org.apache.solr.ltr.model

Class LinearModel

java.lang.Object

org.apache.solr.ltr.model.LTRScoringModel

org.apache.solr.ltr.model.LinearModel

All Implemented Interfaces:

Accountable

public class LinearModel
extends LTRScoringModel

A scoring model that computes scores using a dot product. Example models are RankSVM and Pranking.

Example configuration:

{
   "class" : "org.apache.solr.ltr.model.LinearModel",
   "name" : "myModelName",
   "features" : [
       { "name" : "userTextTitleMatch" },
       { "name" : "originalScore" },
       { "name" : "isBook" }
   ],
   "params" : {
       "weights" : {
           "userTextTitleMatch" : 1.0,
           "originalScore" : 0.5,
           "isBook" : 0.1
       }
   }
}

Training libraries:

• LIBLINEAR -- A Library for Large Linear Classification

• LIBSVM -- A Library for Support Vector Machines

Background reading:

• Thorsten Joachims. Optimizing Search Engines Using Clickthrough Data. Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2002.

• Koby Crammer and Yoram Singer. Pranking with Ranking. Advances in Neural Information Processing Systems (NIPS), 2001.

Field Summary

Fields

Modifier and Type	Field and Description
protected Float[]	featureToWeight featureToWeight is part of the LTRScoringModel params map and therefore here it does not individually influence the class hashCode, equals, etc.

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

features, name, norms

Constructor Summary

Constructors

Constructor and Description
LinearModel(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
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.
float	score(float[] modelFeatureValuesNormalized) Given a list of normalized values for all features a scoring algorithm cares about, calculate and return a score.
void	setWeights(Object weights)
String	toString()
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.LTRScoringModel

equals, getAllFeatures, getFeatures, getFeatureStoreName, getInstance, getName, getNormalizerExplanation, getNorms, getParams, hashCode, normalizeFeaturesInPlace, ramBytesUsed

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

featureToWeight

protected Float[] featureToWeight

featureToWeight is part of the LTRScoringModel params map and therefore here it does not individually influence the class hashCode, equals, etc.

Constructor Detail

LinearModel

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

Method Detail

setWeights

public void setWeights(Object weights)

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

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

toString

public String toString()

Overrides:

toString in class LTRScoringModel