org.apache.lucene.search
public abstract class Similarity extends Object implements Serializable
Subclasses implement search scoring.
The score of query q
for document d
correlates to the
cosine-distance or dot-product between document and query vectors in a
Vector Space Model (VSM) of Information Retrieval.
A document whose vector is closer to the query vector in that model is scored higher.
The score is computed as follows:
|
where
DefaultSimilarity
is:
tf(t in d) =
|
frequency½ |
DefaultSimilarity
is:
idf(t) =
|
1 + log ( |
|
) |
coord(q,d)
by the Similarity in effect at search time.
DefaultSimilarity
is:
queryNorm(q) =
queryNorm(sumOfSquaredWeights)
=
|
|
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 sub-query
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.
Also notice that search time is too late to modify this norm part of scoring, e.g. by
using a different Similarity for search.
See Also: setDefault setSimilarity setSimilarity
Method Summary | |
---|---|
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. |
float | idf(Term term, Searcher searcher) Computes a score factor for a simple term.
|
float | idf(Collection terms, Searcher searcher) Computes a score factor for a phrase.
|
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). |
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(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.
|
float | tf(int freq) Computes a score factor based on a term or phrase's frequency in a
document. |
abstract float | tf(float freq) Computes a score factor based on a term or phrase's frequency in a
document. |
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.
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
See Also: Similarity
The encoding uses a three-bit mantissa, a five-bit exponent, and the zero-exponent 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.
See Also: Field SmallFloat
This is initially an instance of DefaultSimilarity.
See Also: setSimilarity setSimilarity
See Also: Similarity
The default implementation is:
return idf(searcher.docFreq(term), searcher.maxDoc());Note that maxDoc is used instead of numDocs because it is proportional to docFreq , i.e., when one is inaccurate, so is the other, and in the same direction.
Parameters: term the term in question searcher the document collection being searched
Returns: a score factor for the term
The default implementation sums the idf factor for each term in the phrase.
Parameters: terms the terms in the phrase searcher the document collection being searched
Returns: a score factor for the phrase
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.
Parameters: docFreq the number of documents which contain the term numDocs the total number of documents in the collection
Returns: a score factor based on the term's document frequency
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.
That these values are computed under addDocument and stored then using Similarity. Thus they have limited precision, and documents must be re-indexed if this method is altered.
Parameters: fieldName the name of the field numTokens the total number of tokens contained in fields named fieldName of doc.
Returns: a normalization factor for hits on this field of this document
See Also: Field
This does not affect ranking, but rather just attempts to make scores from different queries comparable.
Parameters: sumOfSquaredWeights the sum of the squares of query term weights
Returns: a normalization factor for query weights
The default implementation returns 1.
WARNING: The status of the Payloads feature is experimental. The APIs introduced here might change in the future and will not be supported anymore in such a case.
Parameters: payload The payload byte array to be scored offset The offset into the payload array length The length in the array
Returns: An implementation dependent float to be used as a scoring factor
See Also: setSimilarity setSimilarity
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.
Parameters: distance the edit distance of this sloppy phrase match
Returns: the frequency increment for this match
See Also: PhraseQuery
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 Similarity.
Parameters: freq the frequency of a term within a document
Returns: a score factor based on a term's within-document frequency
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.
Parameters: freq the frequency of a term within a document
Returns: a score factor based on a term's within-document frequency