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1.0 = tf(termFreq(description:salesman)=1) 5.379941 = idf(docFreq=5) 0.15625 = fieldNorm(field=description, doc=108) score => 0.44194174 0.5944051 = (MATCH) fieldWeight(description:salesman in 471), product of: 1.4142125 = tf(termFreq(description:salesman)=2) 5.379941 = idf(docFreq=5) 0.078125 = fieldNorm(field=description, doc=471) score => 0.4375 0.58843106 = (MATCH) fieldWeight(description:salesman in 57), product of: 1.0 = tf(termFreq(description:salesman)=1) 5.379941 = idf(docFreq=5) 0.109375 = fieldNorm(field=description, doc=57)
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The scores dropped just as expected. This is not a very useful example. It was intended to demonstrate how to modify a weight calculation. The idf is different for every query, so a constant value would quickly become irrelevant. Maybe it should be changed to idf 1 or idf + 1.
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Did you notice the Similarity class making its way into the calculation again Our former recommendation still holds. Before writing a full implementation of a Weight class and either extending or writing a new Query class, examine Similarity and see if what you want to accomplish can be done solely with it.
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We said earlier that we d hold off implementing our own Scorer class until we explained some other things, namely the Weight class. As one last example before we move on to other subject matter, we re going to do just that, implement a Scorer.
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12.3.2 Revisiting the Scorer class
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The end is in sight. We have one final matter to take care of before we move on from scoring. Did you tie B in listing 12.14 to the Scorer class we wrote in listing 12.12 This is how you employ your own implementation of a Scorer class. All that is necessary is to put your modifications in the MyTermScorer class, as we do in listing 12.15. We ll now implement our Scorer class, but we won t change the score method; we ll change the constructor to fill the scoreCACHE by multiplying the weightValue by a constant 1.0, as follows in listing 12.15.
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Listing 12.15 Changing the scorer constructor to multiply by a constant tf
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MyTermScorer(Weight weight, TermDocs td, Similarity similarity, byte[] norms) { super(similarity); this.weight = weight; this.termDocs = td; this.norms = norms; this.weightValue = weight.getValue();
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Scoring things my way
for (int i = 0; i < SCORE_CACHE_SIZE; i++) scoreCache[i] = 1.0 * weightValue; }
385 Multiplying by a constant tf value
The MyTermQuery class s normalize method returned to using the idf value instead of the constant 6, so we ll see the effect of changing only the scorer constructor. The top four document explanations then become these:
score => 0.84061575 0.84061575 = (MATCH) fieldWeight(description:salesman in 108), product of: 1.0 = tf(termFreq(description:salesman)=1) 5.379941 = idf(docFreq=5) 0.15625 = fieldNorm(field=description, doc=108) score => 0.58843106 0.58843106 = (MATCH) fieldWeight(description:salesman in 57), product of: 1.0 = tf(termFreq(description:salesman)=1) 5.379941 = idf(docFreq=5) 0.109375 = fieldNorm(field=description, doc=57) score => 0.42030787 0.42030787 = (MATCH) fieldWeight(description:salesman in 217), product of: 1.0 = tf(termFreq(description:salesman)=1) 5.379941 = idf(docFreq=5) 0.078125 = fieldNorm(field=description, doc=217) score => 0.42030787 0.5944051 = (MATCH) fieldWeight(description:salesman in 471), product of: 1.4142125 = tf(termFreq(description:salesman)=2) 5.379941 = idf(docFreq=5) 0.078125 = fieldNorm(field=description, doc=471)
Now compare this with listing 12.5. They are identical! How did this happen By changing the constructor to multiply by a constant 1.0, we, in effect, said that we don t care about term frequency. If a document contains the term, report it. Is that not exactly what we did in listing 12.4 when we changed the DefaultSimilarity s term frequency Rather than do that, we extended the Query class, wrote our own Weight class, then wrote our own scorer. Hopefully this demonstrates that things can be made simpler. Don t do more work than you have to!
12.3.3 Is it worth it
At the beginning of this chapter we asked a series of questions about the scoring of documents and how we could possibly manipulate the scoring, if at all. The last question in that group was, Is it worth it to change this scoring mechanism To paraphrase one of the core Lucene developers, Lucene s scoring works well right out of the box. At the same time, Lucene is definitely not all things to all people. Some people are never satisfied. We believe the answer to this question is the same ubiquitous answer
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