Lucene的评分(score)机制研究

首先，需要学习Lucene的评分计算公式——

分值计算方式为查询语句q中每个项t与文档d的匹配分值之和，当然还有权重的因素。其中每一项的意思如下表所示：

通过Searcher.explain(Query query, int doc)方法可以查看某个文档的得分的具体构成。 示例：

public class ScoreSortTest {
    public final static String INDEX_STORE_PATH = "index";
    public static void main(String[] args) throws Exception {
        IndexWriter writer = new IndexWriter(INDEX_STORE_PATH, new StandardAnalyzer(), true);
        writer.setUseCompoundFile(false);

        Document doc1 = new Document();
        Document doc2 = new Document();
        Document doc3 = new Document();

        Field f1 = new Field("bookname","bc bc", Field.Store.YES, Field.Index.TOKENIZED);
        Field f2 = new Field("bookname","ab bc", Field.Store.YES, Field.Index.TOKENIZED);
        Field f3 = new Field("bookname","ab bc cd", Field.Store.YES, Field.Index.TOKENIZED);

        doc1.add(f1);
        doc2.add(f2);
        doc3.add(f3);

        writer.addDocument(doc1);
        writer.addDocument(doc2);
        writer.addDocument(doc3);

        writer.close();

        IndexSearcher searcher = new IndexSearcher(INDEX_STORE_PATH);
        TermQuery q = new TermQuery(new Term("bookname", "bc"));
        q.setBoost(2f);
        Hits hits = searcher.search(q);
        for(int i=0; i<hits.length();i++){
            Document doc = hits.doc(i);
            System.out.print(doc.get("bookname") + "\t\t");
            System.out.println(hits.score(i));
            System.out.println(searcher.explain(q, hits.id(i)));//
        }
    }
}

运行结果：

bc bc    0.629606
0.629606 = (MATCH) fieldWeight(bookname:bc in 0), product of:
  1.4142135 = tf(termFreq(bookname:bc)=2)
  0.71231794 = idf(docFreq=3, numDocs=3)
  0.625 = fieldNorm(field=bookname, doc=0) 

ab bc    0.4451987
0.4451987 = (MATCH) fieldWeight(bookname:bc in 1), product of:
  1.0 = tf(termFreq(bookname:bc)=1)
  0.71231794 = idf(docFreq=3, numDocs=3)
  0.625 = fieldNorm(field=bookname, doc=1) 

ab bc cd    0.35615897
0.35615897 = (MATCH) fieldWeight(bookname:bc in 2), product of:
  1.0 = tf(termFreq(bookname:bc)=1)
  0.71231794 = idf(docFreq=3, numDocs=3)
  0.5 = fieldNorm(field=bookname, doc=2) 

涉及到的源码：

idf的计算

idf是项在倒排文档中出现的频率，计算方式为

1. /** Implemented as <code>log(numDocs/(docFreq+1)) + 1</code>. */
2. @Override
3. public float idf(long docFreq, long numDocs) {
4. return (float)(Math.log(numDocs/(double)(docFreq+1)) + 1.0);
5. }

docFreq是根据指定关键字进行检索，检索到的Document的数量，我们测试的docFreq=14；numDocs是指索引文件中总共的Document的数量，我们测试的numDocs=1453。用计算器验证一下，没有错误，这里就不啰嗦了。

queryNorm的计算

queryNorm的计算在DefaultSimilarity类中实现，如下所示：

1. /** Implemented as <code>1/sqrt(sumOfSquaredWeights)</code>. */
2. public float queryNorm(float sumOfSquaredWeights) {
3. return (float)(1.0 / Math.sqrt(sumOfSquaredWeights));
4. }

这里，sumOfSquaredWeights的计算是在org.apache.lucene.search.TermQuery.TermWeight类中的sumOfSquaredWeights方法实现：

1. public float sumOfSquaredWeights() {
2. queryWeight = idf * getBoost();             // compute query weight
3. return queryWeight * queryWeight;          // square it
4. }

其实默认情况下，sumOfSquaredWeights = idf * idf，因为Lucune中默认的boost = 1.0。

fieldWeight的计算

在org/apache/lucene/search/similarities/TFIDFSimilarity.java的explainScore方法中有：

1. // explain field weight
2. Explanation fieldExpl = new Explanation();
3. fieldExpl.setDescription("fieldWeight in "+doc+
4. ", product of:");
5. Explanation tfExplanation = new Explanation();
6. tfExplanation.setValue(tf(freq.getValue()));
7. tfExplanation.setDescription("tf(freq="+freq.getValue()+"), with freq of:");
8. tfExplanation.addDetail(freq);
9. fieldExpl.addDetail(tfExplanation);
10. fieldExpl.addDetail(stats.idf);
11. Explanation fieldNormExpl = new Explanation();
12. float fieldNorm = norms != null ? decodeNormValue(norms.get(doc)) : 1.0f;
13. fieldNormExpl.setValue(fieldNorm);
14. fieldNormExpl.setDescription("fieldNorm(doc="+doc+")");
15. fieldExpl.addDetail(fieldNormExpl);
16. fieldExpl.setValue(tfExplanation.getValue() *
17. stats.idf.getValue() *
18. fieldNormExpl.getValue());
19. result.addDetail(fieldExpl);

重点是这一句：

1. fieldExpl.setValue(tfExplanation.getValue() *
2. stats.idf.getValue() *
3. fieldNormExpl.getValue());

使用计算式表示就是

fieldWeight = tf * idf * fieldNorm

tf和idf的计算参考前面的，fieldNorm的计算在索引的时候确定了，此时直接从索引文件中读取，这个方法并没有给出直接的计算。如果使用DefaultSimilarity的话，它实际上就是lengthNorm，域越长的话Norm越小，在org/apache/lucene/search/similarities/DefaultSimilarity.java里面有关于它的计算：

1. public float lengthNorm(FieldInvertState state) {
2. final int numTerms;
3. if (discountOverlaps)
4. numTerms = state.getLength() - state.getNumOverlap();
5. else
6. numTerms = state.getLength();
7. return state.getBoost() * ((float) (1.0 / Math.sqrt(numTerms)));
8. }

参考文献：

【1】http://www.hankcs.com/program/java/lucene-scoring-algorithm-explained.html

【2】http://grantbb.iteye.com/blog/181802