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
public class LibLinearRegressionModel extends LibLinearModel<Regressor>
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

    • 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 it's 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.