-
TFIDFSimilarity
-
TFIDFSimilarity(): void
-
discountOverlaps: boolean
-
setDiscountOverlaps(boolean): void
-
getDiscountOverlaps(): boolean
-
tf(float): float
-
idfExplain(CollectionStatistics, TermStatistics): Explanation
-
idfExplain(CollectionStatistics, TermStatistics[]): Explanation
-
idf(long, long): float
-
lengthNorm(int): float
-
computeNorm(FieldInvertState): long
-
scorer(float, CollectionStatistics, TermStatistics[]): SimScorer
-
TFIDFScorer
-
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.lucene.search.similarities;
import java.util.ArrayList;
import java.util.List;
import org.apache.lucene.index.FieldInvertState;
import org.apache.lucene.index.IndexOptions;
import org.apache.lucene.search.CollectionStatistics;
import org.apache.lucene.search.Explanation;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.TermStatistics;
import org.apache.lucene.util.SmallFloat;
Implementation of Similarity with the Vector Space Model.
Expert: Scoring API.
TFIDFSimilarity 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):
cosine-similarity(q,d) =
V(q) · V(d) ––––––––– |V(q)| |V(d)|
VSM Score
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:
- Normalizing V(d) to the unit vector is known to be problematic in that
it removes all document length information.
For some documents removing this info is probably ok,
e.g. a document made by duplicating a certain paragraph 10 times,
especially if that paragraph is made of distinct terms.
But for a document which contains no duplicated paragraphs,
this might be wrong.
To avoid this problem, a different document length normalization
factor is used, which normalizes to a vector equal to or larger
than the unit vector: doc-len-norm(d).
- At indexing, users can specify that certain documents are more
important than others, by assigning a document boost.
For this, the score of each document is also multiplied by its boost value
doc-boost(d).
- Lucene is field based, hence each query term applies to a single
field, document length normalization is by the length of the certain field,
and in addition to document boost there are also document fields boosts.
- The same field can be added to a document during indexing several times,
and so the boost of that field is the multiplication of the boosts of
the separate additions (or parts) of that field within the document.
- At search time users can specify boosts to each query, sub-query, and
each query term, hence the contribution of a query term to the score of
a document is multiplied by the boost of that query term query-boost(q).
- A document may match a multi term query without containing all
the terms of that query (this is correct for some of the queries).
Under the simplifying assumption of a single field in the index,
we get Lucene's Conceptual scoring formula:
score(q,d) =
query-boost(q) ·
V(q) · V(d) ––––––––– |V(q)|
· doc-len-norm(d)
· doc-boost(d)
Lucene 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:
- Query-boost for the query (actually for each query term)
is known when search starts.
- Query Euclidean norm |V(q)| can be computed when search starts,
as it is independent of the document being scored.
From search optimization perspective, it is a valid question
why bother to normalize the query at all, because all
scored documents will be multiplied by the same |V(q)|,
and hence documents ranks (their order by score) will not
be affected by this normalization.
There are two good reasons to keep this normalization:
- Recall that
Cosine Similarity can be used find how similar
two documents are. One can use Lucene for e.g.
clustering, and use a document as a query to compute
its similarity to other documents.
In this use case it is important that the score of document d3
for query d1 is comparable to the score of document d3
for query d2. In other words, scores of a document for two
distinct queries should be comparable.
There are other applications that may require this.
And this is exactly what normalizing the query vector V(q)
provides: comparability (to a certain extent) of two or more queries.
- Document length norm doc-len-norm(d) and document
boost doc-boost(d) are known at indexing time.
They are computed in advance and their multiplication
is saved as a single value in the index: norm(d).
(In the equations below, norm(t in d) means norm(field(t) in doc d)
where field(t) is the field associated with term t.)
Lucene's Practical Scoring Function is derived from the above.
The color codes demonstrate how it relates
to those of the conceptual formula:
score(q,d) =
∑
(
tf(t in d) ·
idf(t)2 ·
t.getBoost() ·
norm(t,d)
)
t in q
Lucene Practical Scoring Function
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
ClassicSimilarity is:
tf(t in d) =
frequency½
-
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
ClassicSimilarity is:
idf(t) =
1 + log (
docCount+1 ––––––––– docFreq+1
)
-
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 wrapping with
BoostQuery. 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) is an index-time boost factor that solely
depends on the number of tokens of this field in the document, so
that shorter fields contribute more to the score.
See Also:
/**
* Implementation of {@link Similarity} with the Vector Space Model.
* <p>
* Expert: Scoring API.
* <p>TFIDFSimilarity defines the components of Lucene scoring.
* Overriding computation of these components is a convenient
* way to alter Lucene scoring.
*
* <p>Suggested reading:
* <a href="http://nlp.stanford.edu/IR-book/html/htmledition/queries-as-vectors-1.html">
* Introduction To Information Retrieval, Chapter 6</a>.
*
* <p>The following describes how Lucene scoring evolves from
* underlying information retrieval models to (efficient) implementation.
* We first brief on <i>VSM Score</i>,
* then derive from it <i>Lucene's Conceptual Scoring Formula</i>,
* from which, finally, evolves <i>Lucene's Practical Scoring Function</i>
* (the latter is connected directly with Lucene classes and methods).
*
* <p>Lucene combines
* <a href="http://en.wikipedia.org/wiki/Standard_Boolean_model">
* Boolean model (BM) of Information Retrieval</a>
* with
* <a href="http://en.wikipedia.org/wiki/Vector_Space_Model">
* Vector Space Model (VSM) of Information Retrieval</a> -
* documents "approved" by BM are scored by VSM.
*
* <p>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
* <a href="http://en.wikipedia.org/wiki/Tfidf">Tf-idf</a> values.
*
* <p>VSM does not require weights to be <i>Tf-idf</i> values,
* but <i>Tf-idf</i> values are believed to produce search results of high quality,
* and so Lucene is using <i>Tf-idf</i>.
* <i>Tf</i> and <i>Idf</i> are described in more detail below,
* but for now, for completion, let's just say that
* for given term <i>t</i> and document (or query) <i>x</i>,
* <i>Tf(t,x)</i> varies with the number of occurrences of term <i>t</i> in <i>x</i>
* (when one increases so does the other) and
* <i>idf(t)</i> similarly varies with the inverse of the
* number of index documents containing term <i>t</i>.
*
* <p><i>VSM score</i> of document <i>d</i> for query <i>q</i> is the
* <a href="http://en.wikipedia.org/wiki/Cosine_similarity">
* Cosine Similarity</a>
* of the weighted query vectors <i>V(q)</i> and <i>V(d)</i>:
*
* <br> <br>
* <table cellpadding="2" cellspacing="2" border="0" style="width:auto; margin-left:auto; margin-right:auto" summary="formatting only">
* <tr><td>
* <table cellpadding="1" cellspacing="0" border="1" style="margin-left:auto; margin-right:auto" summary="formatting only">
* <tr><td>
* <table cellpadding="2" cellspacing="2" border="0" style="margin-left:auto; margin-right:auto" summary="cosine similarity formula">
* <tr>
* <td valign="middle" align="right" rowspan="1">
* cosine-similarity(q,d) =
* </td>
* <td valign="middle" align="center">
* <table summary="cosine similarity formula">
* <tr><td align="center" style="text-align: center"><small>V(q) · V(d)</small></td></tr>
* <tr><td align="center" style="text-align: center">–––––––––</td></tr>
* <tr><td align="center" style="text-align: center"><small>|V(q)| |V(d)|</small></td></tr>
* </table>
* </td>
* </tr>
* </table>
* </td></tr>
* </table>
* </td></tr>
* <tr><td>
* <center><u>VSM Score</u></center>
* </td></tr>
* </table>
* <br> <br>
*
*
* Where <i>V(q)</i> · <i>V(d)</i> is the
* <a href="http://en.wikipedia.org/wiki/Dot_product">dot product</a>
* of the weighted vectors,
* and <i>|V(q)|</i> and <i>|V(d)|</i> are their
* <a href="http://en.wikipedia.org/wiki/Euclidean_norm#Euclidean_norm">Euclidean norms</a>.
*
* <p>Note: the above equation can be viewed as the dot product of
* the normalized weighted vectors, in the sense that dividing
* <i>V(q)</i> by its euclidean norm is normalizing it to a unit vector.
*
* <p>Lucene refines <i>VSM score</i> for both search quality and usability:
* <ul>
* <li>Normalizing <i>V(d)</i> to the unit vector is known to be problematic in that
* it removes all document length information.
* For some documents removing this info is probably ok,
* e.g. a document made by duplicating a certain paragraph <i>10</i> times,
* especially if that paragraph is made of distinct terms.
* But for a document which contains no duplicated paragraphs,
* this might be wrong.
* To avoid this problem, a different document length normalization
* factor is used, which normalizes to a vector equal to or larger
* than the unit vector: <i>doc-len-norm(d)</i>.
* </li>
*
* <li>At indexing, users can specify that certain documents are more
* important than others, by assigning a document boost.
* For this, the score of each document is also multiplied by its boost value
* <i>doc-boost(d)</i>.
* </li>
*
* <li>Lucene is field based, hence each query term applies to a single
* field, document length normalization is by the length of the certain field,
* and in addition to document boost there are also document fields boosts.
* </li>
*
* <li>The same field can be added to a document during indexing several times,
* and so the boost of that field is the multiplication of the boosts of
* the separate additions (or parts) of that field within the document.
* </li>
*
* <li>At search time users can specify boosts to each query, sub-query, and
* each query term, hence the contribution of a query term to the score of
* a document is multiplied by the boost of that query term <i>query-boost(q)</i>.
* </li>
*
* <li>A document may match a multi term query without containing all
* the terms of that query (this is correct for some of the queries).
* </li>
* </ul>
*
* <p>Under the simplifying assumption of a single field in the index,
* we get <i>Lucene's Conceptual scoring formula</i>:
*
* <br> <br>
* <table cellpadding="2" cellspacing="2" border="0" style="width:auto; margin-left:auto; margin-right:auto" summary="formatting only">
* <tr><td>
* <table cellpadding="1" cellspacing="0" border="1" style="margin-left:auto; margin-right:auto" summary="formatting only">
* <tr><td>
* <table cellpadding="2" cellspacing="2" border="0" style="margin-left:auto; margin-right:auto" summary="formatting only">
* <tr>
* <td valign="middle" align="right" rowspan="1">
* score(q,d) =
* <span style="color: #CCCC00">query-boost(q)</span> ·
* </td>
* <td valign="middle" align="center">
* <table summary="Lucene conceptual scoring formula">
* <tr><td align="center" style="text-align: center"><small><span style="color: #993399">V(q) · V(d)</span></small></td></tr>
* <tr><td align="center" style="text-align: center">–––––––––</td></tr>
* <tr><td align="center" style="text-align: center"><small><span style="color: #FF33CC">|V(q)|</span></small></td></tr>
* </table>
* </td>
* <td valign="middle" align="right" rowspan="1">
* · <span style="color: #3399FF">doc-len-norm(d)</span>
* · <span style="color: #3399FF">doc-boost(d)</span>
* </td>
* </tr>
* </table>
* </td></tr>
* </table>
* </td></tr>
* <tr><td>
* <center><u>Lucene Conceptual Scoring Formula</u></center>
* </td></tr>
* </table>
* <br> <br>
*
* <p>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.
*
* <p>We now describe how Lucene implements this conceptual scoring formula, and
* derive from it <i>Lucene's Practical Scoring Function</i>.
*
* <p>For efficient score computation some scoring components
* are computed and aggregated in advance:
*
* <ul>
* <li><i>Query-boost</i> for the query (actually for each query term)
* is known when search starts.
* </li>
*
* <li>Query Euclidean norm <i>|V(q)|</i> can be computed when search starts,
* as it is independent of the document being scored.
* From search optimization perspective, it is a valid question
* why bother to normalize the query at all, because all
* scored documents will be multiplied by the same <i>|V(q)|</i>,
* and hence documents ranks (their order by score) will not
* be affected by this normalization.
* There are two good reasons to keep this normalization:
* <ul>
* <li>Recall that
* <a href="http://en.wikipedia.org/wiki/Cosine_similarity">
* Cosine Similarity</a> can be used find how similar
* two documents are. One can use Lucene for e.g.
* clustering, and use a document as a query to compute
* its similarity to other documents.
* In this use case it is important that the score of document <i>d3</i>
* for query <i>d1</i> is comparable to the score of document <i>d3</i>
* for query <i>d2</i>. In other words, scores of a document for two
* distinct queries should be comparable.
* There are other applications that may require this.
* And this is exactly what normalizing the query vector <i>V(q)</i>
* provides: comparability (to a certain extent) of two or more queries.
* </li>
* </ul>
* </li>
*
* <li>Document length norm <i>doc-len-norm(d)</i> and document
* boost <i>doc-boost(d)</i> are known at indexing time.
* They are computed in advance and their multiplication
* is saved as a single value in the index: <i>norm(d)</i>.
* (In the equations below, <i>norm(t in d)</i> means <i>norm(field(t) in doc d)</i>
* where <i>field(t)</i> is the field associated with term <i>t</i>.)
* </li>
* </ul>
*
* <p><i>Lucene's Practical Scoring Function</i> is derived from the above.
* The color codes demonstrate how it relates
* to those of the <i>conceptual</i> formula:
*
* <table cellpadding="2" cellspacing="2" border="0" style="width:auto; margin-left:auto; margin-right:auto" summary="formatting only">
* <tr><td>
* <table cellpadding="" cellspacing="2" border="2" style="margin-left:auto; margin-right:auto" summary="formatting only">
* <tr><td>
* <table cellpadding="2" cellspacing="2" border="0" style="margin-left:auto; margin-right:auto" summary="Lucene conceptual scoring formula">
* <tr>
* <td valign="middle" align="right" rowspan="1">
* score(q,d) =
* <big><big><big>∑</big></big></big>
* </td>
* <td valign="middle" align="right" rowspan="1">
* <big><big>(</big></big>
* <A HREF="#formula_tf"><span style="color: #993399">tf(t in d)</span></A> ·
* <A HREF="#formula_idf"><span style="color: #993399">idf(t)</span></A><sup>2</sup> ·
* <A HREF="#formula_termBoost"><span style="color: #CCCC00">t.getBoost()</span></A> ·
* <A HREF="#formula_norm"><span style="color: #3399FF">norm(t,d)</span></A>
* <big><big>)</big></big>
* </td>
* </tr>
* <tr valign="top">
* <td></td>
* <td align="center" style="text-align: center"><small>t in q</small></td>
* <td></td>
* </tr>
* </table>
* </td></tr>
* </table>
* </td></tr>
* <tr><td>
* <center><u>Lucene Practical Scoring Function</u></center>
* </td></tr>
* </table>
*
* <p> where
* <ol>
* <li>
* <A NAME="formula_tf"></A>
* <b><i>tf(t in d)</i></b>
* correlates to the term's <i>frequency</i>,
* defined as the number of times term <i>t</i> appears in the currently scored document <i>d</i>.
* Documents that have more occurrences of a given term receive a higher score.
* Note that <i>tf(t in q)</i> is assumed to be <i>1</i> 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 <i>tf(t in d)</i> in
* {@link org.apache.lucene.search.similarities.ClassicSimilarity#tf(float) ClassicSimilarity} is:
*
* <br> <br>
* <table cellpadding="2" cellspacing="2" border="0" style="width:auto; margin-left:auto; margin-right:auto" summary="term frequency computation">
* <tr>
* <td valign="middle" align="right" rowspan="1">
* {@link org.apache.lucene.search.similarities.ClassicSimilarity#tf(float) tf(t in d)} =
* </td>
* <td valign="top" align="center" rowspan="1">
* frequency<sup><big>½</big></sup>
* </td>
* </tr>
* </table>
* <br> <br>
* </li>
*
* <li>
* <A NAME="formula_idf"></A>
* <b><i>idf(t)</i></b> stands for Inverse Document Frequency. This value
* correlates to the inverse of <i>docFreq</i>
* (the number of documents in which the term <i>t</i> appears).
* This means rarer terms give higher contribution to the total score.
* <i>idf(t)</i> appears for <i>t</i> in both the query and the document,
* hence it is squared in the equation.
* The default computation for <i>idf(t)</i> in
* {@link org.apache.lucene.search.similarities.ClassicSimilarity#idf(long, long) ClassicSimilarity} is:
*
* <br> <br>
* <table cellpadding="2" cellspacing="2" border="0" style="width:auto; margin-left:auto; margin-right:auto" summary="inverse document frequency computation">
* <tr>
* <td valign="middle" align="right">
* {@link org.apache.lucene.search.similarities.ClassicSimilarity#idf(long, long) idf(t)} =
* </td>
* <td valign="middle" align="center">
* 1 + log <big>(</big>
* </td>
* <td valign="middle" align="center">
* <table summary="inverse document frequency computation">
* <tr><td align="center" style="text-align: center"><small>docCount+1</small></td></tr>
* <tr><td align="center" style="text-align: center">–––––––––</td></tr>
* <tr><td align="center" style="text-align: center"><small>docFreq+1</small></td></tr>
* </table>
* </td>
* <td valign="middle" align="center">
* <big>)</big>
* </td>
* </tr>
* </table>
* <br> <br>
* </li>
*
* <li>
* <A NAME="formula_termBoost"></A>
* <b><i>t.getBoost()</i></b>
* is a search time boost of term <i>t</i> in the query <i>q</i> as
* specified in the query text
* (see <A HREF="{@docRoot}/../queryparser/org/apache/lucene/queryparser/classic/package-summary.html#Boosting_a_Term">query syntax</A>),
* or as set by wrapping with
* {@link org.apache.lucene.search.BoostQuery#BoostQuery(org.apache.lucene.search.Query, float) BoostQuery}.
* 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
* {@link org.apache.lucene.search.TermQuery TermQuery} objects,
* and so the boost of a term in the query is accessible by calling the sub-query
* {@link org.apache.lucene.search.BoostQuery#getBoost() getBoost()}.
* <br> <br>
* </li>
*
* <li>
* <A NAME="formula_norm"></A>
* <b><i>norm(t,d)</i></b> is an index-time boost factor that solely
* depends on the number of tokens of this field in the document, so
* that shorter fields contribute more to the score.
* </li>
* </ol>
*
* @see org.apache.lucene.index.IndexWriterConfig#setSimilarity(Similarity)
* @see IndexSearcher#setSimilarity(Similarity)
*/
public abstract class TFIDFSimilarity extends Similarity {
Sole constructor. (For invocation by subclass
constructors, typically implicit.)
/**
* Sole constructor. (For invocation by subclass
* constructors, typically implicit.)
*/
public TFIDFSimilarity() {}
True if overlap tokens (tokens with a position of increment of zero) are
discounted from the document's length.
/**
* True if overlap tokens (tokens with a position of increment of zero) are
* discounted from the document's length.
*/
protected boolean discountOverlaps = true;
Determines 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.
@lucene.experimental
@see #computeNorm
/** Determines 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.
*
* @lucene.experimental
*
* @see #computeNorm
*/
public void setDiscountOverlaps(boolean v) {
discountOverlaps = v;
}
Returns true if overlap tokens are discounted from the document's length.
See Also: - setDiscountOverlaps
/**
* Returns true if overlap tokens are discounted from the document's length.
* @see #setDiscountOverlaps
*/
public boolean getDiscountOverlaps() {
return discountOverlaps;
}
Computes a score factor based on a term or phrase's frequency in a document. This value is multiplied by the idf(long, long) 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.
Params: - freq – the frequency of a term within a document
Returns: a score factor based on a term's within-document frequency
/** Computes a score factor based on a term or phrase's frequency in a
* document. This value is multiplied by the {@link #idf(long, long)}
* factor for each term in the query and these products are then summed to
* form the initial score for a document.
*
* <p>Terms and phrases repeated in a document indicate the topic of the
* document, so implementations of this method usually return larger values
* when <code>freq</code> is large, and smaller values when <code>freq</code>
* is small.
*
* @param freq the frequency of a term within a document
* @return a score factor based on a term's within-document frequency
*/
public abstract float tf(float freq);
Computes a score factor for a simple term and returns an explanation
for that score factor.
The default implementation uses:
idf(docFreq, docCount);
Note that CollectionStatistics.docCount() is used instead of IndexReader#numDocs() because also TermStatistics.docFreq() is used, and when the latter is inaccurate, so is CollectionStatistics.docCount(), and in the same direction. In addition, CollectionStatistics.docCount() does not skew when fields are sparse. Params: - 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.
/**
* Computes a score factor for a simple term and returns an explanation
* for that score factor.
*
* <p>
* The default implementation uses:
*
* <pre class="prettyprint">
* idf(docFreq, docCount);
* </pre>
*
* Note that {@link CollectionStatistics#docCount()} is used instead of
* {@link org.apache.lucene.index.IndexReader#numDocs() IndexReader#numDocs()} because also
* {@link TermStatistics#docFreq()} is used, and when the latter
* is inaccurate, so is {@link CollectionStatistics#docCount()}, and in the same direction.
* In addition, {@link CollectionStatistics#docCount()} does not skew when fields are sparse.
*
* @param collectionStats collection-level statistics
* @param termStats term-level statistics for the term
* @return an Explain object that includes both an idf score factor
and an explanation for the term.
*/
public Explanation idfExplain(CollectionStatistics collectionStats, TermStatistics termStats) {
final long df = termStats.docFreq();
final long docCount = collectionStats.docCount();
final float idf = idf(df, docCount);
return Explanation.match(idf, "idf(docFreq, docCount)",
Explanation.match(df, "docFreq, number of documents containing term"),
Explanation.match(docCount, "docCount, total number of documents with field"));
}
Computes a score factor for a phrase.
The default implementation sums the idf factor for
each term in the phrase.
Params: - 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.
/**
* Computes a score factor for a phrase.
*
* <p>
* The default implementation sums the idf factor for
* each term in the phrase.
*
* @param collectionStats collection-level statistics
* @param termStats term-level statistics for the terms in the phrase
* @return an Explain object that includes both an idf
* score factor for the phrase and an explanation
* for each term.
*/
public Explanation idfExplain(CollectionStatistics collectionStats, TermStatistics termStats[]) {
double idf = 0d; // sum into a double before casting into a float
List<Explanation> subs = new ArrayList<>();
for (final TermStatistics stat : termStats ) {
Explanation idfExplain = idfExplain(collectionStats, stat);
subs.add(idfExplain);
idf += idfExplain.getValue().floatValue();
}
return Explanation.match((float) idf, "idf(), sum of:", subs);
}
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(float) 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.
Params: - docFreq – the number of documents which contain the term
- docCount – the total number of documents in the collection
Returns: a score factor based on the term's document frequency
/** 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
* {@link #tf(float)} factor for each term in the query and these products are
* then summed to form the initial score for a document.
*
* <p>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.
*
* @param docFreq the number of documents which contain the term
* @param docCount the total number of documents in the collection
* @return a score factor based on the term's document frequency
*/
public abstract float idf(long docFreq, long docCount);
Compute an index-time normalization value for this field instance.
Params: - length – the number of terms in the field, optionally
discounting overlaps
Returns: a length normalization value
/**
* Compute an index-time normalization value for this field instance.
*
* @param length the number of terms in the field, optionally {@link #setDiscountOverlaps(boolean) discounting overlaps}
* @return a length normalization value
*/
public abstract float lengthNorm(int length);
@Override
public final long computeNorm(FieldInvertState state) {
final int numTerms;
if (state.getIndexOptions() == IndexOptions.DOCS && state.getIndexCreatedVersionMajor() >= 8) {
numTerms = state.getUniqueTermCount();
} else if (discountOverlaps) {
numTerms = state.getLength() - state.getNumOverlap();
} else {
numTerms = state.getLength();
}
return SmallFloat.intToByte4(numTerms);
}
@Override
public final SimScorer scorer(float boost, CollectionStatistics collectionStats, TermStatistics... termStats) {
final Explanation idf = termStats.length == 1
? idfExplain(collectionStats, termStats[0])
: idfExplain(collectionStats, termStats);
float[] normTable = new float[256];
for (int i = 1; i < 256; ++i) {
int length = SmallFloat.byte4ToInt((byte) i);
float norm = lengthNorm(length);
normTable[i] = norm;
}
normTable[0] = 1f / normTable[255];
return new TFIDFScorer(boost, idf, normTable);
}
Collection statistics for the TF-IDF model. The only statistic of interest
to this model is idf. /** Collection statistics for the TF-IDF model. The only statistic of interest
* to this model is idf. */
class TFIDFScorer extends SimScorer {
The idf and its explanation /** The idf and its explanation */
private final Explanation idf;
private final float boost;
private final float queryWeight;
final float[] normTable;
public TFIDFScorer(float boost, Explanation idf, float[] normTable) {
// TODO: Validate?
this.idf = idf;
this.boost = boost;
this.queryWeight = boost * idf.getValue().floatValue();
this.normTable = normTable;
}
@Override
public float score(float freq, long norm) {
final float raw = tf(freq) * queryWeight; // compute tf(f)*weight
float normValue = normTable[(int) (norm & 0xFF)];
return raw * normValue; // normalize for field
}
@Override
public Explanation explain(Explanation freq, long norm) {
return explainScore(freq, norm, normTable);
}
private Explanation explainScore(Explanation freq, long encodedNorm, float[] normTable) {
List<Explanation> subs = new ArrayList<Explanation>();
if (boost != 1F) {
subs.add(Explanation.match(boost, "boost"));
}
subs.add(idf);
Explanation tf = Explanation.match(tf(freq.getValue().floatValue()), "tf(freq="+freq.getValue()+"), with freq of:", freq);
subs.add(tf);
float norm = normTable[(int) (encodedNorm & 0xFF)];
Explanation fieldNorm = Explanation.match(norm, "fieldNorm");
subs.add(fieldNorm);
return Explanation.match(
queryWeight * tf.getValue().floatValue() * norm,
"score(freq="+freq.getValue()+"), product of:",
subs);
}
}
}