Fortunately, this situation is wildly improved in trunk (to be 4.0), where we have a selection of modern scoring models, including Okapi BM25, Language models, Divergence from Randomness models and Information-based models. To support these, we now save a number of commonly used index statistics per index segment, and make them available at search time.
To understand the new statistics, let's pretend we've indexed the following two example documents, each with only one field "title":
- document 1: The Lion, the Witch, and the Wardrobe
- document 2: The Da Vinci Code
- How many documents contain at least one
occurrence of the term in the field; 3.x
indices also save this (
TermEnum.docFreq()). For term "lion" docFreq is 1, and for term "the" it's 2.
- Number of postings, i.e. sum
TermsEnum.docFreq()across all terms in the field. For our example documents this is 9.
- Number of occurrences of this term in the field, across all documents. For term "the" it's 4, for term "vinci" it's 1.
- Number of term occurrences
in the field, across all documents; this is the sum
TermsEnum.totalTermFreq()across all unique terms in the field. For our example documents this is 11.
- How many documents have at least one term for this field. In our example documents, this is 2, but if for example one of the documents was missing the title field, it would be 1.
- How many unique terms were seen in this field. For our example documents this is 8. Note that this statistic is of limited utility for scoring, because it's only available per-segment and you cannot (efficiently!) compute this across all segments in the index (unless there is only one segment).
- Number of unique terms
across all fields; this is the sum
Terms.getUniqueTermCount()across all fields. In our example documents this is 8. Note that this is also only available per-segment.
- Number of unique fields. For our example documents this is 1; if we also had a body field and an abstract field, it would be 3. Note that this is also only available per-segment.
TermsEnum.docFreq(), so if you want to experiment with the new scoring models in Lucene 4.0, you should either re-index or upgrade your index using
IndexUpgrader. Note that the new scoring models all use the same single-byte norms format, so you can freely switch between them without re-indexing.
In addition to what's stored in the index, there are also these statistics available per-field, per-document while indexing, in the
Similarity.computeNormmethod for both 3.x and 4.0:
- How many tokens in the document. For document 1 it's 7; for document 2 it's 4.
- For this field in this document, how many unique terms are there? For document 1, it's 5; for document 2 it's 4.
- What was the count for the most frequent term in this document. For document 1 it's 3 ("the" occurs 3 times); for document 2 it's 1.
From these available statistics you're now free to derive other commonly used statistics:
- Average field length across all documents is
Terms.getDocCount(), if not all documents have the field).
- Average within-document field term frequency is
- Average number of unique terms per field across all documents is
Terms.getDocCount(field), if not all documents have the field).
This is super-awesome! Thanks Mike!ReplyDelete
How can I get the count of a term for a given document and field?ReplyDelete
You should pull a DocsEnum for that field + term, advance to the docID and call .freq() to get that count. This is also available in term vectors (if you want to get the count for all terms within a single doc).
I'm trying to figure out in Lucene 4 the document length (total number of keywords it contains) from the norm vector at query run-time. For some (unclear) reason the encode method of the norm values is not public.
Any way of doing it?
Which similarity are you using? Just the default (DefaultSimilarity)? If so, then you can get the docLength from the norm by calling DefaultSimilarity.decodeNormValue. Note that if you boosted the field during indexing it will change this document length (higher boost makes doc length smaller). Also note that this is heavily quantized: we use only a single byte to encode the length by default.
If the quantization or mixin of boost is a problem ou can also make your own Similarity impl and store docLen yourself (eg as an int).
Thanks for the quick response.
I'm experimenting with BM25 and the new Language models based similarities in Lucene4. It turns out that the method decodeNormValue() is public for TFIDFSimilarity while protected for the rest. Of course I can expand these similarities classes to publicize this method, so my question is whether there is a special reason why it should not be publicized by default.
In general, I really like the new flexibility with the similarity measures provided by Lucene 4. I would however wish to have a more simple and intuitive API to extract (exact) doc-length from the index. I think these values should provided by the API and should not be handled by the application (just a wishful thinking)
thanks, and all the best
I think it's actually an artifact from back when TFIDFSim was the only
Similarity in Lucene, that its decodeNormValue is still
public... really how the sim stores its stats (and what stats it
stores) are private implementation details to it.
If you really want the int per field X doc ... you could clone
BM25Sim, but then instead of norm.setByte(...) in computeNorm, use
norm.setInt (and then remove all the encode/decodeNormValue stuff).
Then in the Exact/Sloppy scorers, pull the int docLengths instead of
byte norms. The downside of course is 4X the RAM per field X doc.
How can I get the freq of a term in a document?
Thanks in advance.
You need to first get a TermsEnum (Terms.iterator(null)), seek to that term (termsEnum.seekExact: verify you got back "true" indicating that the term exists), then use its .advance method to skip to that docID (verify the returned docID is the one you advanced to), then call freq().
How could I get the number of the unique terms in a document in my Similarity impl?
Thanks in advance.
That stat is available at indexing time, passed to your Similarity.computeNorm in the FieldInvertState argument, member uniqueTermCount.
If you need this at search time you'll have to save it away somewhere ...
I'm trying to understand what you explained but I'm pretty confused. I try to create the TermsEnum by the IndexReader but it always giving me null exception.
Can you send questions to the users's list (firstname.lastname@example.org)? Include the code fragment you're currently using and the exception of what went wrong ...
I'd like to know if there is any Analyzer that (at indexing time) doesn't tokenize on white spaces, but allows me to manage multi word phrases as single terms so that the API described in this post will keep woking as expected. In my use case I've a set of phrases that I need TermsEnum.docFreq(). Is something possible? In the case there isn't anything like that, would be trivial to implement a custom analyzer? Many Thanks
Can you ask this question on the Lucene user's list (email@example.com)?
Hello, I calculate the average term frequency for each document in Similarity.computeNorm and encode it into my norm. However, I need also the corresponding mean value over all documents.ReplyDelete
Does anybody have a suggestion how I could achieve this?
Can you load your norms at search time and sum them up and compute the mean? You'd just have to do it on each searcher init.Delete