org.tribuo.regression.impl

Class SkeletalIndependentRegressionSparseTrainer<T>

java.lang.Object

org.tribuo.regression.impl.SkeletalIndependentRegressionSparseTrainer<T>

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, SparseTrainer<Regressor>, Trainer<Regressor>

public abstract class SkeletalIndependentRegressionSparseTrainer<T>
extends Object
implements SparseTrainer<Regressor>

Base class for training n independent sparse models, one per dimension. Generates the SparseVectors once to reduce allocation.

Then wraps them in an SkeletalIndependentRegressionSparseModel to provide a Regressor prediction.

It trains each model sequentially, and could be optimised to train in parallel.

Field Summary

Fields inherited from interface org.tribuo.Trainer

DEFAULT_SEED

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	SkeletalIndependentRegressionSparseTrainer() for olcut.

Method Summary

Modifier and Type	Method and Description
protected abstract SkeletalIndependentRegressionSparseModel	createModel(Map<String,T> models, ModelProvenance provenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Regressor> outputInfo) Constructs the appropriate subclass of SkeletalIndependentRegressionModel for this trainer.
int	getInvocationCount() The number of times this trainer instance has had it's train method invoked.
protected abstract String	getModelClassName() Returns the class name of the model that this class produces.
void	postConfig() Used by the OLCUT configuration system, and should not be called by external code.
SkeletalIndependentRegressionSparseModel	train(Dataset<Regressor> examples) Trains a sparse predictive model using the examples in the given data set.
SkeletalIndependentRegressionSparseModel	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.
protected abstract T	trainDimension(double[] outputs, SparseVector[] features, float[] weights, SplittableRandom rng) Trains a single dimension of the possibly multiple dimensions.
protected abstract boolean	useBias() Returns true if the SparseVector should be constructed with a bias feature.

Methods inherited from class java.lang.Object

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

Methods inherited from interface com.oracle.labs.mlrg.olcut.provenance.Provenancable

getProvenance

Constructor Detail

SkeletalIndependentRegressionSparseTrainer

protected SkeletalIndependentRegressionSparseTrainer()

for olcut.

Method Detail

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 SkeletalIndependentRegressionSparseModel train(Dataset<Regressor> examples)

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.

Returns:

A sparse predictive model that can be used to generate predictions for new examples.

train

public SkeletalIndependentRegressionSparseModel 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.

createModel

protected abstract SkeletalIndependentRegressionSparseModel createModel(Map<String,T> models,
                                                                        ModelProvenance provenance,
                                                                        ImmutableFeatureMap featureMap,
                                                                        ImmutableOutputInfo<Regressor> outputInfo)

Constructs the appropriate subclass of SkeletalIndependentRegressionModel for this trainer.

Parameters:

models - The models to use.

provenance - The model provenance

featureMap - The feature map.

outputInfo - The regression info.

Returns:

A subclass of IndependentRegressionModel.

trainDimension

protected abstract T trainDimension(double[] outputs,
                                    SparseVector[] features,
                                    float[] weights,
                                    SplittableRandom rng)

Trains a single dimension of the possibly multiple dimensions.

Parameters:

outputs - The regression targets for this dimension.

features - The features.

weights - The example weights.

rng - The RNG to use.

Returns:

An object representing the model.

useBias

protected abstract boolean useBias()

Returns true if the SparseVector should be constructed with a bias feature.

Returns:

True if the trainer needs a bias.

getModelClassName

protected abstract String getModelClassName()

Returns the class name of the model that this class produces.

Returns:

The class name of the model.