| java.lang.Object
 org.apache.lucene.search.Similarity
All known Subclasses: org.apache.lucene.search.SimilarityDelegator, org.apache.lucene.search.DefaultSimilarity,
| Similarity | | abstract public class Similarity implements Serializable(Code) | | Expert: Scoring API.
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
-
tf(t in d)
correlates to the term's frequency,
defined as the number of times term t appears in the currently scored document d.
Documents that have more occurrences of a given term receive a higher score.
The default computation for tf(t in d) in
org.apache.lucene.search.DefaultSimilarity.tf(float) DefaultSimilarity is:
-
idf(t) stands for Inverse Document Frequency. This value
correlates to the inverse of docFreq
(the number of documents in which the term t appears).
This means rarer terms give higher contribution to the total score.
The default computation for idf(t) in
org.apache.lucene.search.DefaultSimilarity.idf(intint) DefaultSimilarity is:
-
coord(q,d)
is a score factor based on how many of the query terms are found in the specified document.
Typically, a document that contains more of the query's terms will receive a higher score
than another document with fewer query terms.
This is a search time factor computed in
Similarity.coord(int,int) coord(q,d) by the Similarity in effect at search time.
-
queryNorm(q)
is a normalizing factor used to make scores between queries comparable.
This factor does not affect document ranking (since all ranked documents are multiplied by the same factor),
but rather just attempts to make scores from different queries (or even different indexes) comparable.
This is a search time factor computed by the Similarity in effect at search time.
The default computation in
org.apache.lucene.search.DefaultSimilarity.queryNorm(float) DefaultSimilarity is:
The sum of squared weights (of the query terms) is
computed by the query
org.apache.lucene.search.Weight object.
For example, a
org.apache.lucene.search.BooleanQuery boolean query computes this value as:
-
t.getBoost()
is a search time boost of term t in the query q as
specified in the query text
(see query syntax),
or as set by application calls to
org.apache.lucene.search.Query.setBoost(float) 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
org.apache.lucene.search.TermQuery TermQuery objects,
and so the boost of a term in the query is accessible by calling the sub-query
org.apache.lucene.search.Query.getBoost getBoost() .
-
norm(t,d) encapsulates a few (indexing time) boost and length factors:
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:
However the resulted norm value is
Similarity.encodeNorm(float) encoded as a single byte
before being stored.
At search time, the norm byte value is read from the index
org.apache.lucene.store.Directory directory and
Similarity.decodeNorm(byte) 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: Similarity.setDefault(Similarity)
See Also: org.apache.lucene.index.IndexWriter.setSimilarity(Similarity)
See Also: Searcher.setSimilarity(Similarity) |
| Method Summary | |
| abstract public float | coord(int overlap, int maxOverlap)
Computes a score factor based on the fraction of all query terms that a
document contains. | | public static float | decodeNorm(byte b)
Decodes a normalization factor stored in an index. | | public static byte | encodeNorm(float f)
Encodes a normalization factor for storage in an index.
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. | | public static Similarity | getDefault()
Return the default Similarity implementation used by indexing and search
code. | | public static float[] | getNormDecoder()
Returns a table for decoding normalization bytes. | | public float | idf(Term term, Searcher searcher)
Computes a score factor for a simple term. | | public float | idf(Collection terms, Searcher searcher)
Computes a score factor for a phrase. | | abstract public 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 public float | lengthNorm(String fieldName, int numTokens)
Computes the normalization value for a field given the total number of
terms contained in a field. | | abstract public float | queryNorm(float sumOfSquaredWeights)
Computes the normalization value for a query given the sum of the squared
weights of each of the query terms. | | public float | scorePayload(String fieldName, byte[] payload, int offset, int length)
Calculate a scoring factor based on the data in the payload. | | public static void | setDefault(Similarity similarity)
Set the default Similarity implementation used by indexing and search
code. | | abstract public float | sloppyFreq(int distance)
Computes the amount of a sloppy phrase match, based on an edit distance. | | public float | tf(int freq)
Computes a score factor based on a term or phrase's frequency in a
document. | | abstract public float | tf(float freq)
Computes a score factor based on a term or phrase's frequency in a
document. |
| coord | | abstract public float coord(int overlap, int maxOverlap)(Code) | | Computes a score factor based on the fraction of all query terms that a
document contains. This value is multiplied into scores.
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
Parameters:
maxOverlap - the total number of terms in the query a score factor based on term overlap with the query |
| encodeNorm | | public static byte encodeNorm(float f)(Code) | | Encodes a normalization factor for storage in an index.
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: org.apache.lucene.document.Field.setBoost(float)
See Also: org.apache.lucene.util.SmallFloat |
| idf | | public float idf(Term term, Searcher searcher) throws IOException(Code) | | Computes a score factor for a simple term.
The default implementation is:
return idf(searcher.docFreq(term), searcher.maxDoc());
Note that
Searcher.maxDoc is used instead of
org.apache.lucene.index.IndexReader.numDocs because it is proportional to
Searcher.docFreq(Term) , i.e., when one is inaccurate,
so is the other, and in the same direction.
Parameters:
term - the term in question
Parameters:
searcher - the document collection being searched a score factor for the term |
| idf | | public float idf(Collection terms, Searcher searcher) throws IOException(Code) | | Computes a score factor for a phrase.
The default implementation sums the
Similarity.idf(Term,Searcher) factor
for each term in the phrase.
Parameters:
terms - the terms in the phrase
Parameters:
searcher - the document collection being searched a score factor for the phrase |
| idf | | abstract public float idf(int docFreq, int numDocs)(Code) | | Computes a score factor based on a term's document frequency (the number
of documents which contain the term). This value is multiplied by the
Similarity.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.
Parameters:
docFreq - the number of documents which contain the term
Parameters:
numDocs - the total number of documents in the collection a score factor based on the term's document frequency |
| lengthNorm | | abstract public float lengthNorm(String fieldName, int numTokens)(Code) | | Computes the normalization value for a field given the total number of
terms contained in a field. These values, together with field boosts, are
stored in an index and multipled into scores for hits on each field by the
search code.
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
org.apache.lucene.index.IndexWriter.addDocument(org.apache.lucene.document.Document)
and stored then using
Similarity.encodeNorm(float) .
Thus they have limited precision, and documents
must be re-indexed if this method is altered.
Parameters:
fieldName - the name of the field
Parameters:
numTokens - the total number of tokens contained in fields namedfieldName of doc. a normalization factor for hits on this field of this document
See Also: org.apache.lucene.document.Field.setBoost(float) |
| queryNorm | | abstract public float queryNorm(float sumOfSquaredWeights)(Code) | | Computes the normalization value for a query given the sum of the squared
weights of each of the query terms. This value is then multipled into the
weight of each query term.
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 a normalization factor for query weights |
| scorePayload | | public float scorePayload(String fieldName, byte[] payload, int offset, int length)(Code) | | Calculate a scoring factor based on the data in the payload. Overriding implementations
are responsible for interpreting what is in the payload. Lucene makes no assumptions about
what is in the byte array.
The default implementation returns 1.
Parameters:
fieldName - The fieldName of the term this payload belongs to
Parameters:
payload - The payload byte array to be scored
Parameters:
offset - The offset into the payload array
Parameters:
length - The length in the array An implementation dependent float to be used as a scoring factor |
| sloppyFreq | | abstract public float sloppyFreq(int distance)(Code) | | Computes the amount of a sloppy phrase match, based on an edit distance.
This value is summed for each sloppy phrase match in a document to form
the frequency that is passed to
Similarity.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.
See Also: PhraseQuery.setSlop(int)
Parameters:
distance - the edit distance of this sloppy phrase match the frequency increment for this match |
| tf | | public float tf(int freq)(Code) | | Computes a score factor based on a term or phrase's frequency in a
document. This value is multiplied by the
Similarity.idf(Term,Searcher) 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
Similarity.tf(float) .
Parameters:
freq - the frequency of a term within a document a score factor based on a term's within-document frequency |
| tf | | abstract public float tf(float freq)(Code) | | Computes a score factor based on a term or phrase's frequency in a
document. This value is multiplied by the
Similarity.idf(Term,Searcher) 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.
Parameters:
freq - the frequency of a term within a document a score factor based on a term's within-document frequency |
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