Class PLSFilter
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:
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:
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Field Summary
FieldsModifier and TypeFieldDescriptionstatic final int
the type of algorithm: PLS1static final int
the type of algorithm: SIMPLSstatic final int
the type of preprocessing: Centerstatic final int
the type of preprocessing: Nonestatic final int
the type of preprocessing: Standardizestatic final Tag[]
the types of algorithmstatic final Tag[]
the types of preprocessing -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionReturns the tip text for this propertyGets the type of algorithm to useReturns the Capabilities of this filter.int
returns the maximum number of attributes to use.String[]
returns the options of the current setupboolean
Gets whether the class attribute is updated with the predicted value.Gets the type of preprocessing to useboolean
Gets whether missing values are replace.Returns the revision string.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 a string describing this classifier.Gets an enumeration describing the available options.static void
runs the filter with the given arguments.Returns the tip text for this propertyReturns the tip text for this propertyReturns the tip text for this propertyReturns the tip text for this propertyvoid
setAlgorithm
(SelectedTag value) Sets the type of algorithm to usevoid
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 usevoid
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, numPendingOutput, output, outputPeek, toString, useFilter, wekaStaticWrapper
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Field Details
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ALGORITHM_SIMPLS
public static final int ALGORITHM_SIMPLSthe type of algorithm: SIMPLS- See Also:
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ALGORITHM_PLS1
public static final int ALGORITHM_PLS1the type of algorithm: PLS1- See Also:
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TAGS_ALGORITHM
the types of algorithm -
PREPROCESSING_NONE
public static final int PREPROCESSING_NONEthe type of preprocessing: None- See Also:
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PREPROCESSING_CENTER
public static final int PREPROCESSING_CENTERthe type of preprocessing: Center- See Also:
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PREPROCESSING_STANDARDIZE
public static final int PREPROCESSING_STANDARDIZEthe type of preprocessing: Standardize- See Also:
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TAGS_PREPROCESSING
the types of preprocessing
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Constructor Details
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PLSFilter
public PLSFilter()default constructor
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Method Details
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globalInfo
Returns a string describing this classifier.- Specified by:
globalInfo
in classSimpleFilter
- Returns:
- a description of the classifier suitable for displaying in the explorer/experimenter gui
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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listOptions
Gets an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classSimpleFilter
- Returns:
- an enumeration of all the available options.
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getOptions
returns the options of the current setup- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classSimpleFilter
- Returns:
- the current options
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setOptions
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 interfaceOptionHandler
- Overrides:
setOptions
in classSimpleFilter
- Parameters:
options
- the options to use- Throws:
Exception
- if the option setting fails- See Also:
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numComponentsTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setNumComponents
public void setNumComponents(int value) sets the maximum number of attributes to use.- Parameters:
value
- the maximum number of attributes
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getNumComponents
public int getNumComponents()returns the maximum number of attributes to use.- Returns:
- the current maximum number of attributes
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performPredictionTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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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.
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getPerformPrediction
public boolean getPerformPrediction()Gets whether the class attribute is updated with the predicted value.- Returns:
- true if the class attribute is updated
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algorithmTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setAlgorithm
Sets the type of algorithm to use- Parameters:
value
- the algorithm type
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getAlgorithm
Gets the type of algorithm to use- Returns:
- the current algorithm type.
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replaceMissingTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setReplaceMissing
public void setReplaceMissing(boolean value) Sets whether to replace missing values.- Parameters:
value
- if true missing values are replaced with the ReplaceMissingValues filter.
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getReplaceMissing
public boolean getReplaceMissing()Gets whether missing values are replace.- Returns:
- true if missing values are replaced with the ReplaceMissingValues filter
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preprocessingTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setPreprocessing
Sets the type of preprocessing to use- Parameters:
value
- the preprocessing type
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getPreprocessing
Gets the type of preprocessing to use- Returns:
- the current preprocessing type.
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getCapabilities
Returns the Capabilities of this filter.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classFilter
- Returns:
- the capabilities of this object
- See Also:
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getRevision
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classFilter
- Returns:
- the revision
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main
runs the filter with the given arguments.- Parameters:
args
- the commandline arguments
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