it.uniroma2.sag.kelp.predictionfunction

Class SequencePredictionFunction

java.lang.Object

it.uniroma2.sag.kelp.predictionfunction.SequencePredictionFunction

All Implemented Interfaces:

PredictionFunction

public class SequencePredictionFunction
extends Object
implements PredictionFunction

This class implements a classifier in a sequence labeling process. During this labeling process, the history of an example is dynamically estimated by a classifier and the entire sequence of labels is derived through a Viterbi Decoding step combined with a Beam Search Strategy.

Author:

Danilo Croce

Field Summary

Fields

Modifier and Type	Field and Description
static int	DEFAULT_BEAM_SIZE
static int	DEFAULT_MAX_EMISSION_CAND

Constructor Summary

Constructors

Constructor and Description
SequencePredictionFunction()
SequencePredictionFunction(SequenceModel model)

Method Summary

Methods

Modifier and Type	Method and Description
int	getBeamSize()
List<Label>	getLabels() Returns the labels representing the concept to be predicted.
int	getMaxEmissionCandidates()
SequenceModel	getModel() Returns the model
SequencePrediction	predict(Example example)
void	reset() Resets all the predictor parameters to the default state.
void	setBeamSize(int beamSize)
void	setLabels(List<Label> labels) Sets the labels representing the concept to be predicted.
void	setMaxEmissionCandidates(int maxEmissionCandidates)
void	setModel(Model model) Sets the model

Methods inherited from class java.lang.Object

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

Field Detail

DEFAULT_MAX_EMISSION_CAND

public static final int DEFAULT_MAX_EMISSION_CAND

See Also:

Constant Field Values

DEFAULT_BEAM_SIZE

public static final int DEFAULT_BEAM_SIZE

See Also:

Constant Field Values

Constructor Detail

SequencePredictionFunction

public SequencePredictionFunction()

SequencePredictionFunction

public SequencePredictionFunction(SequenceModel model)

Parameters:

model - The model produced by a SequenceClassificationLearningAlgorithm

Method Detail

getBeamSize

public int getBeamSize()

Returns:

The size of the beam to be used in the decoding process. This number determines the number of possible sequences produced in the labeling process. It will also increase the process complexity.

getLabels

public List<Label> getLabels()

Description copied from interface: PredictionFunction

Returns the labels representing the concept to be predicted.

Specified by:

getLabels in interface PredictionFunction

Returns:

the labels representing the concept to be predicted

getMaxEmissionCandidates

public int getMaxEmissionCandidates()

Returns:

During the labeling process, each item is classified with respect to the target classes. To reduce the complexity of the labeling process, the returned variable determines the number of classes that received the highest classification scores to be considered after the classification step in the Viterbi Decoding.

getModel

public SequenceModel getModel()

Description copied from interface: PredictionFunction

Returns the model

Specified by:

getModel in interface PredictionFunction

Returns:

model the model

predict

public SequencePrediction predict(Example example)

Specified by:

predict in interface PredictionFunction

reset

public void reset()

Description copied from interface: PredictionFunction

Resets all the predictor parameters to the default state. All the learned process is forgotten

Specified by:

reset in interface PredictionFunction

setBeamSize

public void setBeamSize(int beamSize)

Parameters:

beamSize - The size of the beam to be used in the decoding process. This number determines the number of possible sequences produced in the labeling process. It will also increase the process complexity.

setLabels

public void setLabels(List<Label> labels)

Description copied from interface: PredictionFunction

Sets the labels representing the concept to be predicted.

Specified by:

setLabels in interface PredictionFunction

Parameters:

labels - the labels representing the concept to be predicted

setMaxEmissionCandidates

public void setMaxEmissionCandidates(int maxEmissionCandidates)

Parameters:

maxEmissionCandidates - During the labeling process, each item is classified with respect to the target classes. To reduce the complexity of the labeling process, this variable determines the number of classes that received the highest classification scores to be considered after the classification step in the Viterbi Decoding.

setModel

public void setModel(Model model)

Description copied from interface: PredictionFunction

Sets the model

Specified by:

setModel in interface PredictionFunction

Parameters:

model - the model to set