org.apache.solr.ltr.model

Class LTRScoringModel

java.lang.Object

org.apache.solr.ltr.model.LTRScoringModel

All Implemented Interfaces:

Accountable

Direct Known Subclasses:

AdapterModel, LinearModel, MultipleAdditiveTreesModel, NeuralNetworkModel

public abstract class LTRScoringModel
extends Object
implements Accountable

A scoring model computes scores that can be used to rerank documents.

A scoring model consists of

• a list of features (Feature) and
• a list of normalizers (Normalizer) plus
• parameters or configuration to represent the scoring algorithm.

Example configuration (snippet):

{
   "class" : "...",
   "name" : "myModelName",
   "features" : [
       {
         "name" : "isBook"
       },
       {
         "name" : "originalScore",
         "norm": {
             "class" : "org.apache.solr.ltr.norm.StandardNormalizer",
             "params" : { "avg":"100", "std":"10" }
         }
       },
       {
         "name" : "price",
         "norm": {
             "class" : "org.apache.solr.ltr.norm.MinMaxNormalizer",
             "params" : { "min":"0", "max":"1000" }
         }
       }
   ],
   "params" : {
       ...
   }
}

LTRScoringModel is an abstract class and concrete classes must implement the score(float[]) and explain(LeafReaderContext, int, float, List) methods.

Field Summary

Fields

Modifier and Type	Field and Description
protected List<Feature>	features
protected String	name
protected List<Normalizer>	norms

Constructor Summary

Constructors

Constructor and Description
LTRScoringModel(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)
abstract 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.
Collection<Feature>	getAllFeatures()
List<Feature>	getFeatures()
String	getFeatureStoreName()
static LTRScoringModel	getInstance(SolrResourceLoader solrResourceLoader, String className, String name, List<Feature> features, List<Normalizer> norms, String featureStoreName, List<Feature> allFeatures, Map<String,Object> params)
String	getName()
Explanation	getNormalizerExplanation(Explanation e, int idx)
List<Normalizer>	getNorms()
Map<String,Object>	getParams()
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.
long	ramBytesUsed()
abstract 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()
protected void	validate() Validate that settings make sense and throws ModelException if they do not make sense.

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.apache.lucene.util.Accountable

getChildResources

Field Detail

name

protected final String name

features

protected final List<Feature> features

norms

protected final List<Normalizer> norms

Constructor Detail

LTRScoringModel

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

Method Detail

getInstance

public static LTRScoringModel getInstance(SolrResourceLoader solrResourceLoader,
                                          String className,
                                          String name,
                                          List<Feature> features,
                                          List<Normalizer> norms,
                                          String featureStoreName,
                                          List<Feature> allFeatures,
                                          Map<String,Object> params)
                                   throws ModelException

Throws:

ModelException

validate

protected void validate()
                 throws ModelException

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

Throws:

ModelException

getNorms

public List<Normalizer> getNorms()

Returns:

the norms

getName

public String getName()

Returns:

the name

getFeatures

public List<Feature> getFeatures()

Returns:

the features

getParams

public Map<String,Object> getParams()

hashCode

public int hashCode()

Overrides:

hashCode in class Object

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

ramBytesUsed

public long ramBytesUsed()

Specified by:

ramBytesUsed in interface Accountable

getAllFeatures

public Collection<Feature> getAllFeatures()

getFeatureStoreName

public String getFeatureStoreName()

score

public abstract float score(float[] modelFeatureValuesNormalized)

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

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 abstract 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.

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

toString

public String toString()

Overrides:

toString in class Object

normalizeFeaturesInPlace

public 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.

getNormalizerExplanation

public Explanation getNormalizerExplanation(Explanation e,
                                            int idx)