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‎x-pack/plugin/rank-rrf/src/internalClusterTest/java/org/elasticsearch/xpack/rank/linear/LinearRetrieverIT.java‎

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@@ -47,13 +47,11 @@
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import java.util.Collection;
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import java.util.List;
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import java.util.concurrent.atomic.AtomicInteger;
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import java.util.stream.Collectors;
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import static org.elasticsearch.cluster.metadata.IndexMetadata.SETTING_NUMBER_OF_SHARDS;
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import static org.elasticsearch.test.hamcrest.ElasticsearchAssertions.assertResponse;
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import static org.hamcrest.CoreMatchers.is;
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import static org.hamcrest.Matchers.closeTo;
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import static org.hamcrest.Matchers.containsInAnyOrder;
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import static org.hamcrest.Matchers.containsString;
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import static org.hamcrest.Matchers.equalTo;
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import static org.hamcrest.Matchers.instanceOf;
@@ -858,120 +856,120 @@ public void testLinearRetrieverWithMinScoreValidation() {
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}
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// public void testLinearRetrieverWithMinScoreScenarios() {
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// final int rankWindowSize = 10;
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// // Setup test data
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// indexDoc(INDEX, "doc_1", TEXT_FIELD, "term1", "views.last30d", 10, "views.all", 100);
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// indexDoc(INDEX, "doc_2", TEXT_FIELD, "term1 term2", "views.last30d", 20, "views.all", 200);
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// indexDoc(INDEX, "doc_3", TEXT_FIELD, "term1 term2 term3", "views.last30d", 30, "views.all", 300);
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// indexDoc(INDEX, "doc_4", TEXT_FIELD, "term4", "views.last30d", 40, "views.all", 400);
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// refresh(INDEX);
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// // Create retrievers with different scoring
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// StandardRetrieverBuilder retrieverA = new StandardRetrieverBuilder(QueryBuilders.termQuery(TEXT_FIELD, "term1").boost(10.0f));
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// StandardRetrieverBuilder retrieverB = new StandardRetrieverBuilder(QueryBuilders.termQuery(TEXT_FIELD, "term2").boost(1.0f));
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// float[] weights = new float[] { 1.0f, 1.0f };
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// ScoreNormalizer[] identityNormalizers = LinearRetrieverBuilder.getDefaultNormalizers(2);
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// // Scenario 1: No min_score - all docs returned
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// LinearRetrieverBuilder builderNoMinScore = new LinearRetrieverBuilder(
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// List.of(
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// new CompoundRetrieverBuilder.RetrieverSource(retrieverA, null),
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// new CompoundRetrieverBuilder.RetrieverSource(retrieverB, null)
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// ),
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// rankWindowSize,
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// weights,
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// identityNormalizers
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// );
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// SearchSourceBuilder sourceNoMinScore = new SearchSourceBuilder().retriever(builderNoMinScore).size(rankWindowSize);
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// ElasticsearchAssertions.assertResponse(client().prepareSearch(INDEX).setSource(sourceNoMinScore), resp -> {
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// assertThat(resp.getHits().getTotalHits().value(), equalTo(3L)); // doc_1, doc_2, doc_3 match
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// assertThat(resp.getHits().getHits()[0].getId(), equalTo("doc_3")); // term1(10) + term2(1) = 11
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// assertThat(resp.getHits().getHits()[1].getId(), equalTo("doc_2")); // term1(10) + term2(1) = 11
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// assertThat(resp.getHits().getHits()[2].getId(), equalTo("doc_1")); // term1(10) = 10
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// });
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// // Scenario 2: minScore = 0.0f - all matching docs returned (inclusive)
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// LinearRetrieverBuilder builderZeroMinScore = new LinearRetrieverBuilder(
899-
// List.of(
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// new CompoundRetrieverBuilder.RetrieverSource(retrieverA, null),
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// new CompoundRetrieverBuilder.RetrieverSource(retrieverB, null)
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// ),
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// rankWindowSize,
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// weights,
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// identityNormalizers
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// ).minScore(0.0f);
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// SearchSourceBuilder sourceZeroMinScore = new SearchSourceBuilder().retriever(builderZeroMinScore).size(rankWindowSize);
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// ElasticsearchAssertions.assertResponse(
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// client().prepareSearch(INDEX).setSource(sourceZeroMinScore),
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// resp -> assertThat(resp.getHits().getTotalHits().value(), equalTo(3L))
913-
// );
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// // Scenario 3: Basic filtering - minScore = 10.5f
916-
// LinearRetrieverBuilder builderFilterBasic = new LinearRetrieverBuilder(
917-
// List.of(
918-
// new CompoundRetrieverBuilder.RetrieverSource(retrieverA, null),
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// new CompoundRetrieverBuilder.RetrieverSource(retrieverB, null)
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// ),
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// rankWindowSize,
922-
// weights,
923-
// identityNormalizers
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// ).minScore(10.5f);
925-
926-
// SearchSourceBuilder sourceFilterBasic = new SearchSourceBuilder().retriever(builderFilterBasic).size(rankWindowSize);
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928-
// ElasticsearchAssertions.assertResponse(client().prepareSearch(INDEX).setSource(sourceFilterBasic), resp -> {
929-
// assertThat(resp.getHits().getTotalHits().value(), equalTo(2L)); // doc_2 and doc_3 have score 11.0
930-
// List<String> ids = Arrays.stream(resp.getHits().getHits()).map(h -> h.getId()).collect(Collectors.toList());
931-
// assertThat(ids, containsInAnyOrder("doc_2", "doc_3"));
932-
// });
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// // Scenario 4: Filter all documents - minScore = 20.0f
935-
// LinearRetrieverBuilder builderFilterAll = new LinearRetrieverBuilder(
936-
// List.of(
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// new CompoundRetrieverBuilder.RetrieverSource(retrieverA, null),
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// new CompoundRetrieverBuilder.RetrieverSource(retrieverB, null)
939-
// ),
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// rankWindowSize,
941-
// weights,
942-
// identityNormalizers
943-
// ).minScore(20.0f);
944-
945-
// SearchSourceBuilder sourceFilterAll = new SearchSourceBuilder().retriever(builderFilterAll).size(rankWindowSize);
946-
947-
// ElasticsearchAssertions.assertResponse(
948-
// client().prepareSearch(INDEX).setSource(sourceFilterAll),
949-
// resp -> assertThat(resp.getHits().getTotalHits().value(), equalTo(0L))
950-
// );
951-
952-
// // Scenario 5: Test with MinMax normalization
953-
// StandardRetrieverBuilder retrieverC = new StandardRetrieverBuilder(QueryBuilders.termQuery(TEXT_FIELD, "term1").boost(4.0f));
954-
// StandardRetrieverBuilder retrieverD = new StandardRetrieverBuilder(QueryBuilders.termQuery(TEXT_FIELD, "term2").boost(1.0f));
955-
956-
// ScoreNormalizer[] minMaxNormalizers = new ScoreNormalizer[] { MinMaxScoreNormalizer.INSTANCE, MinMaxScoreNormalizer.INSTANCE };
957-
958-
// LinearRetrieverBuilder builderWithNorm = new LinearRetrieverBuilder(
959-
// List.of(
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// new CompoundRetrieverBuilder.RetrieverSource(retrieverC, null),
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// new CompoundRetrieverBuilder.RetrieverSource(retrieverD, null)
962-
// ),
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// rankWindowSize,
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// weights,
965-
// minMaxNormalizers
966-
// ).minScore(1.1f);
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968-
// SearchSourceBuilder sourceWithNorm = new SearchSourceBuilder().retriever(builderWithNorm).size(rankWindowSize);
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970-
// ElasticsearchAssertions.assertResponse(client().prepareSearch(INDEX).setSource(sourceWithNorm), resp -> {
971-
// // With MinMax normalization, we expect doc_2 and doc_3 to have scores > 1.1
972-
// assertThat(resp.getHits().getTotalHits().value(), equalTo(2L));
973-
// List<String> ids = Arrays.stream(resp.getHits().getHits()).map(h -> h.getId()).collect(Collectors.toList());
974-
// assertThat(ids, containsInAnyOrder("doc_2", "doc_3"));
975-
// });
859+
// final int rankWindowSize = 10;
860+
861+
// // Setup test data
862+
// indexDoc(INDEX, "doc_1", TEXT_FIELD, "term1", "views.last30d", 10, "views.all", 100);
863+
// indexDoc(INDEX, "doc_2", TEXT_FIELD, "term1 term2", "views.last30d", 20, "views.all", 200);
864+
// indexDoc(INDEX, "doc_3", TEXT_FIELD, "term1 term2 term3", "views.last30d", 30, "views.all", 300);
865+
// indexDoc(INDEX, "doc_4", TEXT_FIELD, "term4", "views.last30d", 40, "views.all", 400);
866+
// refresh(INDEX);
867+
868+
// // Create retrievers with different scoring
869+
// StandardRetrieverBuilder retrieverA = new StandardRetrieverBuilder(QueryBuilders.termQuery(TEXT_FIELD, "term1").boost(10.0f));
870+
// StandardRetrieverBuilder retrieverB = new StandardRetrieverBuilder(QueryBuilders.termQuery(TEXT_FIELD, "term2").boost(1.0f));
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872+
// float[] weights = new float[] { 1.0f, 1.0f };
873+
// ScoreNormalizer[] identityNormalizers = LinearRetrieverBuilder.getDefaultNormalizers(2);
874+
875+
// // Scenario 1: No min_score - all docs returned
876+
// LinearRetrieverBuilder builderNoMinScore = new LinearRetrieverBuilder(
877+
// List.of(
878+
// new CompoundRetrieverBuilder.RetrieverSource(retrieverA, null),
879+
// new CompoundRetrieverBuilder.RetrieverSource(retrieverB, null)
880+
// ),
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// rankWindowSize,
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// weights,
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// identityNormalizers
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// );
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// SearchSourceBuilder sourceNoMinScore = new SearchSourceBuilder().retriever(builderNoMinScore).size(rankWindowSize);
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888+
// ElasticsearchAssertions.assertResponse(client().prepareSearch(INDEX).setSource(sourceNoMinScore), resp -> {
889+
// assertThat(resp.getHits().getTotalHits().value(), equalTo(3L)); // doc_1, doc_2, doc_3 match
890+
// assertThat(resp.getHits().getHits()[0].getId(), equalTo("doc_3")); // term1(10) + term2(1) = 11
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// assertThat(resp.getHits().getHits()[1].getId(), equalTo("doc_2")); // term1(10) + term2(1) = 11
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// assertThat(resp.getHits().getHits()[2].getId(), equalTo("doc_1")); // term1(10) = 10
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// });
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// // Scenario 2: minScore = 0.0f - all matching docs returned (inclusive)
896+
// LinearRetrieverBuilder builderZeroMinScore = new LinearRetrieverBuilder(
897+
// List.of(
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// new CompoundRetrieverBuilder.RetrieverSource(retrieverA, null),
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// new CompoundRetrieverBuilder.RetrieverSource(retrieverB, null)
900+
// ),
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// rankWindowSize,
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// weights,
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// identityNormalizers
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// ).minScore(0.0f);
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// SearchSourceBuilder sourceZeroMinScore = new SearchSourceBuilder().retriever(builderZeroMinScore).size(rankWindowSize);
907+
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// ElasticsearchAssertions.assertResponse(
909+
// client().prepareSearch(INDEX).setSource(sourceZeroMinScore),
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// resp -> assertThat(resp.getHits().getTotalHits().value(), equalTo(3L))
911+
// );
912+
913+
// // Scenario 3: Basic filtering - minScore = 10.5f
914+
// LinearRetrieverBuilder builderFilterBasic = new LinearRetrieverBuilder(
915+
// List.of(
916+
// new CompoundRetrieverBuilder.RetrieverSource(retrieverA, null),
917+
// new CompoundRetrieverBuilder.RetrieverSource(retrieverB, null)
918+
// ),
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// rankWindowSize,
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// weights,
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// identityNormalizers
922+
// ).minScore(10.5f);
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// SearchSourceBuilder sourceFilterBasic = new SearchSourceBuilder().retriever(builderFilterBasic).size(rankWindowSize);
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926+
// ElasticsearchAssertions.assertResponse(client().prepareSearch(INDEX).setSource(sourceFilterBasic), resp -> {
927+
// assertThat(resp.getHits().getTotalHits().value(), equalTo(2L)); // doc_2 and doc_3 have score 11.0
928+
// List<String> ids = Arrays.stream(resp.getHits().getHits()).map(h -> h.getId()).collect(Collectors.toList());
929+
// assertThat(ids, containsInAnyOrder("doc_2", "doc_3"));
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// });
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932+
// // Scenario 4: Filter all documents - minScore = 20.0f
933+
// LinearRetrieverBuilder builderFilterAll = new LinearRetrieverBuilder(
934+
// List.of(
935+
// new CompoundRetrieverBuilder.RetrieverSource(retrieverA, null),
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// new CompoundRetrieverBuilder.RetrieverSource(retrieverB, null)
937+
// ),
938+
// rankWindowSize,
939+
// weights,
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// identityNormalizers
941+
// ).minScore(20.0f);
942+
943+
// SearchSourceBuilder sourceFilterAll = new SearchSourceBuilder().retriever(builderFilterAll).size(rankWindowSize);
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945+
// ElasticsearchAssertions.assertResponse(
946+
// client().prepareSearch(INDEX).setSource(sourceFilterAll),
947+
// resp -> assertThat(resp.getHits().getTotalHits().value(), equalTo(0L))
948+
// );
949+
950+
// // Scenario 5: Test with MinMax normalization
951+
// StandardRetrieverBuilder retrieverC = new StandardRetrieverBuilder(QueryBuilders.termQuery(TEXT_FIELD, "term1").boost(4.0f));
952+
// StandardRetrieverBuilder retrieverD = new StandardRetrieverBuilder(QueryBuilders.termQuery(TEXT_FIELD, "term2").boost(1.0f));
953+
954+
// ScoreNormalizer[] minMaxNormalizers = new ScoreNormalizer[] { MinMaxScoreNormalizer.INSTANCE, MinMaxScoreNormalizer.INSTANCE };
955+
956+
// LinearRetrieverBuilder builderWithNorm = new LinearRetrieverBuilder(
957+
// List.of(
958+
// new CompoundRetrieverBuilder.RetrieverSource(retrieverC, null),
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// new CompoundRetrieverBuilder.RetrieverSource(retrieverD, null)
960+
// ),
961+
// rankWindowSize,
962+
// weights,
963+
// minMaxNormalizers
964+
// ).minScore(1.1f);
965+
966+
// SearchSourceBuilder sourceWithNorm = new SearchSourceBuilder().retriever(builderWithNorm).size(rankWindowSize);
967+
968+
// ElasticsearchAssertions.assertResponse(client().prepareSearch(INDEX).setSource(sourceWithNorm), resp -> {
969+
// // With MinMax normalization, we expect doc_2 and doc_3 to have scores > 1.1
970+
// assertThat(resp.getHits().getTotalHits().value(), equalTo(2L));
971+
// List<String> ids = Arrays.stream(resp.getHits().getHits()).map(h -> h.getId()).collect(Collectors.toList());
972+
// assertThat(ids, containsInAnyOrder("doc_2", "doc_3"));
973+
// });
976974
// }
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}

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