PLSFilter (weka-stable 3.6.8 API)

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

All Implemented Interfaces:: Serializable, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, SupervisedFilter

public class PLSFilter
extends SimpleBatchFilter
implements SupervisedFilter, 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: 5541 $
Author:: FracPete (fracpete at waikato dot ac dot nz)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`ALGORITHM_PLS1` the type of algorithm: PLS1
`static int`	`ALGORITHM_SIMPLS` the type of algorithm: SIMPLS
`protected int`	`m_Algorithm` the type of algorithm
`protected double`	`m_ClassMean` the mean of the class
`protected double`	`m_ClassStdDev` the standard deviation of the class
`protected Filter`	`m_Filter` for centering the data
`protected Filter`	`m_Missing` for replacing missing values
`protected int`	`m_NumComponents` the maximum number of components to generate
`protected boolean`	`m_PerformPrediction` whether to include the prediction, i.e., modifying the class attribute
`protected Matrix`	`m_PLS1_b_hat` the b-hat vector for PLS1
`protected Matrix`	`m_PLS1_P` the P matrix for PLS1
`protected Matrix`	`m_PLS1_RegVector` the regression vector "r-hat" for PLS1
`protected Matrix`	`m_PLS1_W` the W matrix for PLS1
`protected int`	`m_Preprocessing` the type of preprocessing
`protected boolean`	`m_ReplaceMissing` whether to replace missing values
`protected Matrix`	`m_SIMPLS_B` the B matrix for SIMPLS (used for prediction)
`protected Matrix`	`m_SIMPLS_W` the W matrix for 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 Tag[]`	`TAGS_ALGORITHM` the types of algorithm
`static Tag[]`	`TAGS_PREPROCESSING` the types of preprocessing

Fields inherited from class weka.filters.SimpleFilter
m_Debug

Fields inherited from class weka.filters.Filter
m_FirstBatchDone, m_InputRelAtts, m_InputStringAtts, m_NewBatch, m_OutputRelAtts, m_OutputStringAtts

Constructor Summary

Constructors
Constructor and Description

PLSFilter()
default constructor

Constructors
Constructor and Description
`PLSFilter()` default constructor

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`String`	`algorithmTipText()` Returns the tip text for this property
`protected Matrix`	`columnAsVector(Matrix m, int columnIndex)` returns the given column as a vector (actually a n x 1 matrix)
`protected Instances`	`determineOutputFormat(Instances inputFormat)` Determines the output format based on the input format and returns this.
`SelectedTag`	`getAlgorithm()` Gets the type of algorithm to use
`Capabilities`	`getCapabilities()` Returns the Capabilities of this filter.
`protected Matrix`	`getDominantEigenVector(Matrix m)` determines the dominant eigenvector for the given matrix and returns it
`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.
`SelectedTag`	`getPreprocessing()` Gets the type of preprocessing to use
`boolean`	`getReplaceMissing()` Gets whether missing values are replace.
`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.
`protected Matrix`	`getVector(Matrix m, int columnIndex)` returns the (column) vector of the matrix at the specified index
`protected Matrix`	`getX(Instance instance)` returns the data minus the class column as matrix
`protected Matrix`	`getX(Instances instances)` returns the data minus the class column as matrix
`protected Matrix`	`getY(Instance instance)` returns the data class column as matrix
`protected Matrix`	`getY(Instances instances)` returns the data class column as matrix
`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.
`protected void`	`normalizeVector(Matrix v)` normalizes the given vector (inplace)
`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
`protected Instances`	`process(Instances instances)` Processes the given data (may change the provided dataset) and returns the modified version.
`protected Instances`	`processPLS1(Instances instances)` processes the instances using the PLS1 algorithm
`protected Instances`	`processSIMPLS(Instances instances)` processes the instances using the SIMPLS algorithm
`String`	`replaceMissingTipText()` Returns the tip text for this property
`void`	`setAlgorithm(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(SelectedTag value)` Sets the type of preprocessing to use
`void`	`setReplaceMissing(boolean value)` Sets whether to replace missing values.
`protected void`	`setVector(Matrix v, Matrix m, int columnIndex)` stores the data from the (column) vector in the matrix at the specified index
`protected Instances`	`toInstances(Instances header, Matrix x, Matrix y)` returns the X and Y matrix again as Instances object, based on the given header (must have a class attribute set).

Methods inherited from class weka.filters.SimpleBatchFilter
batchFinished, hasImmediateOutputFormat, input

Methods inherited from class weka.filters.SimpleFilter
debugTipText, getDebug, reset, setDebug, setInputFormat

Methods inherited from class weka.filters.Filter
batchFilterFile, bufferInput, copyValues, copyValues, filterFile, flushInput, getCapabilities, getInputFormat, getOutputFormat, initInputLocators, initOutputLocators, inputFormatPeek, isFirstBatchDone, isNewBatch, isOutputFormatDefined, makeCopies, makeCopy, numPendingOutput, output, outputFormatPeek, outputPeek, push, resetQueue, runFilter, setOutputFormat, testInputFormat, toString, useFilter, wekaStaticWrapper

Methods inherited from class java.lang.Object
clone, equals, finalize, 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 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 Tag[] TAGS_PREPROCESSING
```
    the types of preprocessing
  - m_NumComponents
```
protected int m_NumComponents
```
    the maximum number of components to generate
  - m_Algorithm
```
protected int m_Algorithm
```
    the type of algorithm
  - m_PLS1_RegVector
```
protected Matrix m_PLS1_RegVector
```
    the regression vector "r-hat" for PLS1
  - m_PLS1_P
```
protected Matrix m_PLS1_P
```
    the P matrix for PLS1
  - m_PLS1_W
```
protected Matrix m_PLS1_W
```
    the W matrix for PLS1
  - m_PLS1_b_hat
```
protected Matrix m_PLS1_b_hat
```
    the b-hat vector for PLS1
  - m_SIMPLS_W
```
protected Matrix m_SIMPLS_W
```
    the W matrix for SIMPLS
  - m_SIMPLS_B
```
protected Matrix m_SIMPLS_B
```
    the B matrix for SIMPLS (used for prediction)
  - m_PerformPrediction
```
protected boolean m_PerformPrediction
```
    whether to include the prediction, i.e., modifying the class attribute
  - m_Missing
```
protected Filter m_Missing
```
    for replacing missing values
  - m_ReplaceMissing
```
protected boolean m_ReplaceMissing
```
    whether to replace missing values
  - m_Filter
```
protected Filter m_Filter
```
    for centering the data
  - m_Preprocessing
```
protected int m_Preprocessing
```
    the type of preprocessing
  - m_ClassMean
```
protected double m_ClassMean
```
    the mean of the class
  - m_ClassStdDev
```
protected double m_ClassStdDev
```
    the standard deviation of the class
- Constructor Detail
  - PLSFilter
```
public PLSFilter()
```
    default constructor
- Method Detail
  - globalInfo
```
public String globalInfo()
```
    Returns a string describing this classifier.
    
    Specified by:
    
    globalInfo in class SimpleFilter
    
    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
  - listOptions
```
public Enumeration listOptions()
```
    Gets an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class 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 OptionHandler
    
    Overrides:
    
    getOptions in class 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 OptionHandler
    
    Overrides:
    
    setOptions in class SimpleFilter
    
    Parameters:
    
    options - the options to use
    
    Throws:
    
    Exception - if the option setting fails
    
    See Also:
    
    SimpleFilter.reset()
  - 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(SelectedTag value)
```
    Sets the type of algorithm to use
    
    Parameters:
    
    value - the algorithm type
  - getAlgorithm
```
public 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(SelectedTag value)
```
    Sets the type of preprocessing to use
    
    Parameters:
    
    value - the preprocessing type
  - getPreprocessing
```
public SelectedTag getPreprocessing()
```
    Gets the type of preprocessing to use
    
    Returns:
    
    the current preprocessing type.
  - determineOutputFormat
```
protected Instances determineOutputFormat(Instances inputFormat)
                                   throws Exception
```
    Determines the output format based on the input format and returns this. In case the output format cannot be returned immediately, i.e., immediateOutputFormat() returns false, then this method will be called from batchFinished().
    
    Specified by:
    
    determineOutputFormat in class SimpleFilter
    
    Parameters:
    
    inputFormat - the input format to base the output format on
    
    Returns:
    
    the output format
    
    Throws:
    
    Exception - in case the determination goes wrong
    
    See Also:
    
    SimpleBatchFilter.hasImmediateOutputFormat(), SimpleBatchFilter.batchFinished()
  - getX
```
protected Matrix getX(Instances instances)
```
    returns the data minus the class column as matrix
    
    Parameters:
    
    instances - the data to work on
    
    Returns:
    
    the data without class attribute
  - getX
```
protected Matrix getX(Instance instance)
```
    returns the data minus the class column as matrix
    
    Parameters:
    
    instance - the instance to work on
    
    Returns:
    
    the data without the class attribute
  - getY
```
protected Matrix getY(Instances instances)
```
    returns the data class column as matrix
    
    Parameters:
    
    instances - the data to work on
    
    Returns:
    
    the class attribute
  - getY
```
protected Matrix getY(Instance instance)
```
    returns the data class column as matrix
    
    Parameters:
    
    instance - the instance to work on
    
    Returns:
    
    the class attribute
  - toInstances
```
protected Instances toInstances(Instances header,
                                Matrix x,
                                Matrix y)
```
    returns the X and Y matrix again as Instances object, based on the given header (must have a class attribute set).
    
    Parameters:
    
    header - the format of the instance object
    
    x - the X matrix (data)
    
    y - the Y matrix (class)
    
    Returns:
    
    the assembled data
  - columnAsVector
```
protected Matrix columnAsVector(Matrix m,
                                int columnIndex)
```
    returns the given column as a vector (actually a n x 1 matrix)
    
    Parameters:
    
    m - the matrix to work on
    
    columnIndex - the column to return
    
    Returns:
    
    the column as n x 1 matrix
  - setVector
```
protected void setVector(Matrix v,
                         Matrix m,
                         int columnIndex)
```
    stores the data from the (column) vector in the matrix at the specified index
    
    Parameters:
    
    v - the vector to store in the matrix
    
    m - the receiving matrix
    
    columnIndex - the column to store the values in
  - getVector
```
protected Matrix getVector(Matrix m,
                           int columnIndex)
```
    returns the (column) vector of the matrix at the specified index
    
    Parameters:
    
    m - the matrix to work on
    
    columnIndex - the column to get the values from
    
    Returns:
    
    the column vector
  - getDominantEigenVector
```
protected Matrix getDominantEigenVector(Matrix m)
```
    determines the dominant eigenvector for the given matrix and returns it
    
    Parameters:
    
    m - the matrix to determine the dominant eigenvector for
    
    Returns:
    
    the dominant eigenvector
  - normalizeVector
```
protected void normalizeVector(Matrix v)
```
    normalizes the given vector (inplace)
    
    Parameters:
    
    v - the vector to normalize
  - processPLS1
```
protected Instances processPLS1(Instances instances)
                         throws Exception
```
    processes the instances using the PLS1 algorithm
    
    Parameters:
    
    instances - the data to process
    
    Returns:
    
    the modified data
    
    Throws:
    
    Exception - in case the processing goes wrong
  - processSIMPLS
```
protected Instances processSIMPLS(Instances instances)
                           throws Exception
```
    processes the instances using the SIMPLS algorithm
    
    Parameters:
    
    instances - the data to process
    
    Returns:
    
    the modified data
    
    Throws:
    
    Exception - in case the processing goes wrong
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns the Capabilities of this filter.
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class Filter
    
    Returns:
    
    the capabilities of this object
    
    See Also:
    
    Capabilities
  - process
```
protected Instances process(Instances instances)
                     throws Exception
```
    Processes the given data (may change the provided dataset) and returns the modified version. This method is called in batchFinished().
    
    Specified by:
    
    process in class SimpleFilter
    
    Parameters:
    
    instances - the data to process
    
    Returns:
    
    the modified data
    
    Throws:
    
    Exception - in case the processing goes wrong
    
    See Also:
    
    SimpleBatchFilter.batchFinished()
  - getRevision
```
public String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class Filter
    
    Returns:
    
    the revision
  - main
```
public static void main(String[] args)
```
    runs the filter with the given arguments.
    
    Parameters:
    
    args - the commandline arguments

Class PLSFilter

Field Summary

Fields inherited from class weka.filters.SimpleFilter

Fields inherited from class weka.filters.Filter

Constructor Summary

Method Summary

Methods inherited from class weka.filters.SimpleBatchFilter

Methods inherited from class weka.filters.SimpleFilter

Methods inherited from class weka.filters.Filter

Methods inherited from class java.lang.Object

Field Detail

ALGORITHM_SIMPLS

ALGORITHM_PLS1

TAGS_ALGORITHM

PREPROCESSING_NONE

PREPROCESSING_CENTER

PREPROCESSING_STANDARDIZE

TAGS_PREPROCESSING

m_NumComponents

m_Algorithm

m_PLS1_RegVector

m_PLS1_P

m_PLS1_W

m_PLS1_b_hat

m_SIMPLS_W

m_SIMPLS_B

m_PerformPrediction

m_Missing

m_ReplaceMissing

m_Filter

m_Preprocessing

m_ClassMean

m_ClassStdDev

Constructor Detail

PLSFilter

Method Detail

globalInfo

getTechnicalInformation

listOptions

getOptions

setOptions

numComponentsTipText

setNumComponents

getNumComponents

performPredictionTipText

setPerformPrediction

getPerformPrediction

algorithmTipText

setAlgorithm

getAlgorithm

replaceMissingTipText

setReplaceMissing

getReplaceMissing

preprocessingTipText

setPreprocessing

getPreprocessing

determineOutputFormat

getX

getX

getY

getY

toInstances

columnAsVector

setVector

getVector

getDominantEigenVector

normalizeVector

processPLS1

processSIMPLS

getCapabilities

process

getRevision

main