package org.apache.lucene.search;
/**
* 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.
*/
import org.apache.lucene.index.FieldInvertState;
import org.apache.lucene.index.Term;
import org.apache.lucene.search.Explanation.IDFExplanation;
import org.apache.lucene.util.SmallFloat;
import java.io.IOException;
import java.io.Serializable;
import java.util.Collection;
/**
* Expert: Scoring API.
*
* <p>Similarity 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" align="center">
* <tr><td>
* <table cellpadding="1" cellspacing="0" border="1" align="center">
* <tr><td>
* <table cellpadding="2" cellspacing="2" border="0" align="center">
* <tr>
* <td valign="middle" align="right" rowspan="1">
* cosine-similarity(q,d) =
* </td>
* <td valign="middle" align="center">
* <table>
* <tr><td align="center"><small>V(q) · V(d)</small></td></tr>
* <tr><td align="center">–––––––––</td></tr>
* <tr><td align="center"><small>|V(q)| |V(d)|</small></td></tr>
* </table>
* </td>
* </tr>
* </table>
* </td></tr>
* </table>
* </td></tr>
* <tr><td>
* <center><font=-1><u>VSM Score</u></font></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),
* and users can further reward documents matching more query terms
* through a coordination factor, which is usually larger when
* more terms are matched: <i>coord-factor(q,d)</i>.
* </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" align="center">
* <tr><td>
* <table cellpadding="1" cellspacing="0" border="1" align="center">
* <tr><td>
* <table cellpadding="2" cellspacing="2" border="0" align="center">
* <tr>
* <td valign="middle" align="right" rowspan="1">
* score(q,d) =
* <font color="#FF9933">coord-factor(q,d)</font> ·
* <font color="#CCCC00">query-boost(q)</font> ·
* </td>
* <td valign="middle" align="center">
* <table>
* <tr><td align="center"><small><font color="#993399">V(q) · V(d)</font></small></td></tr>
* <tr><td align="center">–––––––––</td></tr>
* <tr><td align="center"><small><font color="#FF33CC">|V(q)|</font></small></td></tr>
* </table>
* </td>
* <td valign="middle" align="right" rowspan="1">
* · <font color="#3399FF">doc-len-norm(d)</font>
* · <font color="#3399FF">doc-boost(d)</font>
* </td>
* </tr>
* </table>
* </td></tr>
* </table>
* </td></tr>
* <tr><td>
* <center><font=-1><u>Lucene Conceptual Scoring Formula</u></font></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>
*
* <li>Applying query normalization on the scores helps to keep the
* scores around the unit vector, hence preventing loss of score data
* because of floating point precision limitations.
* </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:
*
* <P>
* <table cellpadding="2" cellspacing="2" border="0" align="center">
* <tr><td>
* <table cellpadding="" cellspacing="2" border="2" align="center">
* <tr><td>
* <table cellpadding="2" cellspacing="2" border="0" align="center">
* <tr>
* <td valign="middle" align="right" rowspan="1">
* score(q,d) =
* <A HREF="#formula_coord"><font color="#FF9933">coord(q,d)</font></A> ·
* <A HREF="#formula_queryNorm"><font color="#FF33CC">queryNorm(q)</font></A> ·
* </td>
* <td valign="bottom" align="center" rowspan="1">
* <big><big><big>∑</big></big></big>
* </td>
* <td valign="middle" align="right" rowspan="1">
* <big><big>(</big></big>
* <A HREF="#formula_tf"><font color="#993399">tf(t in d)</font></A> ·
* <A HREF="#formula_idf"><font color="#993399">idf(t)</font></A><sup>2</sup> ·
* <A HREF="#formula_termBoost"><font color="#CCCC00">t.getBoost()</font></A> ·
* <A HREF="#formula_norm"><font color="#3399FF">norm(t,d)</font></A>
* <big><big>)</big></big>
* </td>
* </tr>
* <tr valigh="top">
* <td></td>
* <td align="center"><small>t in q</small></td>
* <td></td>
* </tr>
* </table>
* </td></tr>
* </table>
* </td></tr>
* <tr><td>
* <center><font=-1><u>Lucene Practical Scoring Function</u></font></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.DefaultSimilarity#tf(float) DefaultSimilarity} is:
*
* <br> <br>
* <table cellpadding="2" cellspacing="2" border="0" align="center">
* <tr>
* <td valign="middle" align="right" rowspan="1">
* {@link org.apache.lucene.search.DefaultSimilarity#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.DefaultSimilarity#idf(int, int) DefaultSimilarity} is:
*
* <br> <br>
* <table cellpadding="2" cellspacing="2" border="0" align="center">
* <tr>
* <td valign="middle" align="right">
* {@link org.apache.lucene.search.DefaultSimilarity#idf(int, int) idf(t)} =
* </td>
* <td valign="middle" align="center">
* 1 + log <big>(</big>
* </td>
* <td valign="middle" align="center">
* <table>
* <tr><td align="center"><small>numDocs</small></td></tr>
* <tr><td align="center">–––––––––</td></tr>
* <tr><td 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_coord"></A>
* <b><i>coord(q,d)</i></b>
* 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
* {@link #coord(int, int) coord(q,d)}
* by the Similarity in effect at search time.
* <br> <br>
* </li>
*
* <li><b>
* <A NAME="formula_queryNorm"></A>
* <i>queryNorm(q)</i>
* </b>
* 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
* {@link org.apache.lucene.search.DefaultSimilarity#queryNorm(float) DefaultSimilarity}
* produces a <a href="http://en.wikipedia.org/wiki/Euclidean_norm#Euclidean_norm">Euclidean norm</a>:
* <br> <br>
* <table cellpadding="1" cellspacing="0" border="0" align="center">
* <tr>
* <td valign="middle" align="right" rowspan="1">
* queryNorm(q) =
* {@link org.apache.lucene.search.DefaultSimilarity#queryNorm(float) queryNorm(sumOfSquaredWeights)}
* =
* </td>
* <td valign="middle" align="center" rowspan="1">
* <table>
* <tr><td align="center"><big>1</big></td></tr>
* <tr><td align="center"><big>
* ––––––––––––––
* </big></td></tr>
* <tr><td align="center">sumOfSquaredWeights<sup><big>½</big></sup></td></tr>
* </table>
* </td>
* </tr>
* </table>
* <br> <br>
*
* The sum of squared weights (of the query terms) is
* computed by the query {@link org.apache.lucene.search.Weight} object.
* For example, a {@link org.apache.lucene.search.BooleanQuery}
* computes this value as:
*
* <br> <br>
* <table cellpadding="1" cellspacing="0" border="0"n align="center">
* <tr>
* <td valign="middle" align="right" rowspan="1">
* {@link org.apache.lucene.search.Weight#sumOfSquaredWeights() sumOfSquaredWeights} =
* {@link org.apache.lucene.search.Query#getBoost() q.getBoost()} <sup><big>2</big></sup>
* ·
* </td>
* <td valign="bottom" align="center" rowspan="1">
* <big><big><big>∑</big></big></big>
* </td>
* <td valign="middle" align="right" rowspan="1">
* <big><big>(</big></big>
* <A HREF="#formula_idf">idf(t)</A> ·
* <A HREF="#formula_termBoost">t.getBoost()</A>
* <big><big>) <sup>2</sup> </big></big>
* </td>
* </tr>
* <tr valigh="top">
* <td></td>
* <td align="center"><small>t in q</small></td>
* <td></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="../../../../../../queryparsersyntax.html#Boosting a Term">query syntax</A>),
* or as set by application calls to
* {@link 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
* {@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.Query#getBoost() getBoost()}.
* <br> <br>
* </li>
*
* <li>
* <A NAME="formula_norm"></A>
* <b><i>norm(t,d)</i></b> encapsulates a few (indexing time) boost and length factors:
*
* <ul>
* <li><b>Document boost</b> - set by calling
* {@link org.apache.lucene.document.Document#setBoost(float) doc.setBoost()}
* before adding the document to the index.
* </li>
* <li><b>Field boost</b> - set by calling
* {@link org.apache.lucene.document.Fieldable#setBoost(float) field.setBoost()}
* before adding the field to a document.
* </li>
* <li>{@link #lengthNorm(String, int) <b>lengthNorm</b>(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.
* </li>
* </ul>
*
* <p>
* 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:
*
* <br> <br>
* <table cellpadding="1" cellspacing="0" border="0"n align="center">
* <tr>
* <td valign="middle" align="right" rowspan="1">
* norm(t,d) =
* {@link org.apache.lucene.document.Document#getBoost() doc.getBoost()}
* ·
* {@link #lengthNorm(String, int) lengthNorm(field)}
* ·
* </td>
* <td valign="bottom" align="center" rowspan="1">
* <big><big><big>∏</big></big></big>
* </td>
* <td valign="middle" align="right" rowspan="1">
* {@link org.apache.lucene.document.Fieldable#getBoost() f.getBoost}()
* </td>
* </tr>
* <tr valigh="top">
* <td></td>
* <td align="center"><small>field <i><b>f</b></i> in <i>d</i> named as <i><b>t</b></i></small></td>
* <td></td>
* </tr>
* </table>
* <br> <br>
* However the resulted <i>norm</i> value is {@link #encodeNormValue(float) encoded} as a single byte
* before being stored.
* At search time, the norm byte value is read from the index
* {@link org.apache.lucene.store.Directory directory} and
* {@link #decodeNormValue(byte) decoded} back to a float <i>norm</i> value.
* This encoding/decoding, while reducing index size, comes with the price of
* precision loss - it is not guaranteed that <i>decode(encode(x)) = x</i>.
* For instance, <i>decode(encode(0.89)) = 0.75</i>.
* <br> <br>
* 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.
* <br> <br>
* 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.
* <br> <br>
* Last, note that search time is too late to modify this <i>norm</i> part of scoring, e.g. by
* using a different {@link Similarity} for search.
* <br> <br>
* </li>
* </ol>
*
* @see #setDefault(Similarity)
* @see org.apache.lucene.index.IndexWriter#setSimilarity(Similarity)
* @see Searcher#setSimilarity(Similarity)
*/
public abstract class Similarity implements Serializable {
/**
* The Similarity implementation used by default.
**/
private static Similarity defaultImpl = new DefaultSimilarity();
public static final int NO_DOC_ID_PROVIDED = -1;
/** Set the default Similarity implementation used by indexing and search
* code.
*
* @see Searcher#setSimilarity(Similarity)
* @see org.apache.lucene.index.IndexWriter#setSimilarity(Similarity)
*/
public static void setDefault(Similarity similarity) {
Similarity.defaultImpl = similarity;
}
/** Return the default Similarity implementation used by indexing and search
* code.
*
* <p>This is initially an instance of {@link DefaultSimilarity}.
*
* @see Searcher#setSimilarity(Similarity)
* @see org.apache.lucene.index.IndexWriter#setSimilarity(Similarity)
*/
public static Similarity getDefault() {
return Similarity.defaultImpl;
}
/** Cache of decoded bytes. */
private static final float[] NORM_TABLE = new float[256];
static {
for (int i = 0; i < 256; i++)
NORM_TABLE[i] = SmallFloat.byte315ToFloat((byte)i);
}
/**
* Decodes a normalization factor stored in an index.
* @see #decodeNormValue(byte)
* @deprecated Use {@link #decodeNormValue} instead.
*/
@Deprecated
public static float decodeNorm(byte b) {
return NORM_TABLE[b & 0xFF]; // & 0xFF maps negative bytes to positive above 127
}
/** Decodes a normalization factor stored in an index.
* @see #encodeNormValue(float)
*/
public float decodeNormValue(byte b) {
return NORM_TABLE[b & 0xFF]; // & 0xFF maps negative bytes to positive above 127
}
/** Returns a table for decoding normalization bytes.
* @see #encodeNormValue(float)
* @see #decodeNormValue(byte)
*
* @deprecated Use instance methods for encoding/decoding norm values to enable customization.
*/
@Deprecated
public static float[] getNormDecoder() {
return NORM_TABLE;
}
/**
* Compute the normalization value for a field, given the accumulated
* state of term processing for this field (see {@link FieldInvertState}).
*
* <p>Implementations should calculate a float value based on the field
* state and then return that value.
*
* <p>For backward compatibility this method by default calls
* {@link #lengthNorm(String, int)} passing
* {@link FieldInvertState#getLength()} as the second argument, and
* then multiplies this value by {@link FieldInvertState#getBoost()}.</p>
*
* @lucene.experimental
*
* @param field field name
* @param state current processing state for this field
* @return the calculated float norm
*/
public float computeNorm(String field, FieldInvertState state) {
return (state.getBoost() * lengthNorm(field, state.getLength()));
}
/** 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.
*
* <p>Matches in longer fields are less precise, so implementations of this
* method usually return smaller values when <code>numTokens</code> is large,
* and larger values when <code>numTokens</code> is small.
*
* <p>Note that the return values are computed under
* {@link org.apache.lucene.index.IndexWriter#addDocument(org.apache.lucene.document.Document)}
* and then stored using
* {@link #encodeNormValue(float)}.
* Thus they have limited precision, and documents
* must be re-indexed if this method is altered.
*
* @param fieldName the name of the field
* @param numTokens the total number of tokens contained in fields named
* <i>fieldName</i> of <i>doc</i>.
* @return a normalization factor for hits on this field of this document
*
* @see org.apache.lucene.document.Field#setBoost(float)
*/
public abstract float lengthNorm(String fieldName, int numTokens);
/** 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).
*
* <p>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.
*
* @param sumOfSquaredWeights the sum of the squares of query term weights
* @return a normalization factor for query weights
*/
public abstract float queryNorm(float sumOfSquaredWeights);
/** Encodes a normalization factor for storage in an index.
*
* <p>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 org.apache.lucene.document.Field#setBoost(float)
* @see org.apache.lucene.util.SmallFloat
*/
public byte encodeNormValue(float f) {
return SmallFloat.floatToByte315(f);
}
/**
* Static accessor kept for backwards compability reason, use encodeNormValue instead.
* @param f norm-value to encode
* @return byte representing the given float
* @deprecated Use {@link #encodeNormValue} instead.
*
* @see #encodeNormValue(float)
*/
@Deprecated
public static byte encodeNorm(float f) {
return SmallFloat.floatToByte315(f);
}
/** Computes a score factor based on a term or phrase's frequency in a
* document. This value is multiplied by the {@link #idf(int, int)}
* 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.
*
* <p>The default implementation calls {@link #tf(float)}.
*
* @param freq the frequency of a term within a document
* @return a score factor based on a term's within-document frequency
*/
public float tf(int freq) {
return tf((float)freq);
}
/** 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 {@link #tf(float)}.
*
* <p>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 PhraseQuery#setSlop(int)
* @param distance the edit distance of this sloppy phrase match
* @return the frequency increment for this match
*/
public abstract float sloppyFreq(int distance);
/** Computes a score factor based on a term or phrase's frequency in a
* document. This value is multiplied by the {@link #idf(int, int)}
* 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.
*
* <p>
* The default implementation uses:
*
* <pre>
* idf(searcher.docFreq(term), searcher.maxDoc());
* </pre>
*
* Note that {@link Searcher#maxDoc()} is used instead of
* {@link org.apache.lucene.index.IndexReader#numDocs() IndexReader#numDocs()} because also
* {@link Searcher#docFreq(Term)} is used, and when the latter
* is inaccurate, so is {@link Searcher#maxDoc()}, and in the same direction.
* In addition, {@link Searcher#maxDoc()} is more efficient to compute
*
* @param term the term in question
* @param searcher the document collection being searched
* @return an IDFExplain object that includes both an idf score factor
and an explanation for the term.
* @throws IOException
*/
public IDFExplanation idfExplain(final Term term, final Searcher searcher) throws IOException {
final int df = searcher.docFreq(term);
final int max = searcher.maxDoc();
final float idf = idf(df, max);
return new IDFExplanation() {
@Override
public String explain() {
return "idf(docFreq=" + df +
", maxDocs=" + max + ")";
}
@Override
public float getIdf() {
return idf;
}};
}
/**
* Computes a score factor for a phrase.
*
* <p>
* The default implementation sums the idf factor for
* each term in the phrase.
*
* @param terms the terms in the phrase
* @param searcher the document collection being searched
* @return an IDFExplain object that includes both an idf
* score factor for the phrase and an explanation
* for each term.
* @throws IOException
*/
public IDFExplanation idfExplain(Collection<Term> terms, Searcher searcher) throws IOException {
final int max = searcher.maxDoc();
float idf = 0.0f;
final StringBuilder exp = new StringBuilder();
for (final Term term : terms ) {
final int df = searcher.docFreq(term);
idf += idf(df, max);
exp.append(" ");
exp.append(term.text());
exp.append("=");
exp.append(df);
}
final float fIdf = idf;
return new IDFExplanation() {
@Override
public float getIdf() {
return fIdf;
}
@Override
public String explain() {
return exp.toString();
}
};
}
/** 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(int)} 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 numDocs the total number of documents in the collection
* @return a score factor based on the term's document frequency
*/
public abstract float idf(int docFreq, int numDocs);
/** Computes a score factor based on the fraction of all query terms that a
* document contains. This value is multiplied into scores.
*
* <p>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.
*
* @param overlap the number of query terms matched in the document
* @param maxOverlap the total number of terms in the query
* @return a score factor based on term overlap with the query
*/
public abstract float coord(int overlap, int maxOverlap);
/**
* 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.
* <p>
* The default implementation returns 1.
*
* @param docId The docId currently being scored. If this value is {@link #NO_DOC_ID_PROVIDED}, then it should be assumed that the PayloadQuery implementation does not provide document information
* @param fieldName The fieldName of the term this payload belongs to
* @param start The start position of the payload
* @param end The end position of the payload
* @param payload The payload byte array to be scored
* @param offset The offset into the payload array
* @param length The length in the array
* @return An implementation dependent float to be used as a scoring factor
*
*/
// TODO: maybe switch this API to BytesRef?
public float scorePayload(int docId, String fieldName, int start, int end, byte [] payload, int offset, int length)
{
return 1;
}
}