org.tribuo.regression.slm

Class SparseLinearModel

java.lang.Object

org.tribuo.Model<T>

org.tribuo.SparseModel<Regressor>

org.tribuo.regression.impl.SkeletalIndependentRegressionSparseModel

org.tribuo.regression.slm.SparseLinearModel

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.provenance.Provenancable<ModelProvenance>, Serializable

public class SparseLinearModel
extends SkeletalIndependentRegressionSparseModel

The inference time version of a sparse linear regression model.

The type of the model depends on the trainer used.

See Also:

Serialized Form

Field Summary

Fields inherited from class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseModel

dimensions

Fields inherited from class org.tribuo.Model

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

Method Summary

Modifier and Type	Method and Description
protected Model<Regressor>	copy(String newName, ModelProvenance newProvenance) Copies a model, replacing it's provenance and name with the supplied values.
protected SparseVector	createFeatures(Example<Regressor> example) Creates the feature vector.
Optional<Excuse<Regressor>>	getExcuse(Example<Regressor> example) Generates an excuse for an example.
Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>>	getTopFeatures(int n) Gets the top n features associated with this model.
Map<String,SparseVector>	getWeights() Gets a copy of the model parameters.
protected Regressor.DimensionTuple	scoreDimension(int dimensionIdx, SparseVector features) Makes a prediction for a single dimension.

Methods inherited from class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseModel

predict

Methods inherited from class org.tribuo.SparseModel

copy, getActiveFeatures

Methods inherited from class org.tribuo.Model

generatesProbabilities, getExcuses, getFeatureIDMap, getName, getOutputIDInfo, getProvenance, innerPredict, predict, predict, setName, toString, validate

Methods inherited from class java.lang.Object

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

Method Detail

createFeatures

protected SparseVector createFeatures(Example<Regressor> example)

Creates the feature vector. Includes a bias term if the model requires it.

Overrides:

createFeatures in class SkeletalIndependentRegressionSparseModel

Parameters:

example - The example to convert.

Returns:

The feature vector.

scoreDimension

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

Description copied from class: SkeletalIndependentRegressionSparseModel

Makes a prediction for a single dimension.

Specified by:

scoreDimension in class SkeletalIndependentRegressionSparseModel

Parameters:

dimensionIdx - The dimension index to predict.

features - The features to use.

Returns:

A single dimension prediction.

getTopFeatures

public Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>> getTopFeatures(int n)

Description copied from class: Model

Gets the top n features associated with this model.

If the model does not produce per output feature lists, it returns a map with a single element with key Model.ALL_OUTPUTS.

If the model cannot describe it's top features then it returns Collections.emptyMap().

Specified by:

getTopFeatures in class Model<Regressor>

Parameters:

n - the number of features to return. If this value is less than 0, all features should be returned for each class, unless the model cannot score it's features.

Returns:

a map from string outputs to an ordered list of pairs of feature names and weights associated with that feature in the model

getExcuse

public Optional<Excuse<Regressor>> getExcuse(Example<Regressor> example)

Description copied from class: Model

Generates an excuse for an example.

This attempts to explain a classification result. Generating an excuse may be quite an expensive operation.

This excuse either contains per class information or an entry with key Model.ALL_OUTPUTS.

The optional is empty if the model does not provide excuses.

Specified by:

getExcuse in class Model<Regressor>

Parameters:

example - The input example.

Returns:

An optional excuse object. The optional is empty if this model does not provide excuses.

copy

protected Model<Regressor> copy(String newName,
                                ModelProvenance newProvenance)

Description copied from class: Model

Copies a model, replacing it's provenance and name with the supplied values.

Used to provide the provenance removal functionality.

Specified by:

copy in class Model<Regressor>

Parameters:

newName - The new name.

newProvenance - The new provenance.

Returns:

A copy of the model.

getWeights

public Map<String,SparseVector> getWeights()

Gets a copy of the model parameters.

Returns:

A map from the dimension name to the model parameters.