weka.classifiers.functions

Class SGD

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.RandomizableClassifier

weka.classifiers.functions.SGD

All Implemented Interfaces:

Serializable, Cloneable, Classifier, UpdateableClassifier, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler

public class SGD
extends RandomizableClassifier
implements UpdateableClassifier, OptionHandler

Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression and linear regression). 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 numeric class attributes, the squared loss function (2) must be used.

Valid options are:

 -F
  Set the loss function to minimize. 0 = hinge loss (SVM), 1 = log loss (logistic regression),
  2 = squared loss (regression).
  (default = 0)

 -L
  The learning rate. If normalization is
  turned off (as it is automatically for streaming data), then the
  default learning rate will need to be reduced (try 0.0001).
  (default = 0.01).

 -R <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: 8034 $

Author:

Eibe Frank (eibe{[at]}cs{[dot]}waikato{[dot]}ac{[dot]}nz), Mark Hall (mhall{[at]}pentaho{[dot]}com)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static int	HINGE the hinge loss function.
static int	LOGLOSS the log loss function.
static int	SQUAREDLOSS the squared loss funtion.
static Tag[]	TAGS_SELECTION Loss functions to choose from

Constructor Summary

Constructors

Constructor and Description
SGD()

Method Summary

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
String	dontNormalizeTipText() Returns the tip text for this property
String	dontReplaceMissingTipText() Returns the tip text for this property
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
double	getLearningRate() Get the learning rate.
SelectedTag	getLossFunction() Get the current loss function.
String[]	getOptions() Gets the current settings of the classifier.
String	getRevision() Returns the revision string.
String	globalInfo() Returns a string describing classifier
String	lambdaTipText() Returns the tip text for this property
String	learningRateTipText() 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	setLearningRate(double lr) Set the learning rate.
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.RandomizableClassifier

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.AbstractClassifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

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

Field Detail

HINGE

public static final int HINGE

the hinge loss function.

See Also:

Constant Field Values

LOGLOSS

public static final int LOGLOSS

the log loss function.

See Also:

Constant Field Values

SQUAREDLOSS

public static final int SQUAREDLOSS

the squared loss funtion.

See Also:

Constant Field Values

TAGS_SELECTION

public static final Tag[] TAGS_SELECTION

Loss functions to choose from

Constructor Detail

SGD

public SGD()

Method Detail

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface Classifier

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class AbstractClassifier

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

setLearningRate

public void setLearningRate(double lr)

Set the learning rate.

Parameters:

lr - the learning rate to use.

getLearningRate

public double getLearningRate()

Get the learning rate.

Returns:

the learning rate

learningRateTipText

public String learningRateTipText()

Returns the tip text for this property

Returns:

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

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 RandomizableClassifier

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:

 -F
  Set the loss function to minimize. 0 = hinge loss (SVM), 1 = log loss (logistic regression),
  2 = squared loss (regression).
  (default = 0)

 -L
  The learning rate. If normalization is
  turned off (as it is automatically for streaming data), then the
  default learning rate will need to be reduced (try 0.0001).
  (default = 0.01).

 -R <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 RandomizableClassifier

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 RandomizableClassifier

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

reset

public void reset()

Reset the classifier.

buildClassifier

public void buildClassifier(Instances data)
                     throws Exception

Method for building the classifier.

Specified by:

buildClassifier in interface Classifier

Parameters:

data - the set of training instances.

Throws:

Exception - if the classifier can't be built successfully.

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

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

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 AbstractClassifier

Returns:

the revision

main

public static void main(String[] args)

Main method for testing this class.