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java.lang.Object org.apache.lucene.search.Similarity
public abstract class Similarity
Expert: Scoring API.
Similarity defines the components of Lucene scoring. Overriding computation of these components is a convenient way to alter Lucene scoring.
Suggested reading: Introduction To Information Retrieval, Chapter 6.
The following describes how Lucene scoring evolves from underlying information retrieval models to (efficient) implementation. We first brief on VSM Score, then derive from it Lucene's Conceptual Scoring Formula, from which, finally, evolves Lucene's Practical Scoring Function (the latter is connected directly with Lucene classes and methods).
Lucene combines Boolean model (BM) of Information Retrieval with Vector Space Model (VSM) of Information Retrieval  documents "approved" by BM are scored by VSM.
In VSM, documents and queries are represented as weighted vectors in a multidimensional space, where each distinct index term is a dimension, and weights are Tfidf values.
VSM does not require weights to be Tfidf values, but Tfidf values are believed to produce search results of high quality, and so Lucene is using Tfidf. Tf and Idf are described in more detail below, but for now, for completion, let's just say that for given term t and document (or query) x, Tf(t,x) varies with the number of occurrences of term t in x (when one increases so does the other) and idf(t) similarly varies with the inverse of the number of index documents containing term t.
VSM score of document d for query q is the
Cosine Similarity
of the weighted query vectors V(q) and V(d):
 

Note: the above equation can be viewed as the dot product of the normalized weighted vectors, in the sense that dividing V(q) by its euclidean norm is normalizing it to a unit vector.
Lucene refines VSM score for both search quality and usability:
Under the simplifying assumption of a single field in the index,
we get Lucene's Conceptual scoring formula:
 

The conceptual formula is a simplification in the sense that (1) terms and documents are fielded and (2) boosts are usually per query term rather than per query.
We now describe how Lucene implements this conceptual scoring formula, and derive from it Lucene's Practical Scoring Function.
For efficient score computation some scoring components are computed and aggregated in advance:
Lucene's Practical Scoring Function is derived from the above. The color codes demonstrate how it relates to those of the conceptual formula:
 

where
DefaultSimilarity
is:
tf(t in d) =

frequency^{½} 
DefaultSimilarity
is:
idf(t) =

1 + log ( 

) 
coord(q,d)
by the Similarity in effect at search time.
DefaultSimilarity
produces a Euclidean norm:
queryNorm(q) =
queryNorm(sumOfSquaredWeights)
=


Weight
object.
For example, a boolean query
computes this value as:
sumOfSquaredWeights =
q.getBoost() ^{2}
·

∑  ( idf(t) · t.getBoost() ) ^{2} 
t in q 
setBoost()
.
Notice that there is really no direct API for accessing a boost of one term in a multi term query,
but rather multi terms are represented in a query as multi
TermQuery
objects,
and so the boost of a term in the query is accessible by calling the subquery
getBoost()
.
doc.setBoost()
before adding the document to the index.
field.setBoost()
before adding the field to a document.
lengthNorm(field)
 computed
when the document is added to the index in accordance with the number of tokens
of this field in the document, so that shorter fields contribute more to the score.
LengthNorm is computed by the Similarity class in effect at indexing.
When a document is added to the index, all the above factors are multiplied.
If the document has multiple fields with the same name, all their boosts are multiplied together:
norm(t,d) =
doc.getBoost()
·
lengthNorm(field)
·

∏ 
f.getBoost ()

field f in d named as t 
encoded
as a single byte
before being stored.
At search time, the norm byte value is read from the index
directory
and
decoded
back to a float norm value.
This encoding/decoding, while reducing index size, comes with the price of
precision loss  it is not guaranteed that decode(encode(x)) = x.
For instance, decode(encode(0.89)) = 0.75.
Similarity
for search.
setDefault(Similarity)
,
IndexWriter.setSimilarity(Similarity)
,
Searcher.setSimilarity(Similarity)
,
Serialized FormField Summary  

static int 
NO_DOC_ID_PROVIDED

Constructor Summary  

Similarity()

Method Summary  

float 
computeNorm(String field,
FieldInvertState state)
Compute the normalization value for a field, given the accumulated state of term processing for this field (see FieldInvertState ). 
abstract float 
coord(int overlap,
int maxOverlap)
Computes a score factor based on the fraction of all query terms that a document contains. 
static float 
decodeNorm(byte b)
Decodes a normalization factor stored in an index. 
static byte 
encodeNorm(float f)
Encodes a normalization factor for storage in an index. 
static Similarity 
getDefault()
Return the default Similarity implementation used by indexing and search code. 
static float[] 
getNormDecoder()
Returns a table for decoding normalization bytes. 
abstract float 
idf(int docFreq,
int numDocs)
Computes a score factor based on a term's document frequency (the number of documents which contain the term). 
Explanation.IDFExplanation 
idfExplain(Collection<Term> terms,
Searcher searcher)
Computes a score factor for a phrase. 
Explanation.IDFExplanation 
idfExplain(Term term,
Searcher searcher)
Computes a score factor for a simple term and returns an explanation for that score factor. 
abstract float 
lengthNorm(String fieldName,
int numTokens)
Computes the normalization value for a field given the total number of terms contained in a field. 
abstract float 
queryNorm(float sumOfSquaredWeights)
Computes the normalization value for a query given the sum of the squared weights of each of the query terms. 
float 
scorePayload(int docId,
String fieldName,
int start,
int end,
byte[] payload,
int offset,
int length)
Calculate a scoring factor based on the data in the payload. 
static void 
setDefault(Similarity similarity)
Set the default Similarity implementation used by indexing and search code. 
abstract float 
sloppyFreq(int distance)
Computes the amount of a sloppy phrase match, based on an edit distance. 
abstract float 
tf(float freq)
Computes a score factor based on a term or phrase's frequency in a document. 
float 
tf(int freq)
Computes a score factor based on a term or phrase's frequency in a document. 
Methods inherited from class java.lang.Object 

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait 
Field Detail 

public static final int NO_DOC_ID_PROVIDED
Constructor Detail 

public Similarity()
Method Detail 

public static void setDefault(Similarity similarity)
Searcher.setSimilarity(Similarity)
,
IndexWriter.setSimilarity(Similarity)
public static Similarity getDefault()
This is initially an instance of DefaultSimilarity
.
Searcher.setSimilarity(Similarity)
,
IndexWriter.setSimilarity(Similarity)
public static float decodeNorm(byte b)
encodeNorm(float)
public static float[] getNormDecoder()
encodeNorm(float)
public float computeNorm(String field, FieldInvertState state)
FieldInvertState
).
Implementations should calculate a float value based on the field state and then return that value.
For backward compatibility this method by default calls
lengthNorm(String, int)
passing
FieldInvertState.getLength()
as the second argument, and
then multiplies this value by FieldInvertState.getBoost()
.
WARNING: This API is new and experimental and may suddenly change.
field
 field namestate
 current processing state for this field
public abstract float lengthNorm(String fieldName, int numTokens)
Matches in longer fields are less precise, so implementations of this
method usually return smaller values when numTokens
is large,
and larger values when numTokens
is small.
Note that the return values are computed under
IndexWriter.addDocument(org.apache.lucene.document.Document)
and then stored using
encodeNorm(float)
.
Thus they have limited precision, and documents
must be reindexed if this method is altered.
fieldName
 the name of the fieldnumTokens
 the total number of tokens contained in fields named
fieldName of doc.
AbstractField.setBoost(float)
public abstract float queryNorm(float sumOfSquaredWeights)
This does not affect ranking, but the default implementation does make scores from different queries more comparable than they would be by eliminating the magnitude of the Query vector as a factor in the score.
sumOfSquaredWeights
 the sum of the squares of query term weights
public static byte encodeNorm(float f)
The encoding uses a threebit mantissa, a fivebit exponent, and the zeroexponent point at 15, thus representing values from around 7x10^9 to 2x10^9 with about one significant decimal digit of accuracy. Zero is also represented. Negative numbers are rounded up to zero. Values too large to represent are rounded down to the largest representable value. Positive values too small to represent are rounded up to the smallest positive representable value.
AbstractField.setBoost(float)
,
SmallFloat
public float tf(int freq)
idf(int, int)
factor for each term in the query and these products are then summed to
form the initial score for a document.
Terms and phrases repeated in a document indicate the topic of the
document, so implementations of this method usually return larger values
when freq
is large, and smaller values when freq
is small.
The default implementation calls tf(float)
.
freq
 the frequency of a term within a document
public abstract float sloppyFreq(int distance)
tf(float)
.
A phrase match with a small edit distance to a document passage more closely matches the document, so implementations of this method usually return larger values when the edit distance is small and smaller values when it is large.
distance
 the edit distance of this sloppy phrase match
PhraseQuery.setSlop(int)
public abstract float tf(float freq)
idf(int, int)
factor for each term in the query and these products are then summed to
form the initial score for a document.
Terms and phrases repeated in a document indicate the topic of the
document, so implementations of this method usually return larger values
when freq
is large, and smaller values when freq
is small.
freq
 the frequency of a term within a document
public Explanation.IDFExplanation idfExplain(Term term, Searcher searcher) throws IOException
The default implementation uses:
idf(searcher.docFreq(term), searcher.maxDoc());Note that
Searcher.maxDoc()
is used instead of
IndexReader#numDocs()
because also
Searcher.docFreq(Term)
is used, and when the latter
is inaccurate, so is Searcher.maxDoc()
, and in the same direction.
In addition, Searcher.maxDoc()
is more efficient to compute
term
 the term in questionsearcher
 the document collection being searched
IOException
public Explanation.IDFExplanation idfExplain(Collection<Term> terms, Searcher searcher) throws IOException
The default implementation sums the idf factor for each term in the phrase.
terms
 the terms in the phrasesearcher
 the document collection being searched
IOException
public abstract float idf(int docFreq, int numDocs)
tf(int)
factor for each term in the query and these products are
then summed to form the initial score for a document.
Terms that occur in fewer documents are better indicators of topic, so implementations of this method usually return larger values for rare terms, and smaller values for common terms.
docFreq
 the number of documents which contain the termnumDocs
 the total number of documents in the collection
public abstract float coord(int overlap, int maxOverlap)
The presence of a large portion of the query terms indicates a better match with the query, so implementations of this method usually return larger values when the ratio between these parameters is large and smaller values when the ratio between them is small.
overlap
 the number of query terms matched in the documentmaxOverlap
 the total number of terms in the query
public float scorePayload(int docId, String fieldName, int start, int end, byte[] payload, int offset, int length)
The default implementation returns 1.
docId
 The docId currently being scored. If this value is NO_DOC_ID_PROVIDED
, then it should be assumed that the PayloadQuery implementation does not provide document informationfieldName
 The fieldName of the term this payload belongs tostart
 The start position of the payloadend
 The end position of the payloadpayload
 The payload byte array to be scoredoffset
 The offset into the payload arraylength
 The length in the array


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