Package org.tribuo.regression.slm

Class ElasticNetCDTrainer

java.lang.Object
org.tribuo.regression.slm.ElasticNetCDTrainer
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, SparseTrainer<Regressor>, Trainer<Regressor>
public class ElasticNetCDTrainer extends Object implements SparseTrainer<Regressor>
An ElasticNet trainer that uses co-ordinate descent. Modelled after scikit-learn's sparse matrix implementation. Each output dimension is trained independently.

See: 

 Friedman J, Hastie T, Tibshirani R.
 "Regularization Paths for Generalized Linear Models via Coordinate Descent"
 Journal of Statistical Software, 2010

  • Constructor Details

    • ElasticNetCDTrainer

      public ElasticNetCDTrainer(double alpha, double l1Ratio)
      Constructs an elastic net trainer using the supplied parameters, with a tolerance of 1e-4, max iterations of 500, randomising the feature choice and using Trainer.DEFAULT_SEED as the RNG seed.
      Parameters:
      alpha - The regularisation strength.
      l1Ratio - The ratio between the l1 and l2 penalties.

    • ElasticNetCDTrainer

      public ElasticNetCDTrainer(double alpha, double l1Ratio, long seed)
      Constructs an elastic net trainer using the supplied parameters, with a tolerance of 1e-4, max iterations of 500, and randomising the feature choice.
      Parameters:
      alpha - The regularisation strength.
      l1Ratio - The ratio between the l1 and l2 penalties.
      seed - The RNG seed.

    • ElasticNetCDTrainer

      public ElasticNetCDTrainer(double alpha, double l1Ratio, double tolerance, int maxIterations, boolean randomise, long seed)
      Constructs an elastic net trainer using the supplied parameters.
      Parameters:
      alpha - The regularisation strength.
      l1Ratio - The ratio between the l1 and l2 penalties.
      tolerance - The convergence tolerance.
      maxIterations - The maximum number of iterations.
      randomise - Randomise the feature choice order.
      seed - The RNG seed.

  • 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 SparseModel<Regressor> train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
      Description copied from interface: SparseTrainer
      Trains a sparse predictive model using the examples in the given data set.
      Specified by:
      train in interface SparseTrainer<Regressor>
      Specified by:
      train in interface Trainer<Regressor>
      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 SparseModel<Regressor> train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)
      Description copied from interface: SparseTrainer
      Trains a predictive model using the examples in the given data set.
      Specified by:
      train in interface SparseTrainer<Regressor>
      Specified by:
      train in interface Trainer<Regressor>
      Parameters:
      examples - the data set containing the examples.
      runProvenance - Training run specific provenance (e.g., fold number).
      invocationCount - The state of the RNG the trainer should be set to before training
      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<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

    • 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>