alessandrobenedetti commented on a change in pull request #1571:
URL: https://github.com/apache/lucene-solr/pull/1571#discussion_r518736297
##########
File path:
solr/contrib/ltr/src/java/org/apache/solr/ltr/search/LTRQParserPlugin.java
##########
@@ -146,93 +149,114 @@ public LTRQParser(String qstr, SolrParams localParams,
SolrParams params,
@Override
public Query parse() throws SyntaxError {
// ReRanking Model
- final String modelName = localParams.get(LTRQParserPlugin.MODEL);
- if ((modelName == null) || modelName.isEmpty()) {
+ final String[] modelNames =
localParams.getParams(LTRQParserPlugin.MODEL);
+ if ((modelNames == null) || modelNames.length==0 ||
modelNames[0].isEmpty()) {
throw new SolrException(SolrException.ErrorCode.BAD_REQUEST,
"Must provide model in the request");
}
-
- final LTRScoringModel ltrScoringModel = mr.getModel(modelName);
- if (ltrScoringModel == null) {
- throw new SolrException(SolrException.ErrorCode.BAD_REQUEST,
- "cannot find " + LTRQParserPlugin.MODEL + " " + modelName);
- }
-
- final String modelFeatureStoreName =
ltrScoringModel.getFeatureStoreName();
- final boolean extractFeatures =
SolrQueryRequestContextUtils.isExtractingFeatures(req);
- final String fvStoreName =
SolrQueryRequestContextUtils.getFvStoreName(req);
- // Check if features are requested and if the model feature store and
feature-transform feature store are the same
- final boolean featuresRequestedFromSameStore =
(modelFeatureStoreName.equals(fvStoreName) || fvStoreName == null) ?
extractFeatures:false;
- if (threadManager != null) {
-
threadManager.setExecutor(req.getCore().getCoreContainer().getUpdateShardHandler().getUpdateExecutor());
- }
- final LTRScoringQuery scoringQuery = new LTRScoringQuery(ltrScoringModel,
- extractEFIParams(localParams),
- featuresRequestedFromSameStore, threadManager);
-
- // Enable the feature vector caching if we are extracting features, and
the features
- // we requested are the same ones we are reranking with
- if (featuresRequestedFromSameStore) {
- scoringQuery.setFeatureLogger(
SolrQueryRequestContextUtils.getFeatureLogger(req) );
+
+ LTRScoringQuery[] rerankingQueries = new
LTRScoringQuery[modelNames.length];
+ for (int i = 0; i < modelNames.length; i++) {
+ final LTRScoringQuery rerankingQuery;
+ if (!ORIGINAL_RANKING.equals(modelNames[i])) {
+ final LTRScoringModel ltrScoringModel = mr.getModel(modelNames[i]);
+ if (ltrScoringModel == null) {
+ throw new SolrException(SolrException.ErrorCode.BAD_REQUEST,
+ "cannot find " + LTRQParserPlugin.MODEL + " " + modelNames[i]);
+ }
+ final String modelFeatureStoreName =
ltrScoringModel.getFeatureStoreName();
+ final boolean extractFeatures =
SolrQueryRequestContextUtils.isExtractingFeatures(req);
+ final String fvStoreName =
SolrQueryRequestContextUtils.getFvStoreName(req); // Check if features
are requested and if the model feature store and feature-transform feature
store are the same
+ final boolean featuresRequestedFromSameStore =
(modelFeatureStoreName.equals(fvStoreName) || fvStoreName == null) ?
extractFeatures : false;
+ if (threadManager != null) {
+
threadManager.setExecutor(req.getCore().getCoreContainer().getUpdateShardHandler().getUpdateExecutor());
+ }
+ rerankingQuery = new LTRScoringQuery(ltrScoringModel,
+ extractEFIParams(localParams),
+ featuresRequestedFromSameStore, threadManager);
+
+ // Enable the feature vector caching if we are extracting features,
and the features
+ // we requested are the same ones we are reranking with
+ if (featuresRequestedFromSameStore) {
+ rerankingQuery.setFeatureLogger(
SolrQueryRequestContextUtils.getFeatureLogger(req) );
+ }
+ }else{
+ rerankingQuery = new LTRScoringQuery(null);
+ }
+
+ // External features
+ rerankingQuery.setRequest(req);
+ rerankingQueries[i] = rerankingQuery;
}
- SolrQueryRequestContextUtils.setScoringQuery(req, scoringQuery);
+ SolrQueryRequestContextUtils.setScoringQuery(req, rerankingQueries);
int reRankDocs = localParams.getInt(RERANK_DOCS, DEFAULT_RERANK_DOCS);
if (reRankDocs <= 0) {
throw new SolrException(SolrException.ErrorCode.BAD_REQUEST,
- "Must rerank at least 1 document");
+ "Must rerank at least 1 document");
+ }
+ if (rerankingQueries.length == 1) {
+ return new LTRQuery(rerankingQueries[0], reRankDocs);
+ } else {
+ return new LTRQuery(rerankingQueries, reRankDocs);
}
-
- // External features
- scoringQuery.setRequest(req);
-
- return new LTRQuery(scoringQuery, reRankDocs);
}
}
+
+ public static boolean isOriginalRanking(LTRScoringQuery rerankingQuery){
+ return rerankingQuery.getScoringModel() == null;
+ }
/**
* A learning to rank Query, will incapsulate a learning to rank model, and
delegate to it the rescoring
* of the documents.
**/
public class LTRQuery extends AbstractReRankQuery {
- private final LTRScoringQuery scoringQuery;
+ private final LTRScoringQuery[] rerankingQueries;
- public LTRQuery(LTRScoringQuery scoringQuery, int reRankDocs) {
- super(defaultQuery, reRankDocs, new LTRRescorer(scoringQuery));
- this.scoringQuery = scoringQuery;
+ public LTRQuery(LTRScoringQuery[] rerankingQueries, int rerankDocs) {
+ super(defaultQuery, rerankDocs, new
LTRInterleavingRescorer(rerankingQueries));
+ this.rerankingQueries = rerankingQueries;
+ }
+
+ public LTRQuery(LTRScoringQuery rerankingQuery, int rerankDocs) {
+ super(defaultQuery, rerankDocs, new LTRRescorer(rerankingQuery));
+ this.rerankingQueries = new LTRScoringQuery[]{rerankingQuery};
Review comment:
So I have done that in my latest commit, I brought back the original
implementation for the transformers, it may be less readable but are we
reducing the number of contains call?
Given that we support just interleaving for 2 ranked list, if a document is
not assigned to modelB is by default assigned to modelA and no need of checking
the getPickedInterleavingDocIds?
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