Class Similarity

Similarity defines the components of Lucene scoring.

Expert: Scoring API.

This is a low-level API, you should only extend this API if you want to implement an information retrieval model. If you are instead looking for a convenient way to alter Lucene's scoring, consider extending a higher-level implementation such as TFIDFSimilarity, which implements the vector space model with this API, or just tweaking the default implementation: DefaultSimilarity.

Similarity determines how Lucene weights terms, and Lucene interacts with this class at both index-time and query-time.

At indexing time, the indexer calls ComputeNorm(FieldInvertState), allowing the Similarity implementation to set a per-document value for the field that will be later accessible via GetNormValues(String). Lucene makes no assumption about what is in this norm, but it is most useful for encoding length normalization information.

Implementations should carefully consider how the normalization is encoded: while Lucene's classical TFIDFSimilarity encodes a combination of index-time boost and length normalization information with SmallSingle into a single byte, this might not be suitable for all purposes.

Many formulas require the use of average document length, which can be computed via a combination of SumTotalTermFreq and MaxDoc or DocCount, depending upon whether the average should reflect field sparsity.

Additional scoring factors can be stored in named NumericDocValuesFields and accessed at query-time with GetNumericDocValues(String).

Finally, using index-time boosts (either via folding into the normalization byte or via DocValues), is an inefficient way to boost the scores of different fields if the boost will be the same for every document, instead the Similarity can simply take a constant boost parameter C, and PerFieldSimilarityWrapper can return different instances with different boosts depending upon field name.

At query-time, Queries interact with the Similarity via these steps:

1. The ComputeWeight(Single, CollectionStatistics, TermStatistics[]) method is called a single time, allowing the implementation to compute any statistics (such as IDF, average document length, etc) across the entire collection. The TermStatistics and CollectionStatistics passed in already contain all of the raw statistics involved, so a Similarity can freely use any combination of statistics without causing any additional I/O. Lucene makes no assumption about what is stored in the returned Similarity.SimWeight object.
2. The query normalization process occurs a single time: GetValueForNormalization() is called for each query leaf node, QueryNorm(Single) is called for the top-level query, and finally Normalize(Single, Single) passes down the normalization value and any top-level boosts (e.g. from enclosing BooleanQuerys).
3. For each segment in the index, the Query creates a GetSimScorer(Similarity.SimWeight, AtomicReaderContext) The GetScore() method is called for each matching document.

When Explain(Query, Int32) is called, queries consult the Similarity's DocScorer for an explanation of how it computed its score. The query passes in a the document id and an explanation of how the frequency was computed.

Note

This API is experimental and might change in incompatible ways in the next release.

Inheritance

System.Object

Similarity

BM25Similarity

MultiSimilarity

PerFieldSimilarityWrapper

SimilarityBase

TFIDFSimilarity

Inherited Members

System.Object.Equals(System.Object)

System.Object.Equals(System.Object, System.Object)

System.Object.GetHashCode()

System.Object.GetType()

System.Object.MemberwiseClone()

System.Object.ReferenceEquals(System.Object, System.Object)

System.Object.ToString()

Namespace: Lucene.Net.Search.Similarities

Assembly: Lucene.Net.dll

Syntax

public abstract class Similarity

Constructors

Similarity()

Sole constructor. (For invocation by subclass constructors, typically implicit.)

Declaration

protected Similarity()

Methods

ComputeNorm(FieldInvertState)

Computes the normalization value for a field, given the accumulated state of term processing for this field (see FieldInvertState).

Matches in longer fields are less precise, so implementations of this method usually set smaller values when state.Length is large, and larger values when

state.Length

is small.

Note

This API is experimental and might change in incompatible ways in the next release.

Declaration

public abstract long ComputeNorm(FieldInvertState state)

Parameters

Type	Name	Description
FieldInvertState	state	current processing state for this field

Returns

Type	Description
System.Int64	computed norm value

ComputeWeight(Single, CollectionStatistics, TermStatistics[])

Compute any collection-level weight (e.g. IDF, average document length, etc) needed for scoring a query.

Declaration

public abstract Similarity.SimWeight ComputeWeight(float queryBoost, CollectionStatistics collectionStats, params TermStatistics[] termStats)

Parameters

Type	Name	Description
System.Single	queryBoost	the query-time boost.
CollectionStatistics	collectionStats	collection-level statistics, such as the number of tokens in the collection.
TermStatistics[]	termStats	term-level statistics, such as the document frequency of a term across the collection.

Returns

Type	Description
Similarity.SimWeight	Similarity.SimWeight object with the information this Similarity needs to score a query.

Coord(Int32, Int32)

Hook to integrate coordinate-level matching.

By default this is disabled (returns 1), as with most modern models this will only skew performance, but some implementations such as TFIDFSimilarity override this.

Declaration

public virtual float Coord(int overlap, int maxOverlap)

Parameters

Type	Name	Description
System.Int32	overlap	the number of query terms matched in the document
System.Int32	maxOverlap	the total number of terms in the query

Returns

Type	Description
System.Single	a score factor based on term overlap with the query

GetSimScorer(Similarity.SimWeight, AtomicReaderContext)

Creates a new Similarity.SimScorer to score matching documents from a segment of the inverted index.

Declaration

public abstract Similarity.SimScorer GetSimScorer(Similarity.SimWeight weight, AtomicReaderContext context)

Parameters

Type	Name	Description
Similarity.SimWeight	weight	collection information from ComputeWeight(Single, CollectionStatistics, TermStatistics[])
AtomicReaderContext	context	segment of the inverted index to be scored.

Returns

Type	Description
Similarity.SimScorer	Sloppy Similarity.SimScorer for scoring documents across context

Exceptions

Type	Condition
System.IO.IOException	if there is a low-level I/O error

QueryNorm(Single)

Computes the normalization value for a query given the sum of the normalized weights GetValueForNormalization() of each of the query terms. this value is passed back to the weight (Normalize(Single, Single) of each query term, to provide a hook to attempt to make scores from different queries comparable.

By default this is disabled (returns 1), but some implementations such as TFIDFSimilarity override this.

Declaration

public virtual float QueryNorm(float valueForNormalization)

Parameters

Type	Name	Description
System.Single	valueForNormalization	the sum of the term normalization values

Returns

Type	Description
System.Single	a normalization factor for query weights

See Also

Similarity

Similarity