package org.apache.lucene.search.similarities;
/*
* 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.
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import java.io.IOException;
import org.apache.lucene.index.AtomicReaderContext;
import org.apache.lucene.index.FieldInvertState;
import org.apache.lucene.index.NumericDocValues;
import org.apache.lucene.search.CollectionStatistics;
import org.apache.lucene.search.Explanation;
import org.apache.lucene.search.TermStatistics;
import org.apache.lucene.util.BytesRef;
import org.apache.lucene.util.SmallFloat;
/**
* BM25 Similarity. Introduced in Stephen E. Robertson, Steve Walker,
* Susan Jones, Micheline Hancock-Beaulieu, and Mike Gatford. Okapi at TREC-3.
* In Proceedings of the Third <b>T</b>ext <b>RE</b>trieval <b>C</b>onference (TREC 1994).
* Gaithersburg, USA, November 1994.
* @lucene.experimental
*/
public class BM25Similarity extends Similarity {
private final float k1;
private final float b;
// TODO: should we add a delta like sifaka.cs.uiuc.edu/~ylv2/pub/sigir11-bm25l.pdf ?
/**
* BM25 with the supplied parameter values.
* @param k1 Controls non-linear term frequency normalization (saturation).
* @param b Controls to what degree document length normalizes tf values.
*/
public BM25Similarity(float k1, float b) {
this.k1 = k1;
this.b = b;
}
/** BM25 with these default values:
* <ul>
* <li>{@code k1 = 1.2},
* <li>{@code b = 0.75}.</li>
* </ul>
*/
public BM25Similarity() {
this.k1 = 1.2f;
this.b = 0.75f;
}
/** Implemented as <code>log(1 + (numDocs - docFreq + 0.5)/(docFreq + 0.5))</code>. */
protected float idf(long docFreq, long numDocs) {
return (float) Math.log(1 + (numDocs - docFreq + 0.5D)/(docFreq + 0.5D));
}
/** Implemented as <code>1 / (distance + 1)</code>. */
protected float sloppyFreq(int distance) {
return 1.0f / (distance + 1);
}
/** The default implementation returns <code>1</code> */
protected float scorePayload(int doc, int start, int end, BytesRef payload) {
return 1;
}
/** The default implementation computes the average as <code>sumTotalTermFreq / maxDoc</code>,
* or returns <code>1</code> if the index does not store sumTotalTermFreq:
* any field that omits frequency information). */
protected float avgFieldLength(CollectionStatistics collectionStats) {
final long sumTotalTermFreq = collectionStats.sumTotalTermFreq();
if (sumTotalTermFreq <= 0) {
return 1f; // field does not exist, or stat is unsupported
} else {
return (float) (sumTotalTermFreq / (double) collectionStats.maxDoc());
}
}
/** The default implementation encodes <code>boost / sqrt(length)</code>
* with {@link SmallFloat#floatToByte315(float)}. This is compatible with
* Lucene's default implementation. If you change this, then you should
* change {@link #decodeNormValue(byte)} to match. */
protected byte encodeNormValue(float boost, int fieldLength) {
return SmallFloat.floatToByte315(boost / (float) Math.sqrt(fieldLength));
}
/** The default implementation returns <code>1 / f<sup>2</sup></code>
* where <code>f</code> is {@link SmallFloat#byte315ToFloat(byte)}. */
protected float decodeNormValue(byte b) {
return NORM_TABLE[b & 0xFF];
}
/**
* True if overlap tokens (tokens with a position of increment of zero) are
* discounted from the document's length.
*/
protected boolean discountOverlaps = true;
/** Sets 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. */
public void setDiscountOverlaps(boolean v) {
discountOverlaps = v;
}
/**
* Returns true if overlap tokens are discounted from the document's length.
* @see #setDiscountOverlaps
*/
public boolean getDiscountOverlaps() {
return discountOverlaps;
}
/** Cache of decoded bytes. */
private static final float[] NORM_TABLE = new float[256];
static {
for (int i = 0; i < 256; i++) {
float f = SmallFloat.byte315ToFloat((byte)i);
NORM_TABLE[i] = 1.0f / (f*f);
}
}
@Override
public final long computeNorm(FieldInvertState state) {
final int numTerms = discountOverlaps ? state.getLength() - state.getNumOverlap() : state.getLength();
return encodeNormValue(state.getBoost(), numTerms);
}
/**
* 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, searcher.maxDoc());
* </pre>
*
* Note that {@link CollectionStatistics#maxDoc()} 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#maxDoc()}, and in the same direction.
* In addition, {@link CollectionStatistics#maxDoc()} is more efficient to compute
*
* @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 max = collectionStats.maxDoc();
final float idf = idf(df, max);
return new Explanation(idf, "idf(docFreq=" + df + ", maxDocs=" + max + ")");
}
/**
* 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[]) {
final long max = collectionStats.maxDoc();
float idf = 0.0f;
final Explanation exp = new Explanation();
exp.setDescription("idf(), sum of:");
for (final TermStatistics stat : termStats ) {
final long df = stat.docFreq();
final float termIdf = idf(df, max);
exp.addDetail(new Explanation(termIdf, "idf(docFreq=" + df + ", maxDocs=" + max + ")"));
idf += termIdf;
}
exp.setValue(idf);
return exp;
}
@Override
public final SimWeight computeWeight(float queryBoost, CollectionStatistics collectionStats, TermStatistics... termStats) {
Explanation idf = termStats.length == 1 ? idfExplain(collectionStats, termStats[0]) : idfExplain(collectionStats, termStats);
float avgdl = avgFieldLength(collectionStats);
// compute freq-independent part of bm25 equation across all norm values
float cache[] = new float[256];
for (int i = 0; i < cache.length; i++) {
cache[i] = k1 * ((1 - b) + b * decodeNormValue((byte)i) / avgdl);
}
return new BM25Stats(collectionStats.field(), idf, queryBoost, avgdl, cache);
}
@Override
public final SimScorer simScorer(SimWeight stats, AtomicReaderContext context) throws IOException {
BM25Stats bm25stats = (BM25Stats) stats;
return new BM25DocScorer(bm25stats, context.reader().getNormValues(bm25stats.field));
}
private class BM25DocScorer extends SimScorer {
private final BM25Stats stats;
private final float weightValue; // boost * idf * (k1 + 1)
private final NumericDocValues norms;
private final float[] cache;
BM25DocScorer(BM25Stats stats, NumericDocValues norms) throws IOException {
this.stats = stats;
this.weightValue = stats.weight * (k1 + 1);
this.cache = stats.cache;
this.norms = norms;
}
@Override
public float score(int doc, float freq) {
// if there are no norms, we act as if b=0
float norm = norms == null ? k1 : cache[(byte)norms.get(doc) & 0xFF];
return weightValue * freq / (freq + norm);
}
@Override
public Explanation explain(int doc, Explanation freq) {
return explainScore(doc, freq, stats, norms);
}
@Override
public float computeSlopFactor(int distance) {
return sloppyFreq(distance);
}
@Override
public float computePayloadFactor(int doc, int start, int end, BytesRef payload) {
return scorePayload(doc, start, end, payload);
}
}
/** Collection statistics for the BM25 model. */
private static class BM25Stats extends SimWeight {
/** BM25's idf */
private final Explanation idf;
/** The average document length. */
private final float avgdl;
/** query's inner boost */
private final float queryBoost;
/** query's outer boost (only for explain) */
private float topLevelBoost;
/** weight (idf * boost) */
private float weight;
/** field name, for pulling norms */
private final String field;
/** precomputed norm[256] with k1 * ((1 - b) + b * dl / avgdl) */
private final float cache[];
BM25Stats(String field, Explanation idf, float queryBoost, float avgdl, float cache[]) {
this.field = field;
this.idf = idf;
this.queryBoost = queryBoost;
this.avgdl = avgdl;
this.cache = cache;
}
@Override
public float getValueForNormalization() {
// we return a TF-IDF like normalization to be nice, but we don't actually normalize ourselves.
final float queryWeight = idf.getValue() * queryBoost;
return queryWeight * queryWeight;
}
@Override
public void normalize(float queryNorm, float topLevelBoost) {
// we don't normalize with queryNorm at all, we just capture the top-level boost
this.topLevelBoost = topLevelBoost;
this.weight = idf.getValue() * queryBoost * topLevelBoost;
}
}
private Explanation explainScore(int doc, Explanation freq, BM25Stats stats, NumericDocValues norms) {
Explanation result = new Explanation();
result.setDescription("score(doc="+doc+",freq="+freq+"), product of:");
Explanation boostExpl = new Explanation(stats.queryBoost * stats.topLevelBoost, "boost");
if (boostExpl.getValue() != 1.0f)
result.addDetail(boostExpl);
result.addDetail(stats.idf);
Explanation tfNormExpl = new Explanation();
tfNormExpl.setDescription("tfNorm, computed from:");
tfNormExpl.addDetail(freq);
tfNormExpl.addDetail(new Explanation(k1, "parameter k1"));
if (norms == null) {
tfNormExpl.addDetail(new Explanation(0, "parameter b (norms omitted for field)"));
tfNormExpl.setValue((freq.getValue() * (k1 + 1)) / (freq.getValue() + k1));
} else {
float doclen = decodeNormValue((byte)norms.get(doc));
tfNormExpl.addDetail(new Explanation(b, "parameter b"));
tfNormExpl.addDetail(new Explanation(stats.avgdl, "avgFieldLength"));
tfNormExpl.addDetail(new Explanation(doclen, "fieldLength"));
tfNormExpl.setValue((freq.getValue() * (k1 + 1)) / (freq.getValue() + k1 * (1 - b + b * doclen/stats.avgdl)));
}
result.addDetail(tfNormExpl);
result.setValue(boostExpl.getValue() * stats.idf.getValue() * tfNormExpl.getValue());
return result;
}
@Override
public String toString() {
return "BM25(k1=" + k1 + ",b=" + b + ")";
}
/**
* Returns the <code>k1</code> parameter
* @see #BM25Similarity(float, float)
*/
public float getK1() {
return k1;
}
/**
* Returns the <code>b</code> parameter
* @see #BM25Similarity(float, float)
*/
public float getB() {
return b;
}
}