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
 

Field Summary

Fields inherited from interface org.tribuo.Trainer

DEFAULT_SEED

Constructor Summary

Constructors

Constructor and Description
ElasticNetCDTrainer(double alpha, double l1Ratio)
ElasticNetCDTrainer(double alpha, double l1Ratio, double tolerance, int maxIterations, boolean randomise, long seed)
ElasticNetCDTrainer(double alpha, double l1Ratio, long seed)

Method Summary

Modifier and Type	Method and Description
int	getInvocationCount() The number of times this trainer instance has had it's train method invoked.
TrainerProvenance	getProvenance()
void	postConfig()
String	toString()
SparseModel<Regressor>	train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) Trains a sparse predictive model using the examples in the given data set.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.tribuo.SparseTrainer

train

Constructor Detail

ElasticNetCDTrainer

public ElasticNetCDTrainer(double alpha,
                           double l1Ratio)

ElasticNetCDTrainer

public ElasticNetCDTrainer(double alpha,
                           double l1Ratio,
                           long seed)

ElasticNetCDTrainer

public ElasticNetCDTrainer(double alpha,
                           double l1Ratio,
                           double tolerance,
                           int maxIterations,
                           boolean randomise,
                           long seed)

Method Detail

postConfig

public void postConfig()

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.

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.

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>