Class LibLinearTrainer<T extends Output<T>>

java.lang.Object
org.tribuo.common.liblinear.LibLinearTrainer<T>
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, Trainer<T>
public abstract class LibLinearTrainer<T extends Output<T>> extends Object implements Trainer<T>
A Trainer which wraps a liblinear-java trainer.

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.

  • Field Details

    • libLinearParams

      protected de.bwaldvogel.liblinear.Parameter libLinearParams

    • trainerType

      @Config(description="Algorithm to use.") protected LibLinearType<T extends Output<T>> trainerType

    • cost

      @Config(description="Cost penalty for misclassifications.") protected double cost

    • maxIterations

      @Config(description="Maximum number of iterations before terminating.") protected int maxIterations

    • terminationCriterion

      @Config(description="Stop iterating when the loss score decreases by less than this value.") protected double terminationCriterion

    • epsilon

      @Config(description="Epsilon insensitivity in the regression cost function.") protected double epsilon

    • seed

      @Config(description="RNG seed.") protected long seed

  • Constructor Details

    • LibLinearTrainer

      protected LibLinearTrainer()
      For OLCUT

    • LibLinearTrainer

      protected LibLinearTrainer(LibLinearType<T> trainerType, double cost, int maxIterations, double terminationCriterion)
      Creates a trainer for a LibLinear model

      Uses Trainer.DEFAULT_SEED as the RNG seed, and 0.1 as epsilon.

      Parameters:
      trainerType - Loss function and optimisation method combination.
      cost - Cost penalty for each incorrectly classified training point.
      maxIterations - The maximum number of dataset iterations.
      terminationCriterion - How close does the optimisation function need to be before terminating that subproblem (usually set to 0.1).

    • LibLinearTrainer

      protected LibLinearTrainer(LibLinearType<T> trainerType, double cost, int maxIterations, double terminationCriterion, long seed)
      Creates a trainer for a LibLinear model
      Parameters:
      trainerType - Loss function and optimisation method combination.
      cost - Cost penalty for each incorrectly classified training point.
      maxIterations - The maximum number of dataset iterations.
      terminationCriterion - How close does the optimisation function need to be before terminating that subproblem (usually set to 0.1).

    • LibLinearTrainer

      protected LibLinearTrainer(LibLinearType<T> trainerType, double cost, int maxIterations, double terminationCriterion, double epsilon)
      Creates a trainer for a LibLinear model

      Uses Trainer.DEFAULT_SEED as the RNG seed.

      Parameters:
      trainerType - Loss function and optimisation method combination.
      cost - Cost penalty for each incorrectly classified training point.
      maxIterations - The maximum number of dataset iterations.
      terminationCriterion - How close does the optimisation function need to be before terminating that subproblem (usually set to 0.1).
      epsilon - The insensitivity of the regression loss to small differences.

    • LibLinearTrainer

      protected LibLinearTrainer(LibLinearType<T> trainerType, double cost, int maxIterations, double terminationCriterion, double epsilon, long seed)
      Creates a trainer for a LibLinear model
      Parameters:
      trainerType - Loss function and optimisation method combination.
      cost - Cost penalty for each incorrectly classified training point.
      maxIterations - The maximum number of dataset iterations.
      terminationCriterion - How close does the optimisation function need to be before terminating that subproblem (usually set to 0.1).
      epsilon - The insensitivity of the regression loss to small differences.
      seed - The RNG seed.

  • Method Details

    • postConfig

      public void postConfig()
      Used by the OLCUT configuration system, and should not be called by external code.
      Specified by:
      postConfig in interface com.oracle.labs.mlrg.olcut.config.Configurable

    • train

      public LibLinearModel<T> train(Dataset<T> examples)
      Specified by:
      train in interface Trainer<T extends Output<T>>

    • train

      public LibLinearModel<T> train(Dataset<T> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
      Specified by:
      train in interface Trainer<T extends Output<T>>

    • train

      public LibLinearModel<T> train(Dataset<T> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)
      Specified by:
      train in interface Trainer<T extends Output<T>>

    • getInvocationCount

      public int getInvocationCount()
      Specified by:
      getInvocationCount in interface Trainer<T extends Output<T>>

    • setInvocationCount

      public void setInvocationCount(int invocationCount)
      Specified by:
      setInvocationCount in interface Trainer<T extends Output<T>>

    • toString

      public String toString()
      Overrides:
      toString in class Object

    • trainModels

      protected abstract List<de.bwaldvogel.liblinear.Model> trainModels(de.bwaldvogel.liblinear.Parameter curParams, int numFeatures, de.bwaldvogel.liblinear.FeatureNode[][] features, double[][] outputs)
      Train all the liblinear instances necessary for this dataset.
      Parameters:
      curParams - The LibLinear parameters.
      numFeatures - The number of features in this dataset.
      features - The features themselves.
      outputs - The outputs.
      Returns:
      A list of liblinear models.

    • createModel

      protected abstract LibLinearModel<T> createModel(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<T> outputIDInfo, List<de.bwaldvogel.liblinear.Model> models)
      Construct the appropriate subtype of LibLinearModel for the prediction task.
      Parameters:
      provenance - The model provenance.
      featureIDMap - The feature id map.
      outputIDInfo - The output id info.
      models - The list of linear models.
      Returns:
      An implementation of LibLinearModel.

    • extractData

      protected abstract com.oracle.labs.mlrg.olcut.util.Pair<de.bwaldvogel.liblinear.FeatureNode[][], double[][]> extractData(Dataset<T> data, ImmutableOutputInfo<T> outputInfo, ImmutableFeatureMap featureMap)
      Extracts the features and Outputs in LibLinear's format.
      Parameters:
      data - The input data.
      outputInfo - The output info.
      featureMap - The feature info.
      Returns:
      The features and outputs.

    • setupParameters

      protected de.bwaldvogel.liblinear.Parameter setupParameters(ImmutableOutputInfo<T> info)
      Constructs the parameters. Most of the time this just clones the existing ones, but classification overrides it to incorporate label weights if they exist.
      Parameters:
      info - The output info.
      Returns:
      The Parameters to use for training.

    • exampleToNodes

      public static <T extends Output<T>> de.bwaldvogel.liblinear.FeatureNode[] exampleToNodes(Example<T> example, ImmutableFeatureMap featureIDMap, List<de.bwaldvogel.liblinear.FeatureNode> features)
      Converts a Tribuo Example into a liblinear FeatureNode array, including a bias feature.

      If there is a collision between feature ids (i.e., if there is feature hashing or some other mechanism changing the feature ids) then the feature values are summed.

      Type Parameters:
      T - The output type.
      Parameters:
      example - The input example.
      featureIDMap - The feature id map which contains the example's indices.
      features - A buffer. If null then an array list is created and used internally.
      Returns:
      The features suitable for use in liblinear.

    • getProvenance

      public TrainerProvenance getProvenance()
      Specified by:
      getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<T extends Output<T>>