it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear

Class LibLinearLearningAlgorithm

java.lang.Object

it.uniroma2.sag.kelp.learningalgorithm.classification.liblinear.LibLinearLearningAlgorithm

All Implemented Interfaces:

BinaryLearningAlgorithm, ClassificationLearningAlgorithm, LearningAlgorithm, LinearMethod

public class LibLinearLearningAlgorithm
extends Object
implements LinearMethod, ClassificationLearningAlgorithm, BinaryLearningAlgorithm

This class implements linear SVMs models trained using a coordinate descent algorithm [Fan et al, 2008]. It operates in an explicit feature space (i.e. it does not relies on any kernel). This code has been adapted from the Java port of the original LIBLINEAR C++ sources.

Further details can be found in:

[Fan et al, 2008] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A Library for Large Linear Classification, Journal of Machine Learning Research 9(2008), 1871-1874. Software available at

The original LIBLINEAR code: http://www.csie.ntu.edu.tw/~cjlin/liblinear

The original JAVA porting (v 1.94): http://liblinear.bwaldvogel.de

Author:

Danilo Croce

Constructor Summary

Constructors

Constructor and Description
LibLinearLearningAlgorithm()
LibLinearLearningAlgorithm(double cp, double cn, String representationName)
LibLinearLearningAlgorithm(Label label, double cp, double cn, boolean fairness, String representationName)
LibLinearLearningAlgorithm(Label label, double cp, double cn, String representationName)

Method Summary

Methods

Modifier and Type	Method and Description
LibLinearLearningAlgorithm	duplicate() Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated.
double	getCn()
double	getCp()
Label	getLabel()
List<Label>	getLabels() Returns the labels representing the concept to be learned.
BinaryLinearClassifier	getPredictionFunction() Returns the classifier learned during the training process
String	getRepresentation() Returns the representation this learning algorithm exploits
boolean	isFairness()
void	learn(Dataset dataset) It starts the training process exploiting the provided dataset
void	reset() Resets all the learning process, returning to the default state.
void	setC(double c)
void	setCn(double cn)
void	setCp(double cp)
void	setFairness(boolean fairness)
void	setLabel(Label label)
void	setLabels(List<Label> labels) Sets the labels representing the concept to be learned.
void	setPredictionFunction(PredictionFunction predictionFunction) Sets the predictionFunction learned during the training process.
void	setRepresentation(String representation) Sets the representation this learning algorithm will exploit

Methods inherited from class java.lang.Object

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

Constructor Detail

LibLinearLearningAlgorithm

public LibLinearLearningAlgorithm(Label label,
                          double cp,
                          double cn,
                          String representationName)

Parameters:

label - The label to be learned

cp - The regularization parameter for positive examples

cn - The regularization parameter for negative examples

representationName - The identifier of the representation to be considered for the training step

LibLinearLearningAlgorithm

public LibLinearLearningAlgorithm(Label label,
                          double cp,
                          double cn,
                          boolean fairness,
                          String representationName)

Parameters:

label - The label to be learned

cp - The regularization parameter for positive examples

cn - The regularization parameter for negative examples

fairness - A boolean parameter to force the fairness policy

representationName - The identifier of the representation to be considered for the training step

LibLinearLearningAlgorithm

public LibLinearLearningAlgorithm(double cp,
                          double cn,
                          String representationName)

Parameters:

cp - The regularization parameter for positive examples

cn - The regularization parameter for negative examples

representationName - The identifier of the representation to be considered for the training step

LibLinearLearningAlgorithm

public LibLinearLearningAlgorithm()

Method Detail

getCp

public double getCp()

Returns:

the regularization parameter for positive examples

setCp

public void setCp(double cp)

Parameters:

cp - the regularization parameter to set for positive examples

getCn

public double getCn()

Returns:

the regularization parameter for negative examples

setCn

public void setCn(double cn)

Parameters:

cn - the regularization parameter to set for negative examples

setC

public void setC(double c)

Parameters:

c - the regularization parameter to set for both positive and negative examples

getRepresentation

public String getRepresentation()

Description copied from interface: LinearMethod

Returns the representation this learning algorithm exploits

Specified by:

getRepresentation in interface LinearMethod

Returns:

the representation

setRepresentation

public void setRepresentation(String representation)

Description copied from interface: LinearMethod

Sets the representation this learning algorithm will exploit

Specified by:

setRepresentation in interface LinearMethod

Parameters:

representation - the representation to set

isFairness

public boolean isFairness()

Returns:

True if the fairness policy is applied. False otherwise.

setFairness

public void setFairness(boolean fairness)

Parameters:

fairness - Set the boolean parameter to force the fairness policy

setLabels

public void setLabels(List<Label> labels)

Description copied from interface: LearningAlgorithm

Sets the labels representing the concept to be learned.

Specified by:

setLabels in interface BinaryLearningAlgorithm

Specified by:

setLabels in interface LearningAlgorithm

Parameters:

labels - the labels representing the concept to be learned

getLabels

public List<Label> getLabels()

Description copied from interface: LearningAlgorithm

Returns the labels representing the concept to be learned.

Specified by:

getLabels in interface BinaryLearningAlgorithm

Specified by:

getLabels in interface LearningAlgorithm

Returns:

the labels representing the concept to be learned

getLabel

public Label getLabel()

Specified by:

getLabel in interface BinaryLearningAlgorithm

Returns:

the label associated to the positive class

setLabel

public void setLabel(Label label)

Specified by:

setLabel in interface BinaryLearningAlgorithm

learn

public void learn(Dataset dataset)

Description copied from interface: LearningAlgorithm

It starts the training process exploiting the provided dataset

Specified by:

learn in interface LearningAlgorithm

Parameters:

dataset - the training data

duplicate

public LibLinearLearningAlgorithm duplicate()

Description copied from interface: LearningAlgorithm

Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated. A shallow copy of the learning parameters is performed, while the state parameters are initialized to their default value. The labels are not initialized neither copied

Specified by:

duplicate in interface LearningAlgorithm

reset

public void reset()

Description copied from interface: LearningAlgorithm

Resets all the learning process, returning to the default state. All the learned process is forgotten

Specified by:

reset in interface LearningAlgorithm

getPredictionFunction

public BinaryLinearClassifier getPredictionFunction()

Description copied from interface: ClassificationLearningAlgorithm

Returns the classifier learned during the training process

Specified by:

getPredictionFunction in interface ClassificationLearningAlgorithm

Specified by:

getPredictionFunction in interface LearningAlgorithm

Returns:

the predictionFunction learned during the training process

setPredictionFunction

public void setPredictionFunction(PredictionFunction predictionFunction)

Description copied from interface: LearningAlgorithm

Sets the predictionFunction learned during the training process. It can be used to change the default prediction function, or to set an already trained one.

Specified by:

setPredictionFunction in interface LearningAlgorithm