lucene实战--打分算法没有那么难？
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准备工作
1.1 下载最新源码，https://github.com/apache/lucene-solr
1.2 编译，按照说明，使用ant进行编译(我使用了ant eclipse)
1.3.将编译后的文件导入到eclipse，sts或者idea中
2.新建测试类
public void test() throws IOException, ParseException {
Analyzer analyzer = new NGramAnalyzer();
// Store the index in memory:
Directory directory = new RAMDirectory();
// To store an index on disk, use this instead:
//Path path = FileSystems.getDefault().getPath("E:\demo\data", "access.data");
//Directory directory = FSDirectory.open(path);
IndexWriterConfig config = new IndexWriterConfig(analyzer);
IndexWriter iwriter = new IndexWriter(directory, config);
Document doc = new Document();
String text = "我是中国人.";
doc.add(new Field("fieldname", text, TextField.TYPE_STORED));
iwriter.addDocument(doc);
iwriter.close();
// Now search the index:
DirectoryReader ireader = DirectoryReader.open(directory);
IndexSearcher isearcher = new IndexSearcher(ireader);
isearcher.setSimilarity(new BM25Similarity());
// Parse a simple query that searches for "text":
QueryParser parser = new QueryParser("fieldname", analyzer);
Query query = parser.parse("中国,人");
ScoreDoc[] hits = isearcher.search(query, 1000).scoreDocs;
// Iterate through the results:
for (int i = 0; i < hits.length; i++) {
Document hitDoc = isearcher.doc(hits[i].doc);
System.out.println(hitDoc.getFields().toString());
}
ireader.close();
directory.close();
}
private static class NGramAnalyzer extends Analyzer {
@Override
protected TokenStreamComponents createComponents(String fieldName) {
final Tokenizer tokenizer = new KeywordTokenizer();
return new TokenStreamComponents(tokenizer, new NGramTokenFilter(tokenizer, 1, 4, true));
}
}其中，分词使用自定义的NGramAnalyzer，它继承自Analyzer，Analyzer分析文本，并将文本转换为TokenStream。详细如下：
/**
* An Analyzer builds TokenStreams, which analyze text. It thus represents a
* policy for extracting index terms from text.
* <p>
* In order to define what analysis is done, subclasses must define their
* {@link TokenStreamComponents TokenStreamComponents} in {@link #createComponents(String)}.
* The components are then reused in each call to {@link #tokenStream(String, Reader)}.
* <p>
* Simple example:
* <pre class="prettyprint">
* Analyzer analyzer = new Analyzer() {
* {@literal @Override}
* protected TokenStreamComponents createComponents(String fieldName) {
* Tokenizer source = new FooTokenizer(reader);
* TokenStream filter = new FooFilter(source);
* filter = new BarFilter(filter);
* return new TokenStreamComponents(source, filter);
* }
* {@literal @Override}
* protected TokenStream normalize(TokenStream in) {
* // Assuming FooFilter is about normalization and BarFilter is about
* // stemming, only FooFilter should be applied
* return new FooFilter(in);
* }
* };
* </pre>
* For more examples, see the {@link org.apache.lucene.analysis Analysis package documentation}.
* <p>
* For some concrete implementations bundled with Lucene, look in the analysis modules:
* <ul>
* <li><a href="{@docRoot}/../analyzers-common/overview-summary.html">Common</a>:
* Analyzers for indexing content in different languages and domains.
* <li><a href="{@docRoot}/../analyzers-icu/overview-summary.html">ICU</a>:
* Exposes functionality from ICU to Apache Lucene.
* <li><a href="{@docRoot}/../analyzers-kuromoji/overview-summary.html">Kuromoji</a>:
* Morphological analyzer for Japanese text.
* <li><a href="{@docRoot}/../analyzers-morfologik/overview-summary.html">Morfologik</a>:
* Dictionary-driven lemmatization for the Polish language.
* <li><a href="{@docRoot}/../analyzers-phonetic/overview-summary.html">Phonetic</a>:
* Analysis for indexing phonetic signatures (for sounds-alike search).
* <li><a href="{@docRoot}/../analyzers-smartcn/overview-summary.html">Smart Chinese</a>:
* Analyzer for Simplified Chinese, which indexes words.
* <li><a href="{@docRoot}/../analyzers-stempel/overview-summary.html">Stempel</a>:
* Algorithmic Stemmer for the Polish Language.
* </ul>
*
* @since 3.1
*/ClassicSimilarity是TFIDFSimilarity的封装，因TFIDFSimilarity是抽象方法，无法直接new出实例.这个算法是lucene早期的默认打分实现。
将测试类放入solr-lucene源码中，并进行debug，如果想要分析TFIDF算法，可以直接new ClassicSimilarity 然后放入IndexSearch，其它的类似。
3.算法介绍
新版的lucene使用了BM25Similarity作为默认打分实现。这里显式使用了BM25Similarity，算法详细。这里简要介绍一下：
其中:
D即文档(Document),Q即查询语句(Query),score(D,Q)指使用Q的查询语句在该文档下的打分函数。
IDF即倒排文件频次(Inverse Document Frequency)指在倒排文档中出现的次数，qi是Q分词后term
其中，N是总的文档数目，n(qi)是出现分词qi的文档数目。f(qi,D)是qi分词在文档Document出现的频次
k1和b是可调参数，默认值为1.2，0.75
|D|是文档的单词的个数，avgdl 指库里的平均文档长度。
4.算法实现
1.IDF实现
单个IDF实现
/** Implemented as <code>log(1 + (docCount - docFreq + 0.5)/(docFreq + 0.5))</code>. */
protected float idf(long docFreq, long docCount) {
return (float) Math.log(1 + (docCount - docFreq + 0.5D)/(docFreq + 0.5D));
}IDF的集合实现
@Override
public final SimWeight computeWeight(float boost, CollectionStatistics collectionStats, TermStatistics... termStats) {
Explanation idf = termStats.length == 1 ? idfExplain(collectionStats, termStats[0]) : idfExplain(collectionStats, termStats);
float avgdl = avgFieldLength(collectionStats);
float[] oldCache = new float[256];
float[] cache = new float[256];
for (int i = 0; i < cache.length; i++) {
oldCache[i] = k1 * ((1 - b) + b * OLD_LENGTH_TABLE[i] / avgdl);
cache[i] = k1 * ((1 - b) + b * LENGTH_TABLE[i] / avgdl);
}
return new BM25Stats(collectionStats.field(), boost, idf, avgdl, oldCache, cache);
}
/**
* 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> details = new ArrayList<>();
for (final TermStatistics stat : termStats ) {
Explanation idfExplain = idfExplain(collectionStats, stat);
details.add(idfExplain);
idf += idfExplain.getValue();
}
return Explanation.match((float) idf, "idf(), sum of:", details);
}
2.k1和b参数实现
public BM25Similarity(float k1, float b) {
if (Float.isFinite(k1) == false || k1 < 0) {
throw new IllegalArgumentException("illegal k1 value: " + k1 + ", must be a non-negative finite value");
}
if (Float.isNaN(b) || b < 0 || b > 1) {
throw new IllegalArgumentException("illegal b value: " + b + ", must be between 0 and 1");
}
this.k1 = k1;
this.b = b;
}
/** BM25 with these default values:
* <ul>
* <li>{@code k1 = 1.2}</li>
* <li>{@code b = 0.75}</li>
* </ul>
*/
public BM25Similarity() {
this(1.2f, 0.75f);
}
3.平均文档长度avgdl 计算
/** The default implementation computes the average as <code>sumTotalTermFreq / docCount</code> */
protected float avgFieldLength(CollectionStatistics collectionStats) {
final long sumTotalTermFreq;
if (collectionStats.sumTotalTermFreq() == -1) {
// frequencies are omitted (tf=1), its # of postings
if (collectionStats.sumDocFreq() == -1) {
// theoretical case only: remove!
return 1f;
}
sumTotalTermFreq = collectionStats.sumDocFreq();
} else {
sumTotalTermFreq = collectionStats.sumTotalTermFreq();
}
final long docCount = collectionStats.docCount() == -1 ? collectionStats.maxDoc() : collectionStats.docCount();
return (float) (sumTotalTermFreq / (double) docCount);
}4.参数Weigh的计算
/** Cache of decoded bytes. */
private static final float[] OLD_LENGTH_TABLE = new float[256];
private static final float[] LENGTH_TABLE = new float[256];
static {
for (int i = 1; i < 256; i++) {
float f = SmallFloat.byte315ToFloat((byte)i);
OLD_LENGTH_TABLE[i] = 1.0f / (f*f);
}
OLD_LENGTH_TABLE[0] = 1.0f / OLD_LENGTH_TABLE[255]; // otherwise inf
for (int i = 0; i < 256; i++) {
LENGTH_TABLE[i] = SmallFloat.byte4ToInt((byte) i);
}
}
@Override
public final SimWeight computeWeight(float boost, CollectionStatistics collectionStats, TermStatistics... termStats) {
Explanation idf = termStats.length == 1 ? idfExplain(collectionStats, termStats[0]) : idfExplain(collectionStats, termStats);
float avgdl = avgFieldLength(collectionStats);
float[] oldCache = new float[256];
float[] cache = new float[256];
for (int i = 0; i < cache.length; i++) {
oldCache[i] = k1 * ((1 - b) + b * OLD_LENGTH_TABLE[i] / avgdl);
cache[i] = k1 * ((1 - b) + b * LENGTH_TABLE[i] / avgdl);
}
return new BM25Stats(collectionStats.field(), boost, idf, avgdl, oldCache, cache);
}相当于 
5.WeightValue计算
BM25Stats(String field, float boost, Explanation idf, float avgdl, float[] oldCache, float[] cache) {
this.field = field;
this.boost = boost;
this.idf = idf;
this.avgdl = avgdl;
this.weight = idf.getValue() * boost;
this.oldCache = oldCache;
this.cache = cache;
}
BM25DocScorer(BM25Stats stats, int indexCreatedVersionMajor, NumericDocValues norms) throws IOException {
this.stats = stats;
this.weightValue = stats.weight * (k1 + 1);
this.norms = norms;
if (indexCreatedVersionMajor >= 7) {
lengthCache = LENGTH_TABLE;
cache = stats.cache;
} else {
lengthCache = OLD_LENGTH_TABLE;
cache = stats.oldCache;
}
}
相当于
红色部分相乘
6.总的得分计算
@Override
public float score(int doc, float freq) throws IOException {
// if there are no norms, we act as if b=0
float norm;
if (norms == null) {
norm = k1;
} else {
if (norms.advanceExact(doc)) {
norm = cache[((byte) norms.longValue()) & 0xFF];
} else {
norm = cache[0];
}
}
return weightValue * freq / (freq + norm);
}其中norm是从cache里取的，cache是放入了
那么整个公式就完整的出来了
7.深入
打分的数据来源于CollectionStatistics,TermStatistics及freq，那么它们是哪里得到的？
SynonymWeight(Query query, IndexSearcher searcher, float boost) throws IOException {
super(query);
CollectionStatistics collectionStats = searcher.collectionStatistics(terms[0].field());//1
long docFreq = 0;
long totalTermFreq = 0;
termContexts = new TermContext[terms.length];
for (int i = 0; i < termContexts.length; i++) {
termContexts[i] = TermContext.build(searcher.getTopReaderContext(), terms[i]);
TermStatistics termStats = searcher.termStatistics(terms[i], termContexts[i]);//2
docFreq = Math.max(termStats.docFreq(), docFreq);
if (termStats.totalTermFreq() == -1) {
totalTermFreq = -1;
} else if (totalTermFreq != -1) {
totalTermFreq += termStats.totalTermFreq();
}
}
TermStatistics[] statics=new TermStatistics[terms.length];
for(int i=0;i<terms.length;i++) {
TermStatistics pseudoStats = new TermStatistics(terms[i].bytes(), docFreq, totalTermFreq,query.getKeyword());
statics[i]=pseudoStats;
}
this.similarity = searcher.getSimilarity(true);
this.simWeight = similarity.computeWeight(boost, collectionStats, statics);
}
CollectionStatistics的来源
/**
* Returns {@link CollectionStatistics} for a field.
*
* This can be overridden for example, to return a field‘s statistics
* across a distributed collection.
* @lucene.experimental
*/
public CollectionStatistics collectionStatistics(String field) throws IOException {
final int docCount;
final long sumTotalTermFreq;
final long sumDocFreq;
assert field != null;
Terms terms = MultiFields.getTerms(reader, field);
if (terms == null) {
docCount = 0;
sumTotalTermFreq = 0;
sumDocFreq = 0;
} else {
docCount = terms.getDocCount();
sumTotalTermFreq = terms.getSumTotalTermFreq();
sumDocFreq = terms.getSumDocFreq();
}
return new CollectionStatistics(field, reader.maxDoc(), docCount, sumTotalTermFreq, sumDocFreq);
}TermStatistics的来源
/**
* Returns {@link TermStatistics} for a term.
*
* This can be overridden for example, to return a term‘s statistics
* across a distributed collection.
* @lucene.experimental
*/
public TermStatistics termStatistics(Term term, TermContext context) throws IOException {
return new TermStatistics(term.bytes(), context.docFreq(), context.totalTermFreq(),term.text());
}freq的来源(tf)
@Override
protected float score(DisiWrapper topList) throws IOException {
return similarity.score(topList.doc, tf(topList));
}
/** combines TF of all subs. */
final int tf(DisiWrapper topList) throws IOException {
int tf = 0;
for (DisiWrapper w = topList; w != null; w = w.next) {
tf += ((TermScorer)w.scorer).freq();
}
return tf;
}底层实现
Lucene50PostingsReader.BlockPostingsEnum
@Override
public int nextDoc() throws IOException {
if (docUpto == docFreq) {
return doc = NO_MORE_DOCS;
}
if (docBufferUpto == BLOCK_SIZE) {
refillDocs();
}
accum += docDeltaBuffer[docBufferUpto];
freq = freqBuffer[docBufferUpto];
posPendingCount += freq;
docBufferUpto++;
docUpto++;
doc = accum;
position = 0;
return doc;
}8.总结
BM25算法的全称是 Okapi BM25，是一种二元独立模型的扩展，也可以用来做搜索的相关度排序。本文通过和lucene的BM25Similarity的实现来深入理解整个打分公式。
在此基础之上，又分析了CollectionStatistics,TermStatistics及freq这些参数是如何计算的。
通过整个分析过程，我们想要定制自己的打分公式，只需要实现Similarity或者SimilarityBase类，然后实现业务上的打分公式即可。
参考文献
【1】https://en.wikipedia.org/wiki/Okapi_BM25
【2】https://www.elastic.co/cn/blog/found-bm-vs-lucene-default-similarity
【3】http://www.blogjava.net/hoojo/archive/2012/09/06/387140.html
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