jsat.classifiers.linear

Class PassiveAggressive

java.lang.Object

jsat.classifiers.linear.PassiveAggressive

All Implemented Interfaces:

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

public class PassiveAggressive
extends java.lang.Object
implements UpdateableClassifier, BinaryScoreClassifier, UpdateableRegressor, Parameterized, SingleWeightVectorModel

An implementations of the 3 versions of the Passive Aggressive algorithm for binary classification and regression. Its a type of online algorithm that performs the minimal update necessary to correct for a mistake.

See:
Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S.,&Singer, Y. (2006). Online passive-aggressive algorithms. Journal of Machine Learning Research, 7, 551–585.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	PassiveAggressive.Mode Controls which version of the Passive Aggressive update is used

Constructor Summary

Constructors

Constructor and Description
PassiveAggressive() Creates a new Passive Aggressive learner that does 10 epochs and uses PassiveAggressive.Mode.PA1
PassiveAggressive(int epochs, PassiveAggressive.Mode mode) Creates a new Passive Aggressive learner

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
PassiveAggressive	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	getC() Returns the aggressiveness parameter
int	getEpochs() Returns the number of epochs used for training
double	getEps() Returns the maximum acceptable difference in prediction and truth
PassiveAggressive.Mode	getMode() Returns which version of the PA update is used
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.
static Distribution	guessC(DataSet d) Guess the distribution to use for the regularization term C in PassiveAggressive.
int	numWeightsVecs() Returns the number of weight vectors that can be returned.
double	regress(DataPoint data)
void	setC(double C) Set the aggressiveness parameter.
void	setEpochs(int epochs) Sets the number of whole iterations through the training set that will be performed for training
void	setEps(double eps) Sets the range for numerical prediction.
void	setMode(PassiveAggressive.Mode mode) Sets which version of the PA update is used.
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(ClassificationDataSet dataSet) Trains the classifier and constructs a model for classification using the given data set.
void	train(ClassificationDataSet dataSet, boolean parallel) Trains the classifier and constructs a model for classification using the given data set.
void	train(RegressionDataSet dataSet)
void	train(RegressionDataSet dataSet, boolean parallel)
void	update(DataPoint dataPoint, double targetValue) 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 java.lang.Object

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

Methods inherited from interface jsat.parameters.Parameterized

getParameter, getParameters

Constructor Detail

PassiveAggressive

public PassiveAggressive()

Creates a new Passive Aggressive learner that does 10 epochs and uses PassiveAggressive.Mode.PA1

PassiveAggressive

public PassiveAggressive(int epochs,
                         PassiveAggressive.Mode mode)

Creates a new Passive Aggressive learner

Parameters:

epochs - the number of training epochs to use during batch training

mode - which version of the update to perform

Method Detail

setC

public void setC(double C)

Set the aggressiveness parameter. Increasing the value of this parameter increases the aggressiveness of the algorithm. It must be a positive value. This parameter essentially performs a type of regularization on the updates
An infinitely large value is equivalent to being completely aggressive, and is performed when the mode is set to PassiveAggressive.Mode.PA.

Parameters:

C - the positive aggressiveness parameter

getC

public double getC()

Returns the aggressiveness parameter

Returns:

the aggressiveness parameter

setMode

public void setMode(PassiveAggressive.Mode mode)

Sets which version of the PA update is used.

Parameters:

mode - which PA update style to perform

getMode

public PassiveAggressive.Mode getMode()

Returns which version of the PA update is used

Returns:

which PA update style is used

setEps

public void setEps(double eps)

Sets the range for numerical prediction. If it is within range of the given value, no error will be incurred.

Parameters:

eps - the maximum acceptable difference in prediction and truth

getEps

public double getEps()

Returns the maximum acceptable difference in prediction and truth

Returns:

the maximum acceptable difference in prediction and truth

setEpochs

public void setEpochs(int epochs)

Sets the number of whole iterations through the training set that will be performed for training

Parameters:

epochs - the number of whole iterations through the data set

getEpochs

public int getEpochs()

Returns the number of epochs used for training

Returns:

the number of epochs used for training

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.

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.

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

train

public void train(ClassificationDataSet dataSet,
                  boolean parallel)

Description copied from interface: Classifier

Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.

Specified by:

train in interface Classifier

Parameters:

dataSet - the data set to train on

parallel - true if multiple threads should be used to train the model. false if it should be done in a single threaded manner.

train

public void train(ClassificationDataSet dataSet)

Description copied from interface: Classifier

Trains the classifier and constructs a model for classification using the given data set.

Specified by:

train in interface Classifier

Parameters:

dataSet - the data set to train on

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

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

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 targetValue)

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

targetValue - the target value of the data point

regress

public double regress(DataPoint data)

Specified by:

regress in interface Regressor

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

clone

public PassiveAggressive 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

Overrides:

clone in class java.lang.Object

guessC

public static Distribution guessC(DataSet d)

Guess the distribution to use for the regularization term C in PassiveAggressive.

Parameters:

d - the data set to get the guess for

Returns:

the guess for the C parameter