PaceRegression

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

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class PaceRegression
extends Classifier
implements OptionHandler, WeightedInstancesHandler, TechnicalInformationHandler

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 the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20)

For more information see:

Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand.

Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002.

BibTeX:

 @phdthesis{Wang2000,
    address = {Hamilton, New Zealand},
    author = {Wang, Y},
    school = {Department of Computer Science, University of Waikato},
    title = {A new approach to fitting linear models in high dimensional spaces},
    year = {2000}
 }
 
 @inproceedings{Wang2002,
    address = {Sydney, Australia},
    author = {Wang, Y. and Witten, I. H.},
    booktitle = {Proceedings of the Nineteenth International Conference in Machine Learning},
    pages = {650-657},
    title = {Modeling for optimal probability prediction},
    year = {2002}
 }

Valid options are:

 -D
  Produce debugging output.
  (default no 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 value>
  Threshold value for the OLSC estimator

Version:: $Revision: 5523 $
Author:: Yong Wang (yongwang@cs.waikato.ac.nz), Gabi Schmidberger (gabi@cs.waikato.ac.nz)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type Field and Description

static Tag[] TAGS_ESTIMATOR
estimator types

Fields
Modifier and Type	Field and Description
`static Tag[]`	`TAGS_ESTIMATOR` estimator types

Constructor Summary

Constructors
Constructor and Description

PaceRegression()

Constructors
Constructor and Description
`PaceRegression()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`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.
`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
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`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.
`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.
`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, makeCopy

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
```
    estimator types
- 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
  - 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 interface TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class Classifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - 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(Instance instance,
                               Instances model)
```
    Checks if an instance has a missing value.
    
    Parameters:
    
    instance - the instance
    
    model - the data
    
    Returns:
    
    true if missing value 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
    
    Returns:
    
    the model as string
  - 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.
    Valid options are:
```
 -D
  Produce debugging output.
  (default no 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 value>
  Threshold value for the OLSC estimator
```
    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.
    
    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 should be printed
  - 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
    
    Parameters:
    
    newThreshold - the threshold for the olsc estimator
  - getThreshold
```
public double getThreshold()
```
    Gets the threshold for olsc estimator
    
    Returns:
    
    the threshold
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class Classifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] argv)
```
    Generates a linear regression function predictor.
    
    Parameters:
    
    argv - the options

Class PaceRegression

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

TAGS_ESTIMATOR

Constructor Detail

PaceRegression

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

buildClassifier

checkForMissing

classifyInstance

toString

listOptions

setOptions

coefficients

getOptions

numParameters

debugTipText

setDebug

getDebug

estimatorTipText

getEstimator

setEstimator

thresholdTipText

setThreshold

getThreshold

getRevision

main