Package org.apache.solr.ltr.model
Class MultipleAdditiveTreesModel
- java.lang.Object
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- org.apache.solr.ltr.model.LTRScoringModel
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- org.apache.solr.ltr.model.MultipleAdditiveTreesModel
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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:
Background reading:
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Nested Class Summary
Nested Classes Modifier and Type Class Description class
MultipleAdditiveTreesModel.RegressionTree
class
MultipleAdditiveTreesModel.RegressionTreeNode
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Field Summary
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Fields inherited from class org.apache.solr.ltr.model.LTRScoringModel
features, name, norms
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description org.apache.lucene.search.Explanation
explain(org.apache.lucene.index.LeafReaderContext context, int doc, float finalScore, List<org.apache.lucene.search.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 throwsModelException
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
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Method Detail
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setTrees
public void setTrees(Object trees)
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validate
protected void validate() throws ModelException
Description copied from class:LTRScoringModel
Validate that settings make sense and throwsModelException
if they do not make sense.- Overrides:
validate
in classLTRScoringModel
- Throws:
ModelException
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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 classLTRScoringModel
- 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
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explain
public org.apache.lucene.search.Explanation explain(org.apache.lucene.index.LeafReaderContext context, int doc, float finalScore, List<org.apache.lucene.search.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 classLTRScoringModel
- Parameters:
context
- Context the document is indoc
- Document to explainfinalScore
- Original scorefeatureExplanations
- Explanations for each feature calculation- Returns:
- Explanation for the scoring of a document
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toString
public String toString()
- Overrides:
toString
in classLTRScoringModel
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