lucene的评分机制：所有hits的分数<=1.0

每个document（d）的分数：

∑tf(t in d)*idf(t)*boost(t.field in d)*lengthNorm(t.field in d)

t In q

 

查询的得分：

score(q,d)=coord(q,d)·queryNorm(q)·∑tf(t in d)*idf(t)*boost(t.field in d)*lengthNorm(t.field in d)

t In q

 

tf(t in d):文档中d出现搜索项t的频率

idf(t):搜索项t在倒排文档中出现的频率

boost（t.field in d）：域的加权因子，在插入文档中设置

lengthNorm（t.field in d):域的标准化值，即在某一域中所有项的个数。通常在索引时计算该值并存储到索引文件中。

coord（q，d）：协调因子（normalization value），该因子的值基于文档中包含查询项的个数

queryNorm（q）：每个查询的标准化值，指每个查询项的权重的平方和

 

 

 

query对象的加权因子，查询时如果是多个子句，则可以通过加权某一个查询子句来加权某一个query对象。

 

DefaultSimilarity.java默认处理计分规则/** Expert: Default scoring implementation. */

public class DefaultSimilarity extends Similarity {  /** Implemented as   *  state.getBoost()*lengthNorm(numTerms), where   *  numTerms is {@link FieldInvertState#getLength()} if {@link   *  #setDiscountOverlaps} is false, else it's {@link   *  FieldInvertState#getLength()} - {@link   *  FieldInvertState#getNumOverlap()}.   *   *  @lucene.experimental */  @Override  public float computeNorm(String field, FieldInvertState state) {    final int numTerms;    if (discountOverlaps)      numTerms = state.getLength() - state.getNumOverlap();    else      numTerms = state.getLength();    return state.getBoost() * ((float) (1.0 / Math.sqrt(numTerms)));  }    /** Implemented as 1/sqrt(sumOfSquaredWeights). */  @Override  public float queryNorm(float sumOfSquaredWeights) {    return (float)(1.0 / Math.sqrt(sumOfSquaredWeights));  }  /** Implemented as sqrt(freq). */  @Override  public float tf(float freq) {    return (float)Math.sqrt(freq);  }      /** Implemented as 1 / (distance + 1). */  @Override  public float sloppyFreq(int distance) {    return 1.0f / (distance + 1);  }      /** Implemented as log(numDocs/(docFreq+1)) + 1. */  @Override  public float idf(int docFreq, int numDocs) {    return (float)(Math.log(numDocs/(double)(docFreq+1)) + 1.0);  }      /** Implemented as overlap / maxOverlap. */  @Override  public float coord(int overlap, int maxOverlap) {    return overlap / (float)maxOverlap;  }  // Default true  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   */  public void setDiscountOverlaps(boolean v) {    discountOverlaps = v;  }  /** @see #setDiscountOverlaps */  public boolean getDiscountOverlaps() {    return discountOverlaps;  }}

IndexSearcher.java的explain方法返回的Explanation对象包含了所有评分因子中各个因子的详细信息。

测试程序和数据参考

import java.io.File;import java.io.IOException;import org.apache.lucene.analysis.Analyzer;import org.apache.lucene.analysis.standard.StandardAnalyzer;import org.apache.lucene.document.Document;import org.apache.lucene.index.IndexReader;import org.apache.lucene.queryParser.ParseException;import org.apache.lucene.queryParser.QueryParser;import org.apache.lucene.search.Explanation;import org.apache.lucene.search.IndexSearcher;import org.apache.lucene.search.Query;import org.apache.lucene.search.ScoreDoc;import org.apache.lucene.store.Directory;import org.apache.lucene.store.FSDirectory;import org.apache.lucene.util.Version;public class DocSearch {	private static IndexSearcher isearcher = null;	public static void search(String key) throws IOException, ParseException{		 Directory directory = FSDirectory.open(new File("E:\\output\\lucence\\index"));		 // Now search the index:	    IndexReader ireader = IndexReader.open(directory); // read-only=true	    isearcher  = new IndexSearcher(ireader);	    // Parse a simple query that searches for "text":	    Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_CURRENT);	    QueryParser parser = new QueryParser(Version.LUCENE_CURRENT,"context", analyzer);	    Query query = parser.parse(key);	    ScoreDoc[] hits = isearcher.search(query, null, 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.getValues("id")[0] + "\t" + hitDoc.getValues("context")[0] + "\t" + hits[i].score);	      Explanation explan = isearcher.explain(query, hits[i].doc);	      System.out.println(explan);	    }	}		public static void main(String[] args) throws IOException, ParseException {		search("旧水泥袋");		isearcher.close();	}	}

输出结果：

只截取第一篇文档的评分信息

4801857        采购旧编织袋、旧水泥袋        4.01721144.0172114 = (MATCH) sum of:  1.4140004 = (MATCH) weight(context:旧 in 1682), product of:    0.54585564 = queryWeight(context:旧), product of:      5.861472 = idf(docFreq=13, maxDocs=1809)      0.09312603 = queryNorm    2.5904293 = (MATCH) fieldWeight(context:旧 in 1682), product of:      1.4142135 = tf(termFreq(context:旧)=2)      5.861472 = idf(docFreq=13, maxDocs=1809)      0.3125 = fieldNorm(field=context, doc=1682)  0.60229266 = (MATCH) weight(context:水 in 1682), product of:    0.42365694 = queryWeight(context:水), product of:      4.549286 = idf(docFreq=51, maxDocs=1809)      0.09312603 = queryNorm    1.4216518 = (MATCH) fieldWeight(context:水 in 1682), product of:      1.0 = tf(termFreq(context:水)=1)      4.549286 = idf(docFreq=51, maxDocs=1809)      0.3125 = fieldNorm(field=context, doc=1682)  1.1562659 = (MATCH) weight(context:泥 in 1682), product of:    0.58700174 = queryWeight(context:泥), product of:      6.3033047 = idf(docFreq=8, maxDocs=1809)      0.09312603 = queryNorm    1.9697827 = (MATCH) fieldWeight(context:泥 in 1682), product of:      1.0 = tf(termFreq(context:泥)=1)      6.3033047 = idf(docFreq=8, maxDocs=1809)      0.3125 = fieldNorm(field=context, doc=1682)  0.84465253 = (MATCH) weight(context:袋 in 1682), product of:    0.42188305 = queryWeight(context:袋), product of:      4.5302377 = idf(docFreq=52, maxDocs=1809)      0.09312603 = queryNorm    2.0021012 = (MATCH) fieldWeight(context:袋 in 1682), product of:      1.4142135 = tf(termFreq(context:袋)=2)      4.5302377 = idf(docFreq=52, maxDocs=1809)      0.3125 = fieldNorm(field=context, doc=1682)