org.apache.solr.ltr.model

Class WrapperModel

java.lang.Object

org.apache.solr.ltr.model.LTRScoringModel

org.apache.solr.ltr.model.AdapterModel

org.apache.solr.ltr.model.WrapperModel

Direct Known Subclasses:

DefaultWrapperModel

public abstract class WrapperModel
extends AdapterModel

A scoring model that wraps the other model.

This model loads a model from an external resource during the initialization. The way of fetching the wrapped model is depended on the implementation of fetchModelMap().

This model doesn't hold the actual parameters of the wrapped model, thus it can manage large models which are difficult to upload to ZooKeeper.

Example configuration:

{
    "class": "...",
    "name": "myModelName",
    "params": {
        ...
    }
 }

NOTE: no "features" are configured in the wrapper model because the wrapped model's features will be used instead. Also note that if a "store" is configured for the wrapper model then it must match the "store" of the wrapped model.

Field Summary

Fields

Modifier and Type	Field and Description
protected LTRScoringModel	model

Fields inherited from class org.apache.solr.ltr.model.AdapterModel

solrResourceLoader

Fields inherited from class org.apache.solr.ltr.model.LTRScoringModel

features, name, norms

Constructor Summary

Constructors

Constructor and Description
WrapperModel(String name, List<Feature> features, List<Normalizer> norms, String featureStoreName, List<Feature> allFeatures, Map<String,Object> params)

Method Summary

Modifier and Type	Method and Description
boolean	equals(Object obj)
Explanation	explain(LeafReaderContext context, int doc, float finalScore, List<Explanation> featureExplanations) Similar to the score() function, except it returns an explanation of how the features were used to calculate the score.
abstract Map<String,Object>	fetchModelMap()
Collection<Feature>	getAllFeatures()
List<Feature>	getFeatures()
Explanation	getNormalizerExplanation(Explanation e, int idx)
List<Normalizer>	getNorms()
int	hashCode()
void	normalizeFeaturesInPlace(float[] modelFeatureValues) Goes through all the stored feature values, and calculates the normalized values for all the features that will be used for scoring.
float	score(float[] modelFeatureValuesNormalized) Given a list of normalized values for all features a scoring algorithm cares about, calculate and return a score.
String	toString()
void	updateModel(LTRScoringModel model)
protected void	validate() Validate that settings make sense and throws ModelException if they do not make sense.

Methods inherited from class org.apache.solr.ltr.model.AdapterModel

init

Methods inherited from class org.apache.solr.ltr.model.LTRScoringModel

getFeatureStoreName, getInstance, getName, getParams

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Field Detail

model

protected LTRScoringModel model

Constructor Detail

WrapperModel

public WrapperModel(String name,
                    List<Feature> features,
                    List<Normalizer> norms,
                    String featureStoreName,
                    List<Feature> allFeatures,
                    Map<String,Object> params)

Method Detail

hashCode

public int hashCode()

Overrides:

hashCode in class LTRScoringModel

equals

public boolean equals(Object obj)

Overrides:

equals in class LTRScoringModel

validate

protected void validate()
                 throws ModelException

Description copied from class: LTRScoringModel

Validate that settings make sense and throws ModelException if they do not make sense.

Overrides:

validate in class LTRScoringModel

Throws:

ModelException

updateModel

public void updateModel(LTRScoringModel model)

fetchModelMap

public abstract Map<String,Object> fetchModelMap()
                                          throws ModelException

Throws:

ModelException

getNorms

public List<Normalizer> getNorms()

Overrides:

getNorms in class LTRScoringModel

Returns:

the norms

getFeatures

public List<Feature> getFeatures()

Overrides:

getFeatures in class LTRScoringModel

Returns:

the features

getAllFeatures

public Collection<Feature> getAllFeatures()

Overrides:

getAllFeatures in class LTRScoringModel

score

public float score(float[] modelFeatureValuesNormalized)

Description copied from class: LTRScoringModel

Given a list of normalized values for all features a scoring algorithm cares about, calculate and return a score.

Specified by:

score in class LTRScoringModel

Parameters:

modelFeatureValuesNormalized - List of normalized feature values. Each feature is identified by its id, which is the index in the array

Returns:

The final score for a document

explain

public Explanation explain(LeafReaderContext context,
                           int doc,
                           float finalScore,
                           List<Explanation> featureExplanations)

Description copied from class: LTRScoringModel

Similar to the score() function, except it returns an explanation of how the features were used to calculate the score.

Specified by:

explain in class LTRScoringModel

Parameters:

context - Context the document is in

doc - Document to explain

finalScore - Original score

featureExplanations - Explanations for each feature calculation

Returns:

Explanation for the scoring of a document

normalizeFeaturesInPlace

public void normalizeFeaturesInPlace(float[] modelFeatureValues)

Description copied from class: LTRScoringModel

Goes through all the stored feature values, and calculates the normalized values for all the features that will be used for scoring.

Overrides:

normalizeFeaturesInPlace in class LTRScoringModel

getNormalizerExplanation

public Explanation getNormalizerExplanation(Explanation e,
                                            int idx)

Overrides:

getNormalizerExplanation in class LTRScoringModel

toString

public String toString()

Overrides:

toString in class LTRScoringModel