Lucene .Net, custom scoring

Question

I have the following Lucene Explanation:

{1.25 = (MATCH) sum of:

  0.5 = (MATCH) weight(Caption:vrom^0.5 in 0) [MySimilarity], result of:
    0.5 = score(doc=0,freq=1 = termFreq=1
), product of:
      0.5 = queryWeight, product of:
        0.5 = boost
        1 = idf(docFreq=1, maxDocs=4)
        1 = queryNorm
      1 = fieldWeight in 0, product of:
        1 = tf(freq=1), with freq of:
          1 = termFreq=1
        1 = idf(docFreq=1, maxDocs=4)
        1 = fieldNorm(doc=0)

  0.75 = (MATCH) weight(Caption:vroma^0.75 in 0) [MySimilarity], result of:
    0.75 = score(doc=0,freq=1 = termFreq=1
), product of:
      0.75 = queryWeight, product of:
        0.75 = boost
        1 = idf(docFreq=1, maxDocs=4)
        1 = queryNorm
      1 = fieldWeight in 0, product of:
        1 = tf(freq=1), with freq of:
          1 = termFreq=1
        1 = idf(docFreq=1, maxDocs=4)
        1 = fieldNorm(doc=0)
}

and I want to filter the matching results by their query weight as a MAX match and not a sum of match.

What i need to do is, from each document, I want to take the highest number given in every clause. (in this example i would like to take 0.75 as the matching score not 1.25). Is this possible, or even right, to do so?

What I have done so far is to create a Similarity in order to change the way the score is calculated, but I am still missing the part of getting the MAX instead of SUM as a result.

I am using Lucene .Net version 4.8(beta).

Thank you in advance!

Answer 1

No need to modify the Similarity to do that. Instead of a boolean query, use a DisjunctionMaxQuery .

Answer 2

Thanks for the solution but I still have a problem. When I try to do that I get the same results as before. My code is the following (I used examples from here ) :

BooleanQuery finalQuery = new BooleanQuery();
DisjunctionMaxQuery q1 = new DisjunctionMaxQuery(0.01f);
Query query = new FuzzyQuery(new Term("Caption", "roma"));
q1.Add(query);
finalQuery.Add(q1, Occur.MUST);

The results I get are (the same as in the question) :

{1.25 = (MATCH) sum of:

  0.5 = (MATCH) weight(Caption:vrom^0.5 in 0) [MySimilarity], result of:
    0.5 = score(doc=0,freq=1 = termFreq=1
), product of:
      0.5 = queryWeight, product of:
        0.5 = boost
        1 = idf(docFreq=1, maxDocs=4)
        1 = queryNorm
      1 = fieldWeight in 0, product of:
        1 = tf(freq=1), with freq of:
          1 = termFreq=1
        1 = idf(docFreq=1, maxDocs=4)
        1 = fieldNorm(doc=0)

  0.75 = (MATCH) weight(Caption:vroma^0.75 in 0) [MySimilarity], result of:
    0.75 = score(doc=0,freq=1 = termFreq=1
), product of:
      0.75 = queryWeight, product of:
        0.75 = boost
        1 = idf(docFreq=1, maxDocs=4)
        1 = queryNorm
      1 = fieldWeight in 0, product of:
        1 = tf(freq=1), with freq of:
          1 = termFreq=1
        1 = idf(docFreq=1, maxDocs=4)
        1 = fieldNorm(doc=0)
}

I tried it and without modify the Similarity but I had the same. The results in this case are :

{0.505973 = (MATCH) sum of:

  0.2023892 = (MATCH) weight(Caption:vrom^0.5 in 0) [DefaultSimilarity], result of:
    0.2023892 = score(doc=0,freq=1 = termFreq=1
), product of:
      0.3187582 = queryWeight, product of:
        0.5 = boost
        1.693147 = idf(docFreq=1, maxDocs=4)
        0.3765274 = queryNorm
      0.6349302 = fieldWeight in 0, product of:
        1 = tf(freq=1), with freq of:
          1 = termFreq=1
        1.693147 = idf(docFreq=1, maxDocs=4)
        0.375 = fieldNorm(doc=0)

  0.3035838 = (MATCH) weight(Caption:vroma^0.75 in 0) [DefaultSimilarity], result of:
    0.3035838 = score(doc=0,freq=1 = termFreq=1
), product of:
      0.4781373 = queryWeight, product of:
        0.75 = boost
        1.693147 = idf(docFreq=1, maxDocs=4)
        0.3765274 = queryNorm
      0.6349302 = fieldWeight in 0, product of:
        1 = tf(freq=1), with freq of:
          1 = termFreq=1
        1.693147 = idf(docFreq=1, maxDocs=4)
        0.375 = fieldNorm(doc=0)
}