Package smile.sequence

Class CRF

java.lang.Object
smile.sequence.CRF
All Implemented Interfaces:
Serializable
public class CRF extends Object implements Serializable
First-order linear conditional random field. A conditional random field is a type of discriminative undirected probabilistic graphical model. It is most often used for labeling or parsing of sequential data.

A CRF is a Markov random field that was trained discriminatively. Therefore, it is not necessary to model the distribution over always observed variables, which makes it possible to include arbitrarily complicated features of the observed variables into the model.

This class implements an algorithm that trains CRFs via gradient tree boosting. In tree boosting, the CRF potential functions are represented as weighted sums of regression trees, which provide compact representations of feature interactions. So the algorithm does not explicitly consider the potentially large parameter space. As a result, gradient tree boosting scales linearly in the order of the Markov model and in the order of the feature interactions, rather than exponentially as in previous algorithms based on iterative scaling and gradient descent.

References

  1. J. Lafferty, A. McCallum and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. ICML, 2001.
  2. Thomas G. Dietterich, Guohua Hao, and Adam Ashenfelter. Gradient Tree Boosting for Training Conditional Random Fields. JMLR, 2008.
See Also:

  • Nested Class Summary

    Nested Classes
    Modifier and Type
    Class
    Description
    static final record 
    CRF.Options
    CRF hyperparameters.

  • Constructor Summary

    Constructors
    Constructor
    Description
    CRF(StructType schema, RegressionTree[][] potentials, double shrinkage)
    Constructor.

  • Method Summary

    Modifier and Type
    Method
    Description
    static CRF
    fit(Tuple[][] sequences, int[][] labels)
    Fits a CRF model.
    static CRF
    fit(Tuple[][] sequences, int[][] labels, CRF.Options options)
    Fits a CRF model.
    int[]
    predict(Tuple[] x)
    Returns the most likely label sequence given the feature sequence by the forward-backward algorithm.
    int[]
    viterbi(Tuple[] x)
    Labels sequence with Viterbi algorithm.

    Methods inherited from class java.lang.Object

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

  • Constructor Details

    • CRF

      public CRF(StructType schema, RegressionTree[][] potentials, double shrinkage)
      Constructor.
      Parameters:
      schema - the schema of features.
      potentials - the potential functions.
      shrinkage - the learning rate.

  • Method Details

    • viterbi

      public int[] viterbi(Tuple[] x)
      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:
      x - the sequence.
      Returns:
      the sequence labels.

    • predict

      public int[] predict(Tuple[] x)
      Returns the most likely label sequence given the feature sequence by the forward-backward algorithm.
      Parameters:
      x - a sequence.
      Returns:
      the most likely label sequence.

    • fit

      public static CRF fit(Tuple[][] sequences, int[][] labels)
      Fits a CRF model.
      Parameters:
      sequences - the training data.
      labels - the training sequence labels.
      Returns:
      the model.

    • fit

      public static CRF fit(Tuple[][] sequences, int[][] labels, CRF.Options options)
      Fits a CRF model.
      Parameters:
      sequences - the training data.
      labels - the training sequence labels.
      options - the hyperparameters.
      Returns:
      the model.