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We ll look first at the Scorer class and its responsibilities in the scoring calculation.
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12.3 Scoring things my way
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The Scorer class is the focal point of Lucene scoring. If it helps, you can think of this class during the scoring process as functioning similarly to a JDBC RowSet having a forward-only cursor with the RowSet data as the group of documents that matched a query. Scorer is an abstract class that defines the following abstract methods:
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public abstract boolean next(); This method advances to the next matching document if one exists. public abstract int doc(); This method returns the current document id value. Just as with a JDBC RowSet, this is not a valid call until the next()method has been called one or more times. public abstract boolean skipTo(int target); Overriding this method could be as simple as using a basic loop, as shown here, which is fine for our purposes:
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boolean skipTo(int target) { do { if (!next()) return false; } while (target > doc()); return true; }
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public abstract Explanation explain(int doc); This is the method that
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lays out the scoring explanation so well. It can become quite involved, so we recommend that you examine the explain method of the TermScorer class and use that as a guide in writing your own explain method. public abstract float score(); This is the method we ve been looking for. Here is where the actual scoring procedure is defined, and you re free to change document scoring by overriding this method. For our purposes we ll examine the TermScorer s score method. This method in conjunction with the TermScorer s constructor is a good basic example for us to work with. The constructor and score()method are given in listing 12.12.
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Scoring things my way
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Listing 12.12 The score() method of the MyTermScorer class
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MyTermScorer(Weight weight, TermDocs td, The TermScorer Similarity similarity, constructor byte[] norms) { super(similarity); this.weight = weight; this.termDocs = td; this.norms = norms; this.weightValue = weight.getValue();
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for (int i = 0; i < SCORE_CACHE_SIZE; i++) scoreCache[i] = getSimilarity() .tf(i) * weightValue; } public float score() { int f = freqs[pointer];
C Calculate and cache score values
The score method
float raw = f < SCORE_CACHE_SIZE scoreCache[f] : getSimilarity().tf(f)*weightValue;
D frequency (tf) counts
Get cached value or
Cached term
E calculate a value F Return the raw score return raw * Similarity.decodeNorm(norms[doc]);
The constructor B caches a number of score values for fast access at C. These scores are calculated by retrieving the Similarity.tf() calculation over the range of cache index values multiplied by the weight.getValue()quantity of the query. Since the default implementation of the tf() method is the square root of the passed-in term, the values inserted into the cache are 0.0, 1.0, 1.414, 1.732, and so on, each multiplied by the weight value. The term frequency D is utilized E to look up the score in the cache or calculate the score if the number of terms is greater than the cache size. This calculation is the same as the constructor s calculation C, as you would expect. Finally, the raw score F is determined by normalizing the cache score against the decoded normalization value of this document and returned.
NOTE
The TermScorer is one of the easiest score implementations to understand. If you followed this even at just a cursory level, you have to have noticed how prevalent the Similarity class is in this calculation. Because of this, the authors recommend that, before you try to implement your own scorer, you determine whether what you want to accomplish can be done through reimplementing the Similarity class. This may save you not only a lot of work but also possibly a lot of long and sleepless nights.
We re going to hold off on an example for a bit until we cover the last important class in the scoring process, Weight. Why we are holding off will become apparent when you see just how intertwined these classes are.
Document ranking
12.3.1 Modifying a query s Weight class
An instance of the Weight class is specific to a particular query. After all, it has two purposes. First and foremost is to normalize a query so that searching does not modify the query in some way. This allows the query to be reused. Second is to build a Scorer class. In other words, a query has an associated Weight instance that it utilizes before performing the search process. We can employ a custom Weight class in several ways. The abstract org.apache.lucene.search.Searcher class implements the Searchable interface, which defines three abstract methods that take a Weight instance as one of their parameters. These are:
void search(Weight weight, Filter filter, HitCollector results) TopDocs search(Weight weight, Filter filter, int n) TopFieldDocs search(Weight weight, Filter filter, int n, Sort sort)
These methods are very low-level API calls, and each returns matching documents differently, so we suggest you read the documentation to see the strengths and weaknesses of each in relation to what you re trying to do.
WARNING Lucene is a dynamic project and its code changes constantly with mod-
ifications and additions. Consequently, the code shown in the Weight class in this section and the Scorer class in section 12.3.2 examples probably have changed since this was written. The methods in these classes we are most concerned with likely have not changed but be sure to look at the latest source code. The last way to change the way weighting is done is to not only develop your own Weight class but also to develop your own Query class and have your Query employ your Weight. This isn t as bad as it sounds. Query classes can be extended, so a lot of the work in this respect is already done for you. The Weight class is different, though. If you ve downloaded the Lucene source code (if you have not, we recommend you do so now) and happened to have looked in the org.apache.lucene.search package, you ll notice that there are no Weight classes there. Many Weight class implementations are inner classes of the query they re related to. As mentioned previously, a Weight class is specific to a particular query. Making them inner classes assures that this is the case. If you look at the org.apache.lucene.search.spans package, you ll see that these queries all use the non-inner class SpanWeight. The manner of implementation is up to you. The sequence of operations for weighting is contained in the Query.weight( Searcher searcher) method, shown in listing 12.13.
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