Package smile.sequence

Class CRFLabeler<T>

java.lang.Object

smile.sequence.CRFLabeler<T>

All Implemented Interfaces:

Serializable, SequenceLabeler<T>

public class CRFLabeler<T>
extends Object
implements SequenceLabeler<T>

First-order CRF sequence labeler.

See Also:

• Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
final Function<T,Tuple>	features	The feature function.
final CRF	model	The CRF model.

Constructor Summary

Constructors

Constructor	Description
CRFLabeler(CRF model, Function<T,Tuple> features)	Constructor.

Method Summary

Modifier and Type	Method	Description
static <T> CRFLabeler<T>	fit(T[][] sequences, int[][] labels, Function<T,Tuple> features)	Fits a CRF model.
static <T> CRFLabeler<T>	fit(T[][] sequences, int[][] labels, Function<T,Tuple> features, int ntrees, int maxDepth, int maxNodes, int nodeSize, double shrinkage)	Fits a CRF.
static <T> CRFLabeler<T>	fit(T[][] sequences, int[][] labels, Function<T,Tuple> features, Properties params)	Fits a CRF model.
int[]	predict(T[] o)	Returns the most likely label sequence given the feature sequence by the forward-backward algorithm.
String	toString()
int[]	viterbi(T[] o)	Labels sequence with Viterbi algorithm.

Methods inherited from class java.lang.Object

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

Field Details

model

public final CRF model

The CRF model.

features

public final Function<T,Tuple> features

The feature function.

Constructor Details

CRFLabeler

public CRFLabeler(CRF model,
 Function<T,Tuple> features)

Constructor.

Parameters:

model - the CRF model.

features - the feature function.

Method Details

fit

public static <T> CRFLabeler<T> fit(T[][] sequences,
 int[][] labels,
 Function<T,Tuple> features)

Fits a CRF model.

Type Parameters:

T - the data type of observations.

Parameters:

sequences - the training data.

labels - the training sequence labels.

features - the feature function.

Returns:

the model.

fit

public static <T> CRFLabeler<T> fit(T[][] sequences,
 int[][] labels,
 Function<T,Tuple> features,
 Properties params)

Fits a CRF model.

Type Parameters:

T - the data type of observations.

Parameters:

sequences - the training data.

labels - the training sequence labels.

features - the feature function.

params - the hyper-parameters.

Returns:

the model.

fit

public static <T> CRFLabeler<T> fit(T[][] sequences,
 int[][] labels,
 Function<T,Tuple> features,
 int ntrees,
 int maxDepth,
 int maxNodes,
 int nodeSize,
 double shrinkage)

Fits a CRF.

Type Parameters:

T - the data type of observations.

Parameters:

sequences - the observation sequences.

labels - the state labels of observations, of which states take values in [0, k), where k is the number of hidden states.

features - the feature function.

ntrees - the number of trees/iterations.

maxDepth - the maximum depth of the tree.

maxNodes - the maximum number of leaf nodes in the tree.

nodeSize - the number of instances in a node below which the tree will not split, setting nodeSize = 5 generally gives good results.

shrinkage - the shrinkage parameter in (0, 1] controls the learning rate of procedure.

Returns:

the model.

toString

public String toString()

Overrides:

toString in class Object

predict

public int[] predict(T[] o)

Returns the most likely label sequence given the feature sequence by the forward-backward algorithm.

Specified by:

predict in interface SequenceLabeler<T>

Parameters:

o - the observation sequence.

Returns:

the most likely state sequence.

viterbi

public int[] viterbi(T[] o)

Labels sequence with Viterbi algorithm. Viterbi algorithm returns the whole sequence label that has the maximum probability, which makes sense in applications (e.g.part-of-speech tagging) that require coherent sequential labeling. The forward-backward algorithm labels a sequence by individual prediction on each position. This usually produces better accuracy although the results may not be coherent.

Parameters:

o - the observation sequence.

Returns:

the sequence labels.