Package org.apache.lucene.search.similarities

Class Similarity

java.lang.Object
org.apache.lucene.search.similarities.Similarity
Direct Known Subclasses:
BM25Similarity, MultiSimilarity, PerFieldSimilarityWrapper, SimilarityBase, TFIDFSimilarity
public abstract class Similarity extends Object
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 AtomicReader.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 SmallFloat 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 CollectionStatistics.sumTotalTermFreq() and CollectionStatistics.maxDoc() or CollectionStatistics.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 AtomicReader.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(float, 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: Similarity.SimWeight.getValueForNormalization() is called for each query leaf node, queryNorm(float) is called for the top-level query, and finally Similarity.SimWeight.normalize(float, float) 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 simScorer(SimWeight, AtomicReaderContext) The score() method is called for each matching document.

When IndexSearcher.explain(org.apache.lucene.search.Query, int) 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.

See Also:

  • Constructor Details

    • Similarity

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

  • Method Details

    • coord

      public float coord(int overlap, int maxOverlap)
      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.

      Parameters:
      overlap - the number of query terms matched in the document
      maxOverlap - the total number of terms in the query
      Returns:
      a score factor based on term overlap with the query

    • queryNorm

      public float queryNorm(float valueForNormalization)
      Computes the normalization value for a query given the sum of the normalized weights Similarity.SimWeight.getValueForNormalization() of each of the query terms. This value is passed back to the weight (Similarity.SimWeight.normalize(float, float) 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.

      Parameters:
      valueForNormalization - the sum of the term normalization values
      Returns:
      a normalization factor for query weights

    • computeNorm

      public abstract long computeNorm(FieldInvertState state)
      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.getLength() is large, and larger values when state.getLength() is small.

      Parameters:
      state - current processing state for this field
      Returns:
      computed norm value

    • computeWeight

      public abstract Similarity.SimWeight computeWeight(float queryBoost, CollectionStatistics collectionStats, TermStatistics... termStats)
      Compute any collection-level weight (e.g. IDF, average document length, etc) needed for scoring a query.
      Parameters:
      queryBoost - the query-time boost.
      collectionStats - collection-level statistics, such as the number of tokens in the collection.
      termStats - term-level statistics, such as the document frequency of a term across the collection.
      Returns:
      SimWeight object with the information this Similarity needs to score a query.

    • simScorer

      public abstract Similarity.SimScorer simScorer(Similarity.SimWeight weight, AtomicReaderContext context) throws IOException
      Creates a new Similarity.SimScorer to score matching documents from a segment of the inverted index.
      Parameters:
      weight - collection information from computeWeight(float, CollectionStatistics, TermStatistics...)
      context - segment of the inverted index to be scored.
      Returns:
      SloppySimScorer for scoring documents across context
      Throws:
      IOException - if there is a low-level I/O error