Class CCEnsembleTrainer

java.lang.Object
org.tribuo.multilabel.ensemble.CCEnsembleTrainer
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, Trainer<MultiLabel>
public final class CCEnsembleTrainer extends Object implements Trainer<MultiLabel>
A trainer for an ensemble of randomly ordered Classifier Chains.

This ensemble is useful if there is no a-priori knowledge of the label dependence structure, as it averages over many possible structures. In addition, ensembling is frequently a powerful technique for improving general classification performance.

ClassifierChainTrainer for more details on classifier chains.

See: 

 Read, J., Pfahringer, B., Holmes, G., & Frank, E.
 "Classifier Chains for Multi-Label Classification"
 Machine Learning, pages 333-359, 2011.

  • Constructor Details

    • CCEnsembleTrainer

      public CCEnsembleTrainer(Trainer<Label> innerTrainer, int numMembers, long seed)
      Constructs a classifier chain ensemble trainer.
      Parameters:
      innerTrainer - The trainer to use to train each chain model.
      numMembers - The number of chains to train.
      seed - The RNG seed.

  • Method Details

    • postConfig

      public void postConfig() throws com.oracle.labs.mlrg.olcut.config.PropertyException
      Specified by:
      postConfig in interface com.oracle.labs.mlrg.olcut.config.Configurable
      Throws:
      com.oracle.labs.mlrg.olcut.config.PropertyException

    • train

      public WeightedEnsembleModel<MultiLabel> train(Dataset<MultiLabel> examples)
      Description copied from interface: Trainer
      Trains a predictive model using the examples in the given data set.
      Specified by:
      train in interface Trainer<MultiLabel>
      Parameters:
      examples - the data set containing the examples.
      Returns:
      a predictive model that can be used to generate predictions for new examples.

    • train

      public WeightedEnsembleModel<MultiLabel> train(Dataset<MultiLabel> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
      Description copied from interface: Trainer
      Trains a predictive model using the examples in the given data set.
      Specified by:
      train in interface Trainer<MultiLabel>
      Parameters:
      examples - the data set containing the examples.
      runProvenance - Training run specific provenance (e.g., fold number).
      Returns:
      a predictive model that can be used to generate predictions for new examples.

    • train

      public WeightedEnsembleModel<MultiLabel> train(Dataset<MultiLabel> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)
      Description copied from interface: Trainer
      Trains a predictive model using the examples in the given data set.
      Specified by:
      train in interface Trainer<MultiLabel>
      Parameters:
      examples - the data set containing the examples.
      runProvenance - Training run specific provenance (e.g., fold number).
      invocationCount - The invocation counter that the trainer should be set to before training, which in most cases alters the state of the RNG inside this trainer. If the value is set to Trainer.INCREMENT_INVOCATION_COUNT then the invocation count is not changed.
      Returns:
      a predictive model that can be used to generate predictions for new examples.

    • getInvocationCount

      public int getInvocationCount()
      Description copied from interface: Trainer
      The number of times this trainer instance has had it's train method invoked.

      This is used to determine how many times the trainer's RNG has been accessed to ensure replicability in the random number stream.

      Specified by:
      getInvocationCount in interface Trainer<MultiLabel>
      Returns:
      The number of train invocations.

    • setInvocationCount

      public void setInvocationCount(int invocationCount)
      Description copied from interface: Trainer
      Set the internal state of the trainer to the provided number of invocations of the train method.

      This is used when reproducing a Tribuo-trained model by setting the state of the RNG to what it was at when Tribuo trained the original model by simulating invocations of the train method. This method should ALWAYS be overridden, and the default method is purely for compatibility.

      In a future major release this default implementation will be removed.

      Specified by:
      setInvocationCount in interface Trainer<MultiLabel>
      Parameters:
      invocationCount - the number of invocations of the train method to simulate

    • getProvenance

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