alessandrobenedetti commented on a change in pull request #1571:
URL: https://github.com/apache/lucene-solr/pull/1571#discussion_r520038324
##########
File path:
solr/contrib/ltr/src/java/org/apache/solr/ltr/response/transform/LTRFeatureLoggerTransformerFactory.java
##########
@@ -210,50 +216,59 @@ public void setContext(ResultContext context) {
}
// Setup LTRScoringQuery
- scoringQuery = SolrQueryRequestContextUtils.getScoringQuery(req);
- docsWereNotReranked = (scoringQuery == null);
- String featureStoreName =
SolrQueryRequestContextUtils.getFvStoreName(req);
- if (docsWereNotReranked || (featureStoreName != null &&
(!featureStoreName.equals(scoringQuery.getScoringModel().getFeatureStoreName()))))
{
- // if store is set in the transformer we should overwrite the logger
-
- final ManagedFeatureStore fr =
ManagedFeatureStore.getManagedFeatureStore(req.getCore());
-
- final FeatureStore store = fr.getFeatureStore(featureStoreName);
- featureStoreName = store.getName(); // if featureStoreName was null
before this gets actual name
-
- try {
- final LoggingModel lm = new LoggingModel(loggingModelName,
- featureStoreName, store.getFeatures());
-
- scoringQuery = new LTRScoringQuery(lm,
- LTRQParserPlugin.extractEFIParams(localparams),
- true,
- threadManager); // request feature weights to be created for all
features
-
- }catch (final Exception e) {
- throw new SolrException(SolrException.ErrorCode.BAD_REQUEST,
- "retrieving the feature store "+featureStoreName, e);
- }
- }
+ rerankingQueries = SolrQueryRequestContextUtils.getScoringQueries(req);
- if (scoringQuery.getOriginalQuery() == null) {
- scoringQuery.setOriginalQuery(context.getQuery());
+ docsWereNotReranked = (rerankingQueries == null ||
rerankingQueries.length == 0);
+ if (docsWereNotReranked) {
+ rerankingQueries = new LTRScoringQuery[]{null};
}
- if (scoringQuery.getFeatureLogger() == null){
- scoringQuery.setFeatureLogger(
SolrQueryRequestContextUtils.getFeatureLogger(req) );
- }
- scoringQuery.setRequest(req);
-
- featureLogger = scoringQuery.getFeatureLogger();
+ modelWeights = new LTRScoringQuery.ModelWeight[rerankingQueries.length];
+ String featureStoreName =
SolrQueryRequestContextUtils.getFvStoreName(req);
+ for (int i = 0; i < rerankingQueries.length; i++) {
+ LTRScoringQuery scoringQuery = rerankingQueries[i];
+ if ((scoringQuery == null || !(scoringQuery instanceof
OriginalRankingLTRScoringQuery)) && (docsWereNotReranked || (featureStoreName
!= null &&
!featureStoreName.equals(scoringQuery.getScoringModel().getFeatureStoreName()))))
{
Review comment:
So I just committed my changes on this, I spent quite a while thinking
about the various scenarios, adjusting the code and adding the related tests.
From your observations:
- if both models are for the requested feature store then that's great and
each document would have been picked by one of the models and so we use the
feature vector already previously calculated by whatever model had picked the
document. [OK]
- if neither model is for the requested feature store then we need to create
a logging model, is one logging model sufficient or do we need two? intuitively
to me one would seem to be sufficient but that's based on partial analysis only
so far.
[One is sufficient, and my latest changes do that, anyway I just realized
that the loggingModel is not heavy to create and when getting the
featureVector, the cache is accessed, the key for that cache doesn't look to
the instance but to the content of classes, so two identical logging models
would have matched in the feature vector cache, the change was probably not
vital, but not harmful either]
- in the third scenario we still need the logging model, because when
specifying a featureStore in the featureVector transformer we aim to extract
all the features of that store, from efi passed to the transformer, so when
explicitly mentioned, we need a logger for both the model again (also for the
one that aligns)
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