org.apache.solr.ltr.model

Class MultipleAdditiveTreesModel

java.lang.Object

org.apache.solr.ltr.model.LTRScoringModel

org.apache.solr.ltr.model.MultipleAdditiveTreesModel

All Implemented Interfaces:

Accountable

public class MultipleAdditiveTreesModel
extends LTRScoringModel

A scoring model that computes scores based on the summation of multiple weighted trees. Example models are LambdaMART and Gradient Boosted Regression Trees (GBRT) .

Example configuration:

{
   "class" : "org.apache.solr.ltr.model.MultipleAdditiveTreesModel",
   "name" : "multipleadditivetreesmodel",
   "features":[
       { "name" : "userTextTitleMatch"},
       { "name" : "originalScore"}
   ],
   "params" : {
       "trees" : [
           {
               "weight" : "1",
               "root": {
                   "feature" : "userTextTitleMatch",
                   "threshold" : "0.5",
                   "left" : {
                       "value" : "-100"
                   },
                   "right" : {
                       "feature" : "originalScore",
                       "threshold" : "10.0",
                       "left" : {
                           "value" : "50"
                       },
                       "right" : {
                           "value" : "75"
                       }
                   }
               }
           },
           {
               "weight" : "2",
               "root" : {
                   "value" : "-10"
               }
           }
       ]
   }
}

Training libraries:

• RankLib

Background reading:

• Christopher J.C. Burges. From RankNet to LambdaRank to LambdaMART: An Overview. Microsoft Research Technical Report MSR-TR-2010-82.

• Z. Zheng, H. Zha, T. Zhang, O. Chapelle, K. Chen, and G. Sun. A General Boosting Method and its Application to Learning Ranking Functions for Web Search. Advances in Neural Information Processing Systems (NIPS), 2007.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	MultipleAdditiveTreesModel.RegressionTree
class	MultipleAdditiveTreesModel.RegressionTreeNode

Field Summary

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

features, name, norms

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

NULL_ACCOUNTABLE

Constructor Summary

Constructors

Constructor and Description
MultipleAdditiveTreesModel(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	setTrees(Object trees)
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

Constructor Detail

MultipleAdditiveTreesModel

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

Method Detail

setTrees

public void setTrees(Object trees)

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