Interface LabelObjective

All Superinterfaces:

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

All Known Implementing Classes:

Hinge, LogMulticlass

public interface LabelObjective
extends SGDObjective<Integer>

An interface for single label prediction objectives.

An objective knows if it generates a probabilistic model or not, and what kind of normalization needs to be applied to produce probability values.

Method Summary

Modifier and Type	Method	Description
VectorNormalizer	getNormalizer()	Generates a new VectorNormalizer which normalizes the predictions into [0,1].
boolean	isProbabilistic()	Does the objective function score probabilities or not?
default com.oracle.labs.mlrg.olcut.util.Pair<Double, SGDVector>	lossAndGradient(Integer truth, SGDVector prediction)
com.oracle.labs.mlrg.olcut.util.Pair<Double, SGDVector>	valueAndGradient(int truth, SGDVector prediction)	Deprecated. In 4.1, to migrate to the new name lossAndGradient(java.lang.Integer, org.tribuo.math.la.SGDVector).

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

postConfig

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

getProvenance

Method Details

valueAndGradient

@Deprecated
com.oracle.labs.mlrg.olcut.util.Pair<Double, SGDVector> valueAndGradient(int truth,
 SGDVector prediction)

Deprecated.

In 4.1, to migrate to the new name lossAndGradient(java.lang.Integer, org.tribuo.math.la.SGDVector).

Scores a prediction, returning the loss and a vector of per label gradients.

Parameters:

truth - The true label id.

prediction - The prediction for each label id.

Returns:

The score and per label gradient.

lossAndGradient

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

Specified by:

lossAndGradient in interface SGDObjective<Integer>

getNormalizer

VectorNormalizer getNormalizer()

Generates a new VectorNormalizer which normalizes the predictions into [0,1].

Returns:

The vector normalizer for this objective.

isProbabilistic

boolean isProbabilistic()

Does the objective function score probabilities or not?

Returns:

boolean.