weka.filters.supervised.attribute
Class PLSFilter

java.lang.Object
  weka.filters.Filter
      weka.filters.SimpleFilter
          weka.filters.SimpleBatchFilter
              weka.filters.supervised.attribute.PLSFilter

All Implemented Interfaces:

Serializable, weka.core.CapabilitiesHandler, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler, weka.filters.SupervisedFilter

public class PLSFilter
extends weka.filters.SimpleBatchFilter
implements weka.filters.SupervisedFilter, weka.core.TechnicalInformationHandler

Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction.
By default it replaces missing values and centers the data.

For more information see:

Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002). A User Friendly Guide to Multivariate Calibration and Classification. NIR Publications.

StatSoft, Inc.. Partial Least Squares (PLS).

Bent Jorgensen, Yuri Goegebeur. Module 7: Partial least squares regression I.

S. de Jong (1993). SIMPLS: an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems. 18:251-263.

BibTeX:

 @book{Naes2002,
    author = {Tormod Naes and Tomas Isaksson and Tom Fearn and Tony Davies},
    publisher = {NIR Publications},
    title = {A User Friendly Guide to Multivariate Calibration and Classification},
    year = {2002},
    ISBN = {0-9528666-2-5}
 }
 
 @misc{missing_id,
    author = {StatSoft, Inc.},
    booktitle = {Electronic Textbook StatSoft},
    title = {Partial Least Squares (PLS)},
    HTTP = {http://www.statsoft.com/textbook/stpls.html}
 }
 
 @misc{missing_id,
    author = {Bent Jorgensen and Yuri Goegebeur},
    booktitle = {ST02: Multivariate Data Analysis and Chemometrics},
    title = {Module 7: Partial least squares regression I},
    HTTP = {http://statmaster.sdu.dk/courses/ST02/module07/}
 }
 
 @article{Jong1993,
    author = {S. de Jong},
    journal = {Chemometrics and Intelligent Laboratory Systems},
    pages = {251-263},
    title = {SIMPLS: an alternative approach to partial least squares regression},
    volume = {18},
    year = {1993}
 }
 

Valid options are:

 -D
  Turns on output of debugging information.

 -C <num>
  The number of components to compute.
  (default: 20)

 -U
  Updates the class attribute as well.
  (default: off)

 -M
  Turns replacing of missing values on.
  (default: off)

 -A <SIMPLS|PLS1>
  The algorithm to use.
  (default: PLS1)

 -P <none|center|standardize>
  The type of preprocessing that is applied to the data.
  (default: center)

Version:

$Revision: 8109 $

Author:

FracPete (fracpete at waikato dot ac dot nz)

See Also:

Serialized Form

Field Summary

static int	ALGORITHM_PLS1 the type of algorithm: PLS1
static int	ALGORITHM_SIMPLS the type of algorithm: SIMPLS
static int	PREPROCESSING_CENTER the type of preprocessing: Center
static int	PREPROCESSING_NONE the type of preprocessing: None
static int	PREPROCESSING_STANDARDIZE the type of preprocessing: Standardize
static weka.core.Tag[]	TAGS_ALGORITHM the types of algorithm
static weka.core.Tag[]	TAGS_PREPROCESSING the types of preprocessing

Constructor Summary

PLSFilter()
default constructor

Method Summary

String	algorithmTipText() Returns the tip text for this property
weka.core.SelectedTag	getAlgorithm() Gets the type of algorithm to use
weka.core.Capabilities	getCapabilities() Returns the Capabilities of this filter.
int	getNumComponents() returns the maximum number of attributes to use.
String[]	getOptions() returns the options of the current setup
boolean	getPerformPrediction() Gets whether the class attribute is updated with the predicted value.
weka.core.SelectedTag	getPreprocessing() Gets the type of preprocessing to use
boolean	getReplaceMissing() Gets whether missing values are replace.
String	getRevision() Returns the revision string.
weka.core.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.
String	globalInfo() Returns a string describing this classifier.
Enumeration	listOptions() Gets an enumeration describing the available options.
static void	main(String[] args) runs the filter with the given arguments.
String	numComponentsTipText() Returns the tip text for this property
String	performPredictionTipText() Returns the tip text for this property
String	preprocessingTipText() Returns the tip text for this property
String	replaceMissingTipText() Returns the tip text for this property
void	setAlgorithm(weka.core.SelectedTag value) Sets the type of algorithm to use
void	setNumComponents(int value) sets the maximum number of attributes to use.
void	setOptions(String[] options) Parses the options for this object.
void	setPerformPrediction(boolean value) Sets whether to update the class attribute with the predicted value.
void	setPreprocessing(weka.core.SelectedTag value) Sets the type of preprocessing to use
void	setReplaceMissing(boolean value) Sets whether to replace missing values.

Methods inherited from class weka.filters.SimpleBatchFilter

batchFinished, input

Methods inherited from class weka.filters.SimpleFilter

debugTipText, getDebug, setDebug, setInputFormat

Methods inherited from class weka.filters.Filter

batchFilterFile, filterFile, getCapabilities, getOutputFormat, isFirstBatchDone, isNewBatch, isOutputFormatDefined, makeCopies, makeCopy, mayRemoveInstanceAfterFirstBatchDone, numPendingOutput, output, outputPeek, runFilter, toString, useFilter, wekaStaticWrapper

Methods inherited from class java.lang.Object

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

Field Detail

ALGORITHM_SIMPLS

public static final int ALGORITHM_SIMPLS

the type of algorithm: SIMPLS

See Also:

Constant Field Values

ALGORITHM_PLS1

public static final int ALGORITHM_PLS1

the type of algorithm: PLS1

See Also:

Constant Field Values

TAGS_ALGORITHM

public static final weka.core.Tag[] TAGS_ALGORITHM

the types of algorithm

PREPROCESSING_NONE

public static final int PREPROCESSING_NONE

the type of preprocessing: None

See Also:

Constant Field Values

PREPROCESSING_CENTER

public static final int PREPROCESSING_CENTER

the type of preprocessing: Center

See Also:

Constant Field Values

PREPROCESSING_STANDARDIZE

public static final int PREPROCESSING_STANDARDIZE

the type of preprocessing: Standardize

See Also:

Constant Field Values

TAGS_PREPROCESSING

public static final weka.core.Tag[] TAGS_PREPROCESSING

the types of preprocessing

Constructor Detail

PLSFilter

public PLSFilter()

default constructor

Method Detail

globalInfo

public String globalInfo()

Returns a string describing this classifier.

Specified by:

globalInfo in class weka.filters.SimpleFilter

Returns:

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

getTechnicalInformation

public weka.core.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 weka.core.TechnicalInformationHandler

Returns:

the technical information about this class

listOptions

public Enumeration listOptions()

Gets an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.filters.SimpleFilter

Returns:

an enumeration of all the available options.

getOptions

public String[] getOptions()

returns the options of the current setup

Specified by:

getOptions in interface weka.core.OptionHandler

Overrides:

getOptions in class weka.filters.SimpleFilter

Returns:

the current options

setOptions

public void setOptions(String[] options)
                throws Exception

Parses the options for this object.

Valid options are:

 -D
  Turns on output of debugging information.

 -C <num>
  The number of components to compute.
  (default: 20)

 -U
  Updates the class attribute as well.
  (default: off)

 -M
  Turns replacing of missing values on.
  (default: off)

 -A <SIMPLS|PLS1>
  The algorithm to use.
  (default: PLS1)

 -P <none|center|standardize>
  The type of preprocessing that is applied to the data.
  (default: center)

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.filters.SimpleFilter

Parameters:

options - the options to use

Throws:

Exception - if the option setting fails

numComponentsTipText

public String numComponentsTipText()

Returns the tip text for this property

Returns:

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

setNumComponents

public void setNumComponents(int value)

sets the maximum number of attributes to use.

Parameters:

value - the maximum number of attributes

getNumComponents

public int getNumComponents()

returns the maximum number of attributes to use.

Returns:

the current maximum number of attributes

performPredictionTipText

public String performPredictionTipText()

Returns the tip text for this property

Returns:

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

setPerformPrediction

public void setPerformPrediction(boolean value)

Sets whether to update the class attribute with the predicted value.

Parameters:

value - if true the class value will be replaced by the predicted value.

getPerformPrediction

public boolean getPerformPrediction()

Gets whether the class attribute is updated with the predicted value.

Returns:

true if the class attribute is updated

algorithmTipText

public String algorithmTipText()

Returns the tip text for this property

Returns:

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

setAlgorithm

public void setAlgorithm(weka.core.SelectedTag value)

Sets the type of algorithm to use

Parameters:

value - the algorithm type

getAlgorithm

public weka.core.SelectedTag getAlgorithm()

Gets the type of algorithm to use

Returns:

the current algorithm type.

replaceMissingTipText

public String replaceMissingTipText()

Returns the tip text for this property

Returns:

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

setReplaceMissing

public void setReplaceMissing(boolean value)

Sets whether to replace missing values.

Parameters:

value - if true missing values are replaced with the ReplaceMissingValues filter.

getReplaceMissing

public boolean getReplaceMissing()

Gets whether missing values are replace.

Returns:

true if missing values are replaced with the ReplaceMissingValues filter

preprocessingTipText

public String preprocessingTipText()

Returns the tip text for this property

Returns:

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

setPreprocessing

public void setPreprocessing(weka.core.SelectedTag value)

Sets the type of preprocessing to use

Parameters:

value - the preprocessing type

getPreprocessing

public weka.core.SelectedTag getPreprocessing()

Gets the type of preprocessing to use

Returns:

the current preprocessing type.

getCapabilities

public weka.core.Capabilities getCapabilities()

Returns the Capabilities of this filter.

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.filters.Filter

Returns:

the capabilities of this object

See Also:

Capabilities

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.filters.Filter

Returns:

the revision

main

public static void main(String[] args)

runs the filter with the given arguments.

Parameters:

args - the commandline arguments