org.tribuo.multilabel.sgd.objectives

Class BinaryCrossEntropy

java.lang.Object

org.tribuo.multilabel.sgd.objectives.BinaryCrossEntropy

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance>, SGDObjective<SGDVector>, MultiLabelObjective

public final class BinaryCrossEntropy
extends Object
implements MultiLabelObjective

A multilabel version of binary cross entropy loss which expects logits.

Generates a probabilistic model, and uses a SigmoidNormalizer.

Constructor Summary

Constructors

Constructor and Description
BinaryCrossEntropy() Constructs a BinaryCrossEntropy objective.

Method Summary

Modifier and Type	Method and Description
VectorNormalizer	getNormalizer() Generates a new VectorNormalizer which normalizes the predictions into a suitable format.
com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance	getProvenance()
boolean	isProbabilistic() Returns true.
com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector>	lossAndGradient(SGDVector truth, SGDVector prediction) Returns a Pair of Double and SGDVector representing the loss and per label gradients respectively.
double	threshold() The default prediction threshold for creating the output.
String	toString()

Methods inherited from class java.lang.Object

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

Methods inherited from interface com.oracle.labs.mlrg.olcut.config.Configurable

postConfig

Constructor Detail

BinaryCrossEntropy

public BinaryCrossEntropy()

Constructs a BinaryCrossEntropy objective.

Method Detail

lossAndGradient

public com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector> lossAndGradient(SGDVector truth,
                                                                              SGDVector prediction)

Returns a Pair of Double and SGDVector representing the loss and per label gradients respectively.

The prediction vector is transformed to produce the per label gradient and returned.

Specified by:

lossAndGradient in interface SGDObjective<SGDVector>

Parameters:

truth - The true label id

prediction - The prediction for each label id

Returns:

A Pair of the score and per label gradient.

getNormalizer

public VectorNormalizer getNormalizer()

Description copied from interface: MultiLabelObjective

Generates a new VectorNormalizer which normalizes the predictions into a suitable format.

Specified by:

getNormalizer in interface MultiLabelObjective

Returns:

The vector normalizer for this objective.

isProbabilistic

public boolean isProbabilistic()

Returns true.

Specified by:

isProbabilistic in interface MultiLabelObjective

Returns:

True.

threshold

public double threshold()

Description copied from interface: MultiLabelObjective

The default prediction threshold for creating the output.

Specified by:

threshold in interface MultiLabelObjective

Returns:

The threshold.

toString

public String toString()

Overrides:

toString in class Object

getProvenance

public com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance getProvenance()

Specified by:

getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance>