jsat.classifiers.linear

Class STGD

java.lang.Object

jsat.classifiers.BaseUpdateableClassifier

jsat.classifiers.linear.STGD

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, BinaryScoreClassifier, Classifier, UpdateableClassifier, Parameterized, Regressor, UpdateableRegressor, SimpleWeightVectorModel, SingleWeightVectorModel

public class STGD
extends BaseUpdateableClassifier
implements UpdateableRegressor, BinaryScoreClassifier, Parameterized, SingleWeightVectorModel

This provides an implementation of Sparse Truncated Gradient Descent for L₁ regularized linear classification and regression on sparse data sets.

Unlike normal L₁ regression, regularization is controlled by the gravity parameter, but other parameters contribute to the level of sparsity.

See: Langford, J., Li, L.,&Zhang, T. (2009). Sparse online learning via truncated gradient. The Journal of Machine Learning Research, 10, 777–801. Retrieved from here

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
STGD(int K, double learningRate, double threshold, double gravity) Creates a new STGD learner

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
STGD	clone()
double	getBias() Returns the bias term used for the model, or 0 of the model does not support or was not trained with a bias term.
double	getBias(int index) Returns the bias term used with the weight vector for the given class index.
double	getGravity() Returns the regularization parameter
int	getK() Returns the frequency of regularization application
double	getLearningRate() Returns the learning rate to use
Vec	getRawWeight() Returns the only weight vector used for the model
Vec	getRawWeight(int index) Returns the raw weight vector associated with the given class index.
double	getScore(DataPoint dp) Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
double	getThreshold() Returns the coefficient threshold parameter
int	numWeightsVecs() Returns the number of weight vectors that can be returned.
double	regress(DataPoint data)
void	setGravity(double gravity) Sets the gravity regularization parameter that "weighs down" the coefficient values.
void	setK(int K) Sets the frequency of applying the gravity parameter to the weight vector.
void	setLearningRate(double learningRate) Sets the learning rate to use
void	setThreshold(double threshold) Sets the threshold for a coefficient value to avoid regularization.
void	setUp(CategoricalData[] categoricalAttributes, int numericAttributes) Prepares the classifier to begin learning from its UpdateableRegressor.update(jsat.classifiers.DataPoint, double) method.
void	setUp(CategoricalData[] categoricalAttributes, int numericAttributes, CategoricalData predicting) Prepares the classifier to begin learning from its UpdateableClassifier.update(jsat.classifiers.DataPoint, int) method.
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	train(RegressionDataSet dataSet)
void	train(RegressionDataSet dataSet, boolean parallel)
void	update(DataPoint dataPoint, double y) Updates the classifier by giving it a new data point to learn from.
void	update(DataPoint dataPoint, int targetClass) Updates the classifier by giving it a new data point to learn from.

Methods inherited from class jsat.classifiers.BaseUpdateableClassifier

getEpochs, setEpochs, train, train, trainEpochs

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.classifiers.Classifier

train, train

Methods inherited from interface jsat.parameters.Parameterized

getParameter, getParameters

Constructor Detail

STGD

public STGD(int K,
            double learningRate,
            double threshold,
            double gravity)

Creates a new STGD learner

Parameters:

K - the regularization frequency

learningRate - the learning rate to use

threshold - the regularization threshold

gravity - the regularization parameter

Method Detail

setK

public void setK(int K)

Sets the frequency of applying the gravity parameter to the weight vector. This value must be positive, and the gravity will be applied every K updates. Increasing this value encourages greater sparsity.

Parameters:

K - the frequency to apply regularization in [1, Infinity )

getK

public int getK()

Returns the frequency of regularization application

Returns:

the frequency of regularization application

setLearningRate

public void setLearningRate(double learningRate)

Sets the learning rate to use

Parameters:

learningRate - the learning rate > 0.

getLearningRate

public double getLearningRate()

Returns the learning rate to use

Returns:

the learning rate to use

setThreshold

public void setThreshold(double threshold)

Sets the threshold for a coefficient value to avoid regularization. While a coefficient reaches this magnitude, regularization will not be applied.

Parameters:

threshold - the coefficient regularization threshold in ( 0, Infinity ]

getThreshold

public double getThreshold()

Returns the coefficient threshold parameter

Returns:

the coefficient threshold parameter

setGravity

public void setGravity(double gravity)

Sets the gravity regularization parameter that "weighs down" the coefficient values. Larger gravity values impose stronger regularization, and encourage greater sparsity.

Parameters:

gravity - the regularization parameter in ( 0, Infinity )

getGravity

public double getGravity()

Returns the regularization parameter

Returns:

the regularization parameter

getRawWeight

public Vec getRawWeight()

Description copied from interface: SingleWeightVectorModel

Returns the only weight vector used for the model

Specified by:

getRawWeight in interface SingleWeightVectorModel

Returns:

the only weight vector used for the model

getBias

public double getBias()

Description copied from interface: SingleWeightVectorModel

Returns the bias term used for the model, or 0 of the model does not support or was not trained with a bias term.

Specified by:

getBias in interface SingleWeightVectorModel

Returns:

the bias term for the model

getRawWeight

public Vec getRawWeight(int index)

Description copied from interface: SimpleWeightVectorModel

Returns the raw weight vector associated with the given class index. If the given class is an implicit zero vector, a ConstantVector object may be returned.
Do not alter the returned weight vector, as it will change the model's values.

If a regression problem, only index = 0 should be used

Specified by:

getRawWeight in interface SimpleWeightVectorModel

Parameters:

index - the class index to get the weight vector for

Returns:

the weight vector used for the specified class

getBias

public double getBias(int index)

Description copied from interface: SimpleWeightVectorModel

Returns the bias term used with the weight vector for the given class index. If the model does not support or was not trained with bias weights, 0 will be returned.

If a regression problem, only index = 0 should be used

Specified by:

getBias in interface SimpleWeightVectorModel

Parameters:

index - the class index to get the weight vector for

Returns:

the bias term for the specified class

numWeightsVecs

public int numWeightsVecs()

Description copied from interface: SimpleWeightVectorModel

Returns the number of weight vectors that can be returned. For binary classification problems the value may be 1 if only a single weight vector's sign is used to determine the class. For multi-class problems, the weight vector count includes the implicit zero vector (if one is being used).

Specified by:

numWeightsVecs in interface SimpleWeightVectorModel

Returns:

the number of weight vectors for which SimpleWeightVectorModel.getRawWeight(int) can be called.

clone

public STGD clone()

Specified by:

clone in interface BinaryScoreClassifier

Specified by:

clone in interface Classifier

Specified by:

clone in interface UpdateableClassifier

Specified by:

clone in interface Regressor

Specified by:

clone in interface UpdateableRegressor

Specified by:

clone in class BaseUpdateableClassifier

setUp

public void setUp(CategoricalData[] categoricalAttributes,
                  int numericAttributes,
                  CategoricalData predicting)

Description copied from interface: UpdateableClassifier

Prepares the classifier to begin learning from its UpdateableClassifier.update(jsat.classifiers.DataPoint, int) method.

Specified by:

setUp in interface UpdateableClassifier

Parameters:

categoricalAttributes - an array containing the categorical attributes that will be in each data point

numericAttributes - the number of numeric attributes that will be in each data point

predicting - the information for the target class that will be predicted

setUp

public void setUp(CategoricalData[] categoricalAttributes,
                  int numericAttributes)

Description copied from interface: UpdateableRegressor

Prepares the classifier to begin learning from its UpdateableRegressor.update(jsat.classifiers.DataPoint, double) method.

Specified by:

setUp in interface UpdateableRegressor

Parameters:

categoricalAttributes - an array containing the categorical attributes that will be in each data point

numericAttributes - the number of numeric attributes that will be in each data point

train

public void train(RegressionDataSet dataSet,
                  boolean parallel)

Specified by:

train in interface Regressor

train

public void train(RegressionDataSet dataSet)

Specified by:

train in interface Regressor

update

public void update(DataPoint dataPoint,
                   int targetClass)

Description copied from interface: UpdateableClassifier

Updates the classifier by giving it a new data point to learn from.

Specified by:

update in interface UpdateableClassifier

Parameters:

dataPoint - the data point to learn

targetClass - the target class of the data point

update

public void update(DataPoint dataPoint,
                   double y)

Description copied from interface: UpdateableRegressor

Updates the classifier by giving it a new data point to learn from.

Specified by:

update in interface UpdateableRegressor

Parameters:

dataPoint - the data point to learn

y - the target value of the data point

classify

public CategoricalResults classify(DataPoint data)

Description copied from interface: Classifier

Performs classification on the given data point.

Specified by:

classify in interface Classifier

Parameters:

data - the data point to classify

Returns:

the results of the classification.

regress

public double regress(DataPoint data)

Specified by:

regress in interface Regressor

supportsWeightedData

public boolean supportsWeightedData()

Description copied from interface: Classifier

Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.

Specified by:

supportsWeightedData in interface Classifier

Specified by:

supportsWeightedData in interface Regressor

Returns:

true if the model supports weighted data, false otherwise

getScore

public double getScore(DataPoint dp)

Description copied from interface: BinaryScoreClassifier

Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.

Specified by:

getScore in interface BinaryScoreClassifier

Parameters:

dp - the data point to predict the class label of

Returns:

the score for the given data point