Package org.tribuo.classification.baseline

Class DummyClassifierTrainer

java.lang.Object
org.tribuo.classification.baseline.DummyClassifierTrainer
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, Trainer<Label>
public final class DummyClassifierTrainer extends Object implements Trainer<Label>
A trainer for simple baseline classifiers. Use this only for comparison purposes, if you can't beat these baselines, your ML system doesn't work.

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

    • train

      public Model<Label> train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance, 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<Label>
      Parameters:
      examples - the data set containing the examples.
      instanceProvenance - 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<Label>
      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<Label>
      Parameters:
      invocationCount - the number of invocations of the train method to simulate

    • toString

      public String toString()
      Overrides:
      toString in class Object

    • getProvenance

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

    • createStratifiedTrainer

      public static DummyClassifierTrainer createStratifiedTrainer(long seed)
      Creates a trainer which creates models which return random labels sampled from the training label distribution.
      Parameters:
      seed - The RNG seed to use.
      Returns:
      A classification trainer.

    • createConstantTrainer

      public static DummyClassifierTrainer createConstantTrainer(String constantLabel)
      Creates a trainer which creates models which return a fixed label.
      Parameters:
      constantLabel - The label to return.
      Returns:
      A classification trainer.

    • createUniformTrainer

      public static DummyClassifierTrainer createUniformTrainer(long seed)
      Creates a trainer which creates models which return random labels sampled uniformly from the labels seen at training time.
      Parameters:
      seed - The RNG seed to use.
      Returns:
      A classification trainer.

    • createMostFrequentTrainer

      public static DummyClassifierTrainer createMostFrequentTrainer()
      Creates a trainer which creates models which return a fixed label, the one which was most frequent in the training data.
      Returns:
      A classification trainer.