

Methods inherited from class java.lang.Object

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

Field Detail

TAGS_ESTIMATOR

public static final Tag[] TAGS_ESTIMATOR

Constructor Detail

PaceRegression

public PaceRegression()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this classifier

Returns:

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

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception

Builds a pace regression model for the given data.

Specified by:

buildClassifier in class Classifier

Parameters:

data - the training data to be used for generating the linear regression function

Throws:

java.lang.Exception - if the classifier could not be built successfully

checkForMissing

public boolean checkForMissing(Instances data)

Checks if instances have a missing value.

Parameters:

data - the data set

Returns:

true if missing value is present in data set

checkForMissing

public boolean checkForMissing(Instance instance,
                               Instances model)

Checks if an instance has a missing value.

Parameters:

instance - the instance

Returns:

true if missing value is present

checkForNonBinary

public boolean checkForNonBinary(Instances data)

Checks if any of the nominal attributes is non-binary.

Parameters:

data - the data set

Returns:

true if non binary attribute is present

classifyInstance

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

Classifies the given instance using the linear regression function.

Overrides:

classifyInstance in class Classifier

Parameters:

instance - the test instance

Returns:

the classification

Throws:

java.lang.Exception - if classification can't be done successfully

toString

public java.lang.String toString()

Outputs the linear regression model as a string.

Overrides:

toString in class java.lang.Object

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class Classifier

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 Classifier

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

coefficients

public double[] coefficients()

Returns the coefficients for this linear model.

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class Classifier

Returns:

an array of strings suitable for passing to setOptions

numParameters

public int numParameters()

Get the number of coefficients used in the model

Returns:

the number of coefficients

debugTipText

public java.lang.String debugTipText()

Returns the tip text for this property

Overrides:

debugTipText in class Classifier

Returns:

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

setDebug

public void setDebug(boolean debug)

Controls whether debugging output will be printed

Overrides:

setDebug in class Classifier

Parameters:

debug - true if debugging output should be printed

getDebug

public boolean getDebug()

Controls whether debugging output will be printed

Overrides:

getDebug in class Classifier

Returns:

true if debugging output is on

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 SelectedTag getEstimator()

Gets the estimator

Returns:

the estimator

setEstimator

public void setEstimator(SelectedTag estimator)

Sets the estimator.

Parameters:

estimator - the new estimator

thresholdTipText

public java.lang.String thresholdTipText()

Returns the tip text for this property

Returns:

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

setThreshold

public void setThreshold(double newThreshold)

Set threshold for the olsc estimator

getThreshold

public double getThreshold()

Gets the threshold for olsc estimator

Returns:

the threshold

main

public static void main(java.lang.String[] argv)

Generates a linear regression function predictor.