Class CRFParameters

java.lang.Object
org.tribuo.classification.sgd.crf.CRFParameters
All Implemented Interfaces:
Serializable, Parameters, org.tribuo.protos.ProtoSerializable<org.tribuo.math.protos.ParametersProto>
public class CRFParameters extends Object implements Parameters, Serializable
A Parameters for training a CRF using SGD.
See Also:

  • Field Details

  • Method Details

    • deserializeFromProto

      public static CRFParameters deserializeFromProto(int version, String className, com.google.protobuf.Any message) throws com.google.protobuf.InvalidProtocolBufferException
      Deserialization factory.
      Parameters:
      version - The serialized object version.
      className - The class name.
      message - The serialized data.
      Returns:
      The deserialized object.
      Throws:
      com.google.protobuf.InvalidProtocolBufferException - If the protobuf could not be parsed from the message.

    • serialize

      public org.tribuo.math.protos.ParametersProto serialize()
      Specified by:
      serialize in interface org.tribuo.protos.ProtoSerializable<org.tribuo.math.protos.ParametersProto>

    • getFeatureWeights

      public DenseVector getFeatureWeights(int id)
      Gets a copy of the weights for the specified label id.
      Parameters:
      id - The label id.
      Returns:
      The feature weights.

    • getBias

      public double getBias(int id)
      Returns the bias for the specified label id.
      Parameters:
      id - The label id.
      Returns:
      The bias.

    • getWeight

      public double getWeight(int labelID, int featureID)
      Returns the feature/label weight for the specified feature and label id.
      Parameters:
      labelID - The label id.
      featureID - The feature id.
      Returns:
      The feature/label weight.

    • getLocalScores

      public DenseVector[] getLocalScores(SGDVector[] features)
      Generate the local scores (i.e., the linear classifier for each token).
      Parameters:
      features - An array of SGDVectors, one per token.
      Returns:
      An array of DenseVectors representing the scores per label for each token.

    • getCliqueValues

      public ChainHelper.ChainCliqueValues getCliqueValues(SGDVector[] features)
      Generates the local scores and tuples them with the label - label transition weights.
      Parameters:
      features - The per token SGDVector of features.
      Returns:
      A tuple containing the array of DenseVector scores and the label - label transition weights.

    • predict

      public int[] predict(SGDVector[] features)
      Generate a prediction using Viterbi.
      Parameters:
      features - The per token SGDVector of features.
      Returns:
      An int array giving the predicted label per token.

    • predictMarginals

      public DenseVector[] predictMarginals(SGDVector[] features)
      Generate a prediction using Belief Propagation.
      Parameters:
      features - The per token SGDVector of features.
      Returns:
      A DenseVector per token containing the marginal distribution over labels.

    • predictConfidenceUsingCBP

      public List<Double> predictConfidenceUsingCBP(SGDVector[] features, List<Chunk> chunks)
      This predicts per chunk confidence using the constrained forward backward algorithm from Culotta and McCallum 2004.

      Runs one pass of BP to get the normalizing constant, and then a further chunks.size() passes of constrained forward backward.

      Parameters:
      features - The per token SGDVector of features.
      chunks - A list of extracted chunks to pin constrain the labels to.
      Returns:
      A list containing the confidence value for each chunk.

    • valueAndGradient

      public com.oracle.labs.mlrg.olcut.util.Pair<Double,Tensor[]> valueAndGradient(SGDVector[] features, int[] labels)
      Generates predictions based on the input features and labels, then scores those predictions to produce a loss for the example and a gradient update.

      Assumes all the features in this example are either SparseVector or DenseVector. Mixing the two will cause undefined behaviour.

      Parameters:
      features - The per token SGDVector of features.
      labels - The per token ground truth labels.
      Returns:
      A Pair containing the loss for this example and the associated gradient.

    • getEmptyCopy

      public Tensor[] getEmptyCopy()
      Returns a 3 element Tensor array. The first element is a DenseVector of label biases. The second element is a DenseMatrix of feature-label weights. The third element is a DenseMatrix of label-label transition weights.
      Specified by:
      getEmptyCopy in interface Parameters
      Returns:
      A Tensor array.

    • get

      public Tensor[] get()
      Specified by:
      get in interface Parameters

    • set

      public void set(Tensor[] newWeights)
      Specified by:
      set in interface Parameters

    • update

      public void update(Tensor[] gradients)
      Specified by:
      update in interface Parameters

    • merge

      public Tensor[] merge(Tensor[][] gradients, int size)
      Specified by:
      merge in interface Parameters