Package org.tribuo.hash

Class HashingTrainer<T extends Output<T>>

java.lang.Object
org.tribuo.hash.HashingTrainer<T>
Type Parameters:
T - The type of Output this trainer works with.
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, Trainer<T>
public final class HashingTrainer<T extends Output<T>> extends Object implements Trainer<T>
A Trainer which hashes the Dataset before the Model is produced. This means the model does not contain any feature names, only one way hashes of names.

It wraps another Trainer which actually performs the training.

  • Constructor Details

    • HashingTrainer

      public HashingTrainer(Trainer<T> trainer, Hasher hasher)
      Constructs a hashing trainer using the supplied parameters.
      Parameters:
      trainer - The trainer to use.
      hasher - The feature hasher to apply.

  • Method Details

    • train

      public Model<T> train(Dataset<T> dataset, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)
      This clones the Dataset, hashes each of the examples and rewrites their feature ids before passing it to the inner trainer.

      This ensures the Trainer sees the data after the collisions, and thus builds the correct size data structures.

      Specified by:
      train in interface Trainer<T extends Output<T>>
      Parameters:
      dataset - The input dataset.
      instanceProvenance - Provenance information specific to this execution of train (e.g., cross validation fold number).
      Returns:
      A trained Model.

    • train

      public Model<T> train(Dataset<T> dataset, 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<T extends Output<T>>
      Parameters:
      dataset - 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<T extends Output<T>>
      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<T extends Output<T>>
      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<T extends Output<T>>