Package org.tribuo.regression.baseline

Class DummyRegressionTrainer

java.lang.Object
org.tribuo.regression.baseline.DummyRegressionTrainer
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, Trainer<Regressor>
public final class DummyRegressionTrainer extends Object implements Trainer<Regressor>
A trainer for simple baseline regressors. 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 DummyRegressionModel train(Dataset<Regressor> 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<Regressor>
      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 DummyRegressionModel train(Dataset<Regressor> 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<Regressor>
      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.

    • toString

      public String toString()
      Overrides:
      toString in class Object

    • 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<Regressor>
      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<Regressor>
      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>

    • createConstantTrainer

      public static DummyRegressionTrainer createConstantTrainer(double value)
      Creates a trainer which create models which return a fixed value.
      Parameters:
      value - The value to return
      Returns:
      A regression trainer.

    • createGaussianTrainer

      public static DummyRegressionTrainer createGaussianTrainer(long seed)
      Creates a trainer which create models which sample the output from a gaussian distribution fit to the training data.
      Parameters:
      seed - The RNG seed.
      Returns:
      A regression trainer.

    • createMeanTrainer

      public static DummyRegressionTrainer createMeanTrainer()
      Creates a trainer which create models which return the mean of the training data.
      Returns:
      A regression trainer.

    • createMedianTrainer

      public static DummyRegressionTrainer createMedianTrainer()
      Creates a trainer which create models which return the median of the training data.
      Returns:
      A regression trainer.

    • createQuartileTrainer

      public static DummyRegressionTrainer createQuartileTrainer(double value)
      Creates a trainer which create models which return the value at the specified fraction of the sorted training data.
      Parameters:
      value - The quartile value.
      Returns:
      A regression trainer.