SPegasos (weka-stable 3.6.8 API)

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

All Implemented Interfaces:

Serializable, Cloneable, UpdateableClassifier, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler
```
public class SPegasos
extends Classifier
implements TechnicalInformationHandler, UpdateableClassifier, OptionHandler
```
Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007). This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes, so the coefficients in the output are based on the normalized data. For more information, see

S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.
BibTeX:
```
 @inproceedings{Shalev-Shwartz2007,
    author = {S. Shalev-Shwartz and Y. Singer and N. Srebro},
    booktitle = {24th International Conference on MachineLearning},
    pages = {807-814},
    title = {Pegasos: Primal Estimated sub-GrAdient SOlver for SVM},
    year = {2007}
 }
 
```
Valid options are:
```
 -L <double>
  The lambda regularization constant (default = 0.0001)
```
```
 -E <integer>
  The number of epochs to perform (batch learning only, default = 500)
```
```
 -N
  Don't normalize the data
```
```
 -M
  Don't replace missing values
```
Version:

$Revision: 6580 $

Author:

Mark Hall (mhall{[at]}pentaho{[dot]}com)

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected static int`	`HINGE`
`protected static int`	`LOGLOSS`
`protected Instances`	`m_data` Holds the header of the training data
`protected boolean`	`m_dontNormalize` Turn off normalization of the input data.
`protected boolean`	`m_dontReplaceMissing` Turn off global replacement of missing values.
`protected int`	`m_epochs` The number of epochs to perform (batch learning).
`protected double`	`m_lambda` The regularization parameter
`protected int`	`m_loss` The current loss function to minimize
`protected NominalToBinary`	`m_nominalToBinary` Convert nominal attributes to numerically coded binary ones
`protected Normalize`	`m_normalize` Normalize the training data
`protected ReplaceMissingValues`	`m_replaceMissing` Replace missing values
`protected double`	`m_t` Holds the current iteration number
`protected double[]`	`m_weights` Stores the weights (+ bias in the last element)
`static Tag[]`	`TAGS_SELECTION` Loss functions to choose from

Fields inherited from class weka.classifiers.Classifier
m_Debug

Constructor Summary

Constructors
Constructor and Description

SPegasos()

Constructors
Constructor and Description
`SPegasos()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances data)` Method for building the classifier.
`double[]`	`distributionForInstance(Instance inst)` Computes the distribution for a given instance
`protected double`	`dloss(double z)`
`String`	`dontNormalizeTipText()` Returns the tip text for this property
`String`	`dontReplaceMissingTipText()` Returns the tip text for this property
`protected static double`	`dotProd(Instance inst1, double[] weights, int classIndex)`
`String`	`epochsTipText()` Returns the tip text for this property
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getDontNormalize()` Get whether normalization has been turned off.
`boolean`	`getDontReplaceMissing()` Get whether global replacement of missing values has been disabled.
`int`	`getEpochs()` Get current number of epochs
`double`	`getLambda()` Get the current value of lambda
`SelectedTag`	`getLossFunction()` Get the current loss function.
`String[]`	`getOptions()` Gets the current settings of the classifier.
`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.
`String`	`globalInfo()` Returns a string describing classifier
`String`	`lambdaTipText()` Returns the tip text for this property
`Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`String`	`lossFunctionTipText()` Returns the tip text for this property
`static void`	`main(String[] args)` Main method for testing this class.
`void`	`reset()` Reset the classifier.
`void`	`setDontNormalize(boolean m)` Turn normalization off/on.
`void`	`setDontReplaceMissing(boolean m)` Turn global replacement of missing values off/on.
`void`	`setEpochs(int e)` Set the number of epochs to use
`void`	`setLambda(double lambda)` Set the value of lambda to use
`void`	`setLossFunction(SelectedTag function)` Set the loss function to use.
`void`	`setOptions(String[] options)` Parses a given list of options.
`String`	`toString()` Prints out the classifier.
`void`	`updateClassifier(Instance instance)` Updates the classifier with the given instance.

Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - m_replaceMissing
```
protected ReplaceMissingValues m_replaceMissing
```
    Replace missing values
  - m_nominalToBinary
```
protected NominalToBinary m_nominalToBinary
```
    Convert nominal attributes to numerically coded binary ones
  - m_normalize
```
protected Normalize m_normalize
```
    Normalize the training data
  - m_lambda
```
protected double m_lambda
```
    The regularization parameter
  - m_weights
```
protected double[] m_weights
```
    Stores the weights (+ bias in the last element)
  - m_t
```
protected double m_t
```
    Holds the current iteration number
  - m_epochs
```
protected int m_epochs
```
    The number of epochs to perform (batch learning). Total iterations is m_epochs * num instances
  - m_dontNormalize
```
protected boolean m_dontNormalize
```
    Turn off normalization of the input data. This option gets forced for incremental training.
  - m_dontReplaceMissing
```
protected boolean m_dontReplaceMissing
```
    Turn off global replacement of missing values. Missing values will be ignored instead. This option gets forced for incremental training.
  - m_data
```
protected Instances m_data
```
    Holds the header of the training data
  - HINGE
```
protected static final int HINGE
```
    See Also:
    
    Constant Field Values
  - LOGLOSS
```
protected static final int LOGLOSS
```
    See Also:
    
    Constant Field Values
  - m_loss
```
protected int m_loss
```
    The current loss function to minimize
  - TAGS_SELECTION
```
public static final Tag[] TAGS_SELECTION
```
    Loss functions to choose from
- Constructor Detail
  - SPegasos
```
public SPegasos()
```
- Method Detail
  - 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
  - lambdaTipText
```
public String lambdaTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setLambda
```
public void setLambda(double lambda)
```
    Set the value of lambda to use
    
    Parameters:
    
    lambda - the value of lambda to use
  - getLambda
```
public double getLambda()
```
    Get the current value of lambda
    
    Returns:
    
    the current value of lambda
  - epochsTipText
```
public String epochsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setEpochs
```
public void setEpochs(int e)
```
    Set the number of epochs to use
    
    Parameters:
    
    e - the number of epochs to use
  - getEpochs
```
public int getEpochs()
```
    Get current number of epochs
    
    Returns:
    
    the current number of epochs
  - setDontNormalize
```
public void setDontNormalize(boolean m)
```
    Turn normalization off/on.
    
    Parameters:
    
    m - true if normalization is to be disabled.
  - getDontNormalize
```
public boolean getDontNormalize()
```
    Get whether normalization has been turned off.
    
    Returns:
    
    true if normalization has been disabled.
  - dontNormalizeTipText
```
public String dontNormalizeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDontReplaceMissing
```
public void setDontReplaceMissing(boolean m)
```
    Turn global replacement of missing values off/on. If turned off, then missing values are effectively ignored.
    
    Parameters:
    
    m - true if global replacement of missing values is to be turned off.
  - getDontReplaceMissing
```
public boolean getDontReplaceMissing()
```
    Get whether global replacement of missing values has been disabled.
    
    Returns:
    
    true if global replacement of missing values has been turned off
  - dontReplaceMissingTipText
```
public String dontReplaceMissingTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setLossFunction
```
public void setLossFunction(SelectedTag function)
```
    Set the loss function to use.
    
    Parameters:
    
    function - the loss function to use.
  - getLossFunction
```
public SelectedTag getLossFunction()
```
    Get the current loss function.
    
    Returns:
    
    the current loss function.
  - lossFunctionTipText
```
public String lossFunctionTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - listOptions
```
public Enumeration<Option> 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(String[] options)
                throws Exception
```
    Parses a given list of options.
    Valid options are:
```
 -L <double>
  The lambda regularization constant (default = 0.0001)
```
```
 -E <integer>
  The number of epochs to perform (batch learning only, default = 500)
```
```
 -N
  Don't normalize the data
```
```
 -M
  Don't replace missing values
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class Classifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    Exception - if an option is not supported
  - getOptions
```
public 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
  - globalInfo
```
public String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    
    a description 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
  - reset
```
public void reset()
```
    Reset the classifier.
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws Exception
```
    Method for building the classifier.
    
    Specified by:
    
    buildClassifier in class Classifier
    
    Parameters:
    
    data - the set of training instances.
    
    Throws:
    
    Exception - if the classifier can't be built successfully.
  - dotProd
```
protected static double dotProd(Instance inst1,
                                double[] weights,
                                int classIndex)
```
  - dloss
```
protected double dloss(double z)
```
  - updateClassifier
```
public void updateClassifier(Instance instance)
                      throws Exception
```
    Updates the classifier with the given instance.
    
    Specified by:
    
    updateClassifier in interface UpdateableClassifier
    
    Parameters:
    
    instance - the new training instance to include in the model
    
    Throws:
    
    Exception - if the instance could not be incorporated in the model.
  - distributionForInstance
```
public double[] distributionForInstance(Instance inst)
                                 throws Exception
```
    Computes the distribution for a given instance
    
    Overrides:
    
    distributionForInstance in class Classifier
    
    Parameters:
    
    instance - the instance for which distribution is computed
    
    Returns:
    
    the distribution
    
    Throws:
    
    Exception - if the distribution can't be computed successfully
  - toString
```
public String toString()
```
    Prints out the classifier.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    a description of the classifier as a string
  - getRevision
```
public 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(String[] args)
```
    Main method for testing this class.

Class SPegasos

Field Summary

Fields inherited from class weka.classifiers.Classifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

m_replaceMissing

m_nominalToBinary

m_normalize

m_lambda

m_weights

m_t

m_epochs

m_dontNormalize

m_dontReplaceMissing

m_data

HINGE

LOGLOSS

m_loss

TAGS_SELECTION

Constructor Detail

SPegasos

Method Detail

getCapabilities

lambdaTipText

setLambda

getLambda

epochsTipText

setEpochs

getEpochs

setDontNormalize

getDontNormalize

dontNormalizeTipText

setDontReplaceMissing

getDontReplaceMissing

dontReplaceMissingTipText

setLossFunction

getLossFunction

lossFunctionTipText

listOptions

setOptions

getOptions

globalInfo

getTechnicalInformation

reset

buildClassifier

dotProd

dloss

updateClassifier

distributionForInstance

toString

getRevision

main