Package org.tribuo.regression.impl

Class SkeletalIndependentRegressionSparseModel

java.lang.Object

org.tribuo.Model<T>

org.tribuo.SparseModel<Regressor>

org.tribuo.regression.impl.SkeletalIndependentRegressionSparseModel

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.provenance.Provenancable<ModelProvenance>, Serializable, ProtoSerializable<org.tribuo.protos.core.ModelProto>

Direct Known Subclasses:

SparseLinearModel

public abstract class SkeletalIndependentRegressionSparseModel
extends SparseModel<Regressor>

A SparseModel which wraps n independent regression models, where n is the size of the MultipleRegressor domain. Each model independently predicts a single regression dimension.

See Also:

• Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
protected final String[]	dimensions	The output dimension names.

Fields inherited from class org.tribuo.Model

ALL_OUTPUTS, BIAS_FEATURE, featureIDMap, generatesProbabilities, name, outputIDInfo, provenance, provenanceOutput

Fields inherited from interface org.tribuo.protos.ProtoSerializable

DESERIALIZATION_METHOD_NAME, PROVENANCE_SERIALIZER

Constructor Summary

Constructors

Modifier	Constructor	Description
protected	SkeletalIndependentRegressionSparseModel(String name, String[] dimensions, ModelProvenance modelProvenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Regressor> outputInfo, Map<String,List<String>> activeFeatures)	models.size() must equal labelInfo.getDomain().size()

Method Summary

Modifier and Type	Method	Description
protected SparseVector	createFeatures(Example<Regressor> example)	Creates the feature vector.
Prediction<Regressor>	predict(Example<Regressor> example)	Uses the model to predict the output for a single example.
protected abstract Regressor.DimensionTuple	scoreDimension(int dimensionIdx, SparseVector features)	Makes a prediction for a single dimension.

Methods inherited from class org.tribuo.SparseModel

copy, getActiveFeatures

Methods inherited from class org.tribuo.Model

castModel, copy, createDataCarrier, deserialize, deserializeFromFile, deserializeFromStream, generatesProbabilities, getExcuse, getExcuses, getFeatureIDMap, getName, getOutputIDInfo, getProvenance, getTopFeatures, innerPredict, predict, predict, serialize, serializeToFile, serializeToStream, setName, toString, validate

Methods inherited from class java.lang.Object

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

Field Details

dimensions

protected final String[] dimensions

The output dimension names.

Constructor Details

SkeletalIndependentRegressionSparseModel

protected SkeletalIndependentRegressionSparseModel(String name,
 String[] dimensions,
 ModelProvenance modelProvenance,
 ImmutableFeatureMap featureMap,
 ImmutableOutputInfo<Regressor> outputInfo,
 Map<String,List<String>> activeFeatures)

models.size() must equal labelInfo.getDomain().size()

Parameters:

name - Model name.

dimensions - Dimension names.

modelProvenance - The model provenance.

featureMap - The feature domain used in training.

outputInfo - The output domain used in training.

activeFeatures - The active features in this model.

Method Details

predict

public Prediction<Regressor> predict(Example<Regressor> example)

Description copied from class: Model

Uses the model to predict the output for a single example.

predict does not mutate the example.

Throws IllegalArgumentException if the example has no features or no feature overlap with the model.

Specified by:

predict in class Model<Regressor>

Parameters:

example - the example to predict.

Returns:

the result of the prediction.

createFeatures

protected SparseVector createFeatures(Example<Regressor> example)

Creates the feature vector. Does not include a bias term.

Designed to be overridden, called by the predict method.

Parameters:

example - The example to convert.

Returns:

The feature vector.

scoreDimension

protected abstract Regressor.DimensionTuple scoreDimension(int dimensionIdx,
 SparseVector features)

Makes a prediction for a single dimension.

Parameters:

dimensionIdx - The dimension index to predict.

features - The features to use.

Returns:

A single dimension prediction.