Package org.tribuo.classification.mnb

Class MultinomialNaiveBayesTrainer

java.lang.Object
org.tribuo.classification.mnb.MultinomialNaiveBayesTrainer
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, Trainer<Label>, WeightedExamples
public class MultinomialNaiveBayesTrainer extends Object implements Trainer<Label>, WeightedExamples
A Trainer which trains a multinomial Naive Bayes model with Laplace smoothing.

All feature values must be non-negative.

See: 

 Wang S, Manning CD.
 "Baselines and Bigrams: Simple, Good Sentiment and Topic Classification"
 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, 2012.

  • Constructor Details

    • MultinomialNaiveBayesTrainer

      public MultinomialNaiveBayesTrainer()
      Constructs a multinomial naive bayes trainer using a smoothing value of 1.0.

    • MultinomialNaiveBayesTrainer

      public MultinomialNaiveBayesTrainer(double alpha)
      Constructs a multinomial naive bayes trainer with the specified smoothing value.
      Parameters:
      alpha - The smoothing value.

  • Method Details

    • train

      public Model<Label> train(Dataset<Label> 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<Label>
      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 Model<Label> train(Dataset<Label> 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<Label>
      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<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>