org.apache.lucene.search.similarities
Class BM25Similarity

java.lang.Object
  org.apache.lucene.search.similarities.Similarity
      org.apache.lucene.search.similarities.BM25Similarity

public class BM25Similarity
extends Similarity

BM25 Similarity. Introduced in Stephen E. Robertson, Steve Walker, Susan Jones, Micheline Hancock-Beaulieu, and Mike Gatford. Okapi at TREC-3. In Proceedings of the Third Text REtrieval Conference (TREC 1994). Gaithersburg, USA, November 1994.

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

Nested Class Summary

Nested classes/interfaces inherited from class org.apache.lucene.search.similarities.Similarity

Similarity.ExactSimScorer, Similarity.SimWeight, Similarity.SloppySimScorer

Field Summary

protected boolean

discountOverlaps True if overlap tokens (tokens with a position of increment of zero) are discounted from the document's length.

Constructor Summary

BM25Similarity()
BM25 with these default values: k1 = 1.2, b = 0.75.

BM25Similarity(float k1, float b)
BM25 with the supplied parameter values.

Method Summary

protected float	avgFieldLength(CollectionStatistics collectionStats) The default implementation computes the average as sumTotalTermFreq / maxDoc, or returns 1 if the index does not store sumTotalTermFreq (Lucene 3.x indexes or any field that omits frequency information).
long	computeNorm(FieldInvertState state) Computes the normalization value for a field, given the accumulated state of term processing for this field (see FieldInvertState).
Similarity.SimWeight	computeWeight(float queryBoost, CollectionStatistics collectionStats, TermStatistics... termStats) Compute any collection-level weight (e.g.
protected float	decodeNormValue(byte b) The default implementation returns 1 / f2 where f is SmallFloat.byte315ToFloat(byte).
protected byte	encodeNormValue(float boost, int fieldLength) The default implementation encodes boost / sqrt(length) with SmallFloat.floatToByte315(float).
Similarity.ExactSimScorer	exactSimScorer(Similarity.SimWeight stats, AtomicReaderContext context) Creates a new Similarity.ExactSimScorer to score matching documents from a segment of the inverted index.
float	getB() Returns the b parameter
boolean	getDiscountOverlaps() Returns true if overlap tokens are discounted from the document's length.
float	getK1() Returns the k1 parameter
protected float	idf(long docFreq, long numDocs) Implemented as log(1 + (numDocs - docFreq + 0.5)/(docFreq + 0.5)).
Explanation	idfExplain(CollectionStatistics collectionStats, TermStatistics termStats) Computes a score factor for a simple term and returns an explanation for that score factor.
Explanation	idfExplain(CollectionStatistics collectionStats, TermStatistics[] termStats) Computes a score factor for a phrase.
protected float	scorePayload(int doc, int start, int end, BytesRef payload) The default implementation returns 1
void	setDiscountOverlaps(boolean v) Sets whether overlap tokens (Tokens with 0 position increment) are ignored when computing norm.
protected float	sloppyFreq(int distance) Implemented as 1 / (distance + 1).
Similarity.SloppySimScorer	sloppySimScorer(Similarity.SimWeight stats, AtomicReaderContext context) Creates a new Similarity.SloppySimScorer to score matching documents from a segment of the inverted index.
String	toString()

Methods inherited from class org.apache.lucene.search.similarities.Similarity

coord, queryNorm

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

discountOverlaps

protected boolean discountOverlaps

True if overlap tokens (tokens with a position of increment of zero) are discounted from the document's length.

Constructor Detail

BM25Similarity

public BM25Similarity(float k1,
                      float b)

BM25 with the supplied parameter values.

Parameters:

k1 - Controls non-linear term frequency normalization (saturation).

b - Controls to what degree document length normalizes tf values.

BM25Similarity

public BM25Similarity()

BM25 with these default values:

• k1 = 1.2,
• b = 0.75.

Method Detail

idf

protected float idf(long docFreq,
                    long numDocs)

Implemented as log(1 + (numDocs - docFreq + 0.5)/(docFreq + 0.5)).

sloppyFreq

protected float sloppyFreq(int distance)

Implemented as 1 / (distance + 1).

scorePayload

protected float scorePayload(int doc,
                             int start,
                             int end,
                             BytesRef payload)

The default implementation returns 1

avgFieldLength

protected float avgFieldLength(CollectionStatistics collectionStats)

The default implementation computes the average as sumTotalTermFreq / maxDoc, or returns 1 if the index does not store sumTotalTermFreq (Lucene 3.x indexes or any field that omits frequency information).

encodeNormValue

protected byte encodeNormValue(float boost,
                               int fieldLength)

The default implementation encodes boost / sqrt(length) with SmallFloat.floatToByte315(float). This is compatible with Lucene's default implementation. If you change this, then you should change decodeNormValue(byte) to match.

decodeNormValue

protected float decodeNormValue(byte b)

The default implementation returns 1 / f2 where f is SmallFloat.byte315ToFloat(byte).

setDiscountOverlaps

public void setDiscountOverlaps(boolean v)

Sets whether overlap tokens (Tokens with 0 position increment) are ignored when computing norm. By default this is true, meaning overlap tokens do not count when computing norms.

getDiscountOverlaps

public boolean getDiscountOverlaps()

Returns true if overlap tokens are discounted from the document's length.

See Also:

setDiscountOverlaps(boolean)

computeNorm

public final long computeNorm(FieldInvertState state)

Description copied from class: Similarity

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.

Specified by:

computeNorm in class Similarity

Parameters:

state - current processing state for this field

Returns:

computed norm value

idfExplain

public Explanation idfExplain(CollectionStatistics collectionStats,
                              TermStatistics termStats)

Computes a score factor for a simple term and returns an explanation for that score factor.

The default implementation uses:

 idf(docFreq, searcher.maxDoc());
 

Note that CollectionStatistics.maxDoc() is used instead of IndexReader#numDocs() because also TermStatistics.docFreq() is used, and when the latter is inaccurate, so is CollectionStatistics.maxDoc(), and in the same direction. In addition, CollectionStatistics.maxDoc() is more efficient to compute

Parameters:

collectionStats - collection-level statistics

termStats - term-level statistics for the term

Returns:

an Explain object that includes both an idf score factor and an explanation for the term.

idfExplain

public Explanation idfExplain(CollectionStatistics collectionStats,
                              TermStatistics[] termStats)

Computes a score factor for a phrase.

The default implementation sums the idf factor for each term in the phrase.

Parameters:

collectionStats - collection-level statistics

termStats - term-level statistics for the terms in the phrase

Returns:

an Explain object that includes both an idf score factor for the phrase and an explanation for each term.

computeWeight

public final Similarity.SimWeight computeWeight(float queryBoost,
                                                CollectionStatistics collectionStats,
                                                TermStatistics... termStats)

Description copied from class: Similarity

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

Specified by:

computeWeight in class Similarity

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.

exactSimScorer

public final Similarity.ExactSimScorer exactSimScorer(Similarity.SimWeight stats,
                                                      AtomicReaderContext context)
                                               throws IOException

Description copied from class: Similarity

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

Specified by:

exactSimScorer in class Similarity

Parameters:

stats - collection information from Similarity.computeWeight(float, CollectionStatistics, TermStatistics...)

context - segment of the inverted index to be scored.

Returns:

ExactSimScorer for scoring documents across context

Throws:

IOException - if there is a low-level I/O error

sloppySimScorer

public final Similarity.SloppySimScorer sloppySimScorer(Similarity.SimWeight stats,
                                                        AtomicReaderContext context)
                                                 throws IOException

Description copied from class: Similarity

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

Specified by:

sloppySimScorer in class Similarity

Parameters:

stats - collection information from Similarity.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

toString

public String toString()

Overrides:

toString in class Object

getK1

public float getK1()

Returns the k1 parameter

See Also:

BM25Similarity(float, float)

getB

public float getB()

Returns the b parameter

See Also:

BM25Similarity(float, float)