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https://issues.apache.org/jira/browse/LUCENE-10559?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17531972#comment-17531972
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Julie Tibshirani commented on LUCENE-10559:
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Big +1 for this addition to KnnGraphTester. I modified KnnGraphTester on a
branch to incorporate a random filter when I ran the experiments here:
[https://github.com/apache/lucene/pull/656#issuecomment-1032109021]. It's
important for everyone to be able to reproduce those experiments, and it'd be
good to add kNN with filtering to luceneutil as well.
As an aside, I can imagine scenarios where filters are correlated or
anti-correlated with proximity to the query vector. For example, maybe you're
looking for a similar-looking product (vector proximity), but in a certain
price range (filter). Or you're looking for similar news headlines (vector
proximity), but within a certain time range (filter).
> Add preFilter/postFilter options to KnnGraphTester
> --------------------------------------------------
>
> Key: LUCENE-10559
> URL: https://issues.apache.org/jira/browse/LUCENE-10559
> Project: Lucene - Core
> Issue Type: Improvement
> Reporter: Michael Sokolov
> Priority: Major
>
> We want to be able to test the efficacy of pre-filtering in KnnVectorQuery:
> if you (say) want the top K nearest neighbors subject to a constraint Q, are
> you better off over-selecting (say 2K) top hits and *then* filtering
> (post-filtering), or incorporating the filtering into the query
> (pre-filtering). How does it depend on the selectivity of the filter?
> I think we can get a reasonable testbed by generating a uniform random filter
> with some selectivity (that is consistent and repeatable). Possibly we'd also
> want to try filters that are correlated with index order, but it seems they'd
> be unlikely to be correlated with vector values in a way that the graph
> structure would notice, so random is a pretty good starting point for this.
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