Lucene++ - a full-featured, c++ search engine
API Documentation
Scoring API. More...
#include <Similarity.h>
Public Member Functions | |
Similarity () | |
virtual | ~Similarity () |
virtual String | getClassName () |
boost::shared_ptr< Similarity > | shared_from_this () |
virtual double | computeNorm (const String &fieldName, const FieldInvertStatePtr &state) |
Compute the normalization value for a field, given the accumulated state of term processing for this field (see FieldInvertState ). | |
virtual double | lengthNorm (const String &fieldName, int32_t numTokens)=0 |
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 multiplied into scores for hits on each field by the search code. | |
virtual double | queryNorm (double sumOfSquaredWeights)=0 |
Computes the normalization value for a query given the sum of the squared weights of each of the query terms. This value is multiplied into the weight of each query term. While the classic query normalization factor is computed as 1/sqrt(sumOfSquaredWeights), other implementations might completely ignore sumOfSquaredWeights (ie return 1). | |
virtual double | tf (int32_t freq) |
Computes a score factor based on a term or phrase's frequency in a document. This value is multiplied by the idf(int32_t, int32_t) factor for each term in the query and these products are then summed to form the initial score for a document. | |
virtual double | sloppyFreq (int32_t distance)=0 |
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 tf(double) . | |
virtual double | tf (double freq)=0 |
Computes a score factor based on a term or phrase's frequency in a document. This value is multiplied by the idf(int32_t, int32_t) factor for each term in the query and these products are then summed to form the initial score for a document. | |
virtual IDFExplanationPtr | idfExplain (const TermPtr &term, const SearcherPtr &searcher) |
Computes a score factor for a simple term and returns an explanation for that score factor. | |
virtual IDFExplanationPtr | idfExplain (Collection< TermPtr > terms, const SearcherPtr &searcher) |
Computes a score factor for a phrase. | |
virtual double | idf (int32_t docFreq, int32_t numDocs)=0 |
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 tf(int32_t) factor for each term in the query and these products are then summed to form the initial score for a document. | |
virtual double | coord (int32_t overlap, int32_t maxOverlap)=0 |
Computes a score factor based on the fraction of all query terms that a document contains. This value is multiplied into scores. | |
virtual double | scorePayload (int32_t docId, const String &fieldName, int32_t start, int32_t end, ByteArray payload, int32_t offset, int32_t length) |
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. | |
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virtual | ~LuceneObject () |
virtual void | initialize () |
Called directly after instantiation to create objects that depend on this object being fully constructed. | |
virtual LuceneObjectPtr | clone (const LuceneObjectPtr &other=LuceneObjectPtr()) |
Return clone of this object. | |
virtual int32_t | hashCode () |
Return hash code for this object. | |
virtual bool | equals (const LuceneObjectPtr &other) |
Return whether two objects are equal. | |
virtual int32_t | compareTo (const LuceneObjectPtr &other) |
Compare two objects. | |
virtual String | toString () |
Returns a string representation of the object. | |
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virtual | ~LuceneSync () |
virtual SynchronizePtr | getSync () |
Return this object synchronize lock. | |
virtual LuceneSignalPtr | getSignal () |
Return this object signal. | |
virtual void | lock (int32_t timeout=0) |
Lock this object using an optional timeout. | |
virtual void | unlock () |
Unlock this object. | |
virtual bool | holdsLock () |
Returns true if this object is currently locked by current thread. | |
virtual void | wait (int32_t timeout=0) |
Wait for signal using an optional timeout. | |
virtual void | notifyAll () |
Notify all threads waiting for signal. | |
Static Public Member Functions | |
static String | _getClassName () |
static SimilarityPtr | getDefault () |
Return the default Similarity implementation used by indexing and search code. This is initially an instance of DefaultSimilarity . | |
static double | decodeNorm (uint8_t b) |
Decodes a normalization factor stored in an index. | |
static const Collection< double > & | getNormDecoder () |
Returns a table for decoding normalization bytes. | |
static uint8_t | encodeNorm (double f) |
Encodes a normalization factor for storage in an index. | |
Static Public Attributes | |
static const Collection< double > | NORM_TABLE |
Static Protected Attributes | |
static const int32_t | NO_DOC_ID_PROVIDED |
Additional Inherited Members | |
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LuceneObject () | |
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SynchronizePtr | objectLock |
LuceneSignalPtr | objectSignal |
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 multi-dimensional space, where each distinct index term is a dimension, and weights are Tf-idf values.
VSM does not require weights to be Tf-idf values, but Tf-idf values are believed to produce search results of high quality, and so Lucene is using Tf-idf. 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):
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Where V(q) · V(d) is the dot product of the weighted vectors, and |V(q)| and |V(d)| are their Euclidean norms.
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:
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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:
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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. Note that tf(t in q) is assumed to be 1 and therefore it does not appear in this equation, However if a query contains twice the same term, there will be two term-queries with that same term and hence the computation would still be correct (although not very efficient). The default computation for tf(t in d) in DefaultSimilarity
is:
tf(t in d) = | frequency<big>½</big> |
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. idf(t) appears for t in both the query and the document, hence it is squared in the equation. The default computation for idf(t) in DefaultSimilarity
is:
idf(t) = | 1 + log <big>(</big> |
| <big>)</big> |
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 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 DefaultSimilarity
produces a Euclidean norm:
queryNorm(q) = queryNorm(sumOfSquaredWeights) = |
|
The sum of squared weights (of the query terms) is computed by the query Weight
object. For example, a boolean query
computes this value as:
<table cellpadding="1" cellspacing="0" border="0"n align="center">
sumOfSquaredWeights
= q.getBoost()
<big>2</big> ·
<big><big><big>∑</big></big></big>
<big><big>(</big></big> idf(t) · t.getBoost() <big><big>) 2 </big></big>
t in q
t.getBoost() is a search time boost of term t in the query q as specified in the query text or as set by application calls to 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()
.
norm(t,d) encapsulates a few (indexing time) boost and length factors:
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:
<table cellpadding="1" cellspacing="0" border="0"n align="center">
norm(t,d) = doc.getBoost()
· lengthNorm(field)
·
<big><big><big>∏</big></big></big>
field f in d named as t
However the resulted norm value is 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.
Compression of norm values to a single byte saves memory at search time, because once a field is referenced at search time, its norms - for all documents - are maintained in memory.
The rationale supporting such lossy compression of norm values is that given the difficulty (and inaccuracy) of users to express their true information need by a query, only big differences matter.
Last, note that search time is too late to modify this norm part of scoring, eg. by using a different Similarity
for search.
Lucene::Similarity::Similarity | ( | ) |
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virtual |
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inlinestatic |
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virtual |
Compute the normalization value for a field, given the accumulated state of term processing for this field (see 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, int32_t)
passing FieldInvertState#getLength()
as the second argument, and then multiplies this value by FieldInvertState#getBoost()
.
field | Field name |
state | Current processing state for this field |
Reimplemented in Lucene::SimilarityDelegator, and Lucene::DefaultSimilarity.
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pure virtual |
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.
overlap | The number of query terms matched in the document |
maxOverlap | The total number of terms in the query |
Implemented in Lucene::DefaultSimilarity, and Lucene::SimilarityDelegator.
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static |
Decodes a normalization factor stored in an index.
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static |
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.
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inlinevirtual |
Reimplemented in Lucene::DefaultSimilarity, and Lucene::SimilarityDelegator.
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static |
Return the default Similarity implementation used by indexing and search code. This is initially an instance of DefaultSimilarity
.
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static |
Returns a table for decoding normalization bytes.
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pure virtual |
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 tf(int32_t)
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 term |
numDocs | The total number of documents in the collection |
Implemented in Lucene::DefaultSimilarity, and Lucene::SimilarityDelegator.
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virtual |
Computes a score factor for a phrase.
The default implementation sums the idf factor for each term in the phrase.
terms | The terms in the phrase |
searcher | The document collection being searched |
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virtual |
Computes a score factor for a simple term and returns an explanation for that score factor.
The default implementation uses:
idf(searcher->docFreq(term), searcher->maxDoc());
Note that Searcher#maxDoc()
is used instead of IndexReader#numDocs()
because also Searcher#docFreq(TermPtr)
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 question |
searcher | The document collection being searched |
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pure virtual |
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 multiplied 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.
Note that the return values are computed under IndexWriter#addDocument(DocumentPtr)
and then stored using encodeNorm(double)
. Thus they have limited precision, and documents must be re-indexed if this method is altered.
fieldName | The name of the field |
numTokens | The total number of tokens contained in fields named fieldName of doc. |
Implemented in Lucene::DefaultSimilarity, and Lucene::SimilarityDelegator.
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pure virtual |
Computes the normalization value for a query given the sum of the squared weights of each of the query terms. This value is multiplied into the weight of each query term. While the classic query normalization factor is computed as 1/sqrt(sumOfSquaredWeights), other implementations might completely ignore sumOfSquaredWeights (ie return 1).
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 |
Implemented in Lucene::DefaultSimilarity, and Lucene::SimilarityDelegator.
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virtual |
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.
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 information |
fieldName | The fieldName of the term this payload belongs to |
start | The start position of the payload |
end | The end position of the payload |
payload | The payload byte array to be scored |
offset | The offset into the payload array |
length | The length in the array |
Reimplemented in Lucene::SimilarityDelegator.
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inline |
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pure virtual |
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 tf(double)
.
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 |
Implemented in Lucene::DefaultSimilarity, and Lucene::SimilarityDelegator.
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pure virtual |
Computes a score factor based on a term or phrase's frequency in a document. This value is multiplied by the idf(int32_t, int32_t)
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 |
Implemented in Lucene::DefaultSimilarity, and Lucene::SimilarityDelegator.
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virtual |
Computes a score factor based on a term or phrase's frequency in a document. This value is multiplied by the idf(int32_t, int32_t)
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(double)
.
freq | The frequency of a term within a document |
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staticprotected |
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static |