Class LibLinearRegressionModel

java.lang.Object
org.tribuo.Model<Regressor>
org.tribuo.common.liblinear.LibLinearModel<Regressor>
org.tribuo.regression.liblinear.LibLinearRegressionModel
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.provenance.Provenancable<ModelProvenance>, Serializable, ONNXExportable, ProtoSerializable<org.tribuo.protos.core.ModelProto>
public class LibLinearRegressionModel extends LibLinearModel<Regressor> implements ONNXExportable
A Model which wraps a LibLinear-java model.

It disables the LibLinear debug output as it's very chatty.

It contains an independent liblinear model for each regression dimension.

See: 

 Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ.
 "LIBLINEAR: A library for Large Linear Classification"
 Journal of Machine Learning Research, 2008.
and for the original algorithm: 
 Cortes C, Vapnik V.
 "Support-Vector Networks"
 Machine Learning, 1995.
See Also:

  • Method Details

    • deserializeFromProto

      public static LibLinearRegressionModel 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.

    • predict

      public Prediction<Regressor> predict(Example<Regressor> example)
      Description copied from class: Model
      Uses the model to predict the output for a single example.

      predict does not mutate the example.

      Throws IllegalArgumentException if the example has no features or no feature overlap with the model.

      Specified by:
      predict in class Model<Regressor>
      Parameters:
      example - the example to predict.
      Returns:
      the result of the prediction.

    • getTopFeatures

      public Map<String, List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>> getTopFeatures(int n)
      Description copied from class: Model
      Gets the top n features associated with this model.

      If the model does not produce per output feature lists, it returns a map with a single element with key Model.ALL_OUTPUTS.

      If the model cannot describe it's top features then it returns Collections.emptyMap().

      Specified by:
      getTopFeatures in class Model<Regressor>
      Parameters:
      n - the number of features to return. If this value is less than 0, all features should be returned for each class, unless the model cannot score it's features.
      Returns:
      a map from string outputs to an ordered list of pairs of feature names and weights associated with that feature in the model

    • copy

      protected LibLinearRegressionModel copy(String newName, ModelProvenance newProvenance)
      Description copied from class: Model
      Copies a model, replacing its provenance and name with the supplied values.

      Used to provide the provenance removal functionality.

      Specified by:
      copy in class Model<Regressor>
      Parameters:
      newName - The new name.
      newProvenance - The new provenance.
      Returns:
      A copy of the model.

    • getFeatureWeights

      protected double[][] getFeatureWeights()
      Description copied from class: LibLinearModel
      Extracts the feature weights from the models. The first dimension corresponds to the model index.
      Specified by:
      getFeatureWeights in class LibLinearModel<Regressor>
      Returns:
      The feature weights.

    • innerGetExcuse

      protected Excuse<Regressor> innerGetExcuse(Example<Regressor> e, double[][] allFeatureWeights)
      The call to model.getFeatureWeights in the public methods copies the weights array so this inner method exists to save the copy in getExcuses.

      If it becomes a problem then we could cache the feature weights in the model.

      Specified by:
      innerGetExcuse in class LibLinearModel<Regressor>
      Parameters:
      e - The example.
      allFeatureWeights - The feature weights.
      Returns:
      An excuse for this example.

    • exportONNXModel

      public ai.onnx.proto.OnnxMl.ModelProto exportONNXModel(String domain, long modelVersion)
      Description copied from interface: ONNXExportable
      Exports this Model as an ONNX protobuf.
      Specified by:
      exportONNXModel in interface ONNXExportable
      Parameters:
      domain - A reverse-DNS name to namespace the model (e.g., org.tribuo.classification.sgd.linear).
      modelVersion - A version number for this model.
      Returns:
      The ONNX ModelProto representing this Tribuo Model.

    • writeONNXGraph

      public ONNXNode writeONNXGraph(ONNXRef<?> input)
      Description copied from interface: ONNXExportable
      Writes this Model into OnnxMl.GraphProto.Builder inside the input's ONNXContext.
      Specified by:
      writeONNXGraph in interface ONNXExportable
      Parameters:
      input - The input to the model graph.
      Returns:
      the output node of the model graph.