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Signed-off-by: zhichao-aws <zhichaog@amazon.com>
opensearch-project · Nov 20, 2024 · 46b9d9a · 46b9d9a
1 parent 302f949
commit 46b9d9a
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Showing 12 changed files with 73 additions and 73 deletions.
diff --git a/src/main/java/org/opensearch/neuralsearch/processor/NeuralSparseTwoPhaseProcessor.java b/src/main/java/org/opensearch/neuralsearch/processor/NeuralSparseTwoPhaseProcessor.java
@@ -13,8 +13,8 @@
 import org.opensearch.index.query.QueryBuilder;
 import org.opensearch.ingest.ConfigurationUtils;
 import org.opensearch.neuralsearch.query.NeuralSparseQueryBuilder;
-import org.opensearch.neuralsearch.util.pruning.PruneType;
-import org.opensearch.neuralsearch.util.pruning.PruneUtils;
+import org.opensearch.neuralsearch.util.prune.PruneType;
+import org.opensearch.neuralsearch.util.prune.PruneUtils;
 import org.opensearch.search.builder.SearchSourceBuilder;
 import org.opensearch.search.pipeline.AbstractProcessor;
 import org.opensearch.search.pipeline.Processor;

diff --git a/src/main/java/org/opensearch/neuralsearch/processor/SparseEncodingProcessor.java b/src/main/java/org/opensearch/neuralsearch/processor/SparseEncodingProcessor.java
@@ -15,11 +15,11 @@
 import org.opensearch.env.Environment;
 import org.opensearch.ingest.IngestDocument;
 import org.opensearch.neuralsearch.ml.MLCommonsClientAccessor;
-import org.opensearch.neuralsearch.util.pruning.PruneType;
+import org.opensearch.neuralsearch.util.prune.PruneType;
 import org.opensearch.neuralsearch.util.TokenWeightUtil;
 
 import lombok.extern.log4j.Log4j2;
-import org.opensearch.neuralsearch.util.pruning.PruneUtils;
+import org.opensearch.neuralsearch.util.prune.PruneUtils;
 
 /**
  * This processor is used for user input data text sparse encoding processing, model_id can be used to indicate which model user use,
@@ -62,7 +62,7 @@ public void doExecute(
         mlCommonsClientAccessor.inferenceSentencesWithMapResult(this.modelId, inferenceList, ActionListener.wrap(resultMaps -> {
             List<Map<String, Float>> sparseVectors = TokenWeightUtil.fetchListOfTokenWeightMap(resultMaps);
             sparseVectors = sparseVectors.stream()
-                .map(vector -> PruneUtils.pruningSparseVector(pruneType, pruneRatio, vector, false).v1())
+                .map(vector -> PruneUtils.pruneSparseVector(pruneType, pruneRatio, vector, false).v1())
                 .toList();
             setVectorFieldsToDocument(ingestDocument, ProcessMap, sparseVectors);
             handler.accept(ingestDocument, null);
@@ -74,7 +74,7 @@ public void doBatchExecute(List<String> inferenceList, Consumer<List<?>> handler
         mlCommonsClientAccessor.inferenceSentencesWithMapResult(this.modelId, inferenceList, ActionListener.wrap(resultMaps -> {
             List<Map<String, Float>> sparseVectors = TokenWeightUtil.fetchListOfTokenWeightMap(resultMaps);
             sparseVectors = sparseVectors.stream()
-                .map(vector -> PruneUtils.pruningSparseVector(pruneType, pruneRatio, vector, false).v1())
+                .map(vector -> PruneUtils.pruneSparseVector(pruneType, pruneRatio, vector, false).v1())
                 .toList();
             handler.accept(sparseVectors);
         }, onException));

diff --git a/...in/java/org/opensearch/neuralsearch/processor/factory/SparseEncodingProcessorFactory.java b/...in/java/org/opensearch/neuralsearch/processor/factory/SparseEncodingProcessorFactory.java
@@ -21,8 +21,8 @@
 import org.opensearch.neuralsearch.processor.SparseEncodingProcessor;
 
 import lombok.extern.log4j.Log4j2;
-import org.opensearch.neuralsearch.util.pruning.PruneUtils;
-import org.opensearch.neuralsearch.util.pruning.PruneType;
+import org.opensearch.neuralsearch.util.prune.PruneUtils;
+import org.opensearch.neuralsearch.util.prune.PruneType;
 
 /**
  * Factory for sparse encoding ingest processor for ingestion pipeline. Instantiates processor based on user provided input.

diff --git a/src/main/java/org/opensearch/neuralsearch/query/NeuralSparseQueryBuilder.java b/src/main/java/org/opensearch/neuralsearch/query/NeuralSparseQueryBuilder.java
@@ -47,8 +47,8 @@
 import lombok.NoArgsConstructor;
 import lombok.Setter;
 import lombok.experimental.Accessors;
-import org.opensearch.neuralsearch.util.pruning.PruneType;
-import org.opensearch.neuralsearch.util.pruning.PruneUtils;
+import org.opensearch.neuralsearch.util.prune.PruneType;
+import org.opensearch.neuralsearch.util.prune.PruneUtils;
 
 /**
  * SparseEncodingQueryBuilder is responsible for handling "neural_sparse" query types. It uses an ML NEURAL_SPARSE model
@@ -146,7 +146,7 @@ public NeuralSparseQueryBuilder getCopyNeuralSparseQueryBuilderForTwoPhase(float
             Map<String, Float> tokens = queryTokensSupplier.get();
             // Splitting tokens based on a threshold value: tokens greater than the threshold are stored in v1,
             // while those less than or equal to the threshold are stored in v2.
-            Tuple<Map<String, Float>, Map<String, Float>> splitTokens = PruneUtils.pruningSparseVector(pruneType, pruneRatio, tokens, true);
+            Tuple<Map<String, Float>, Map<String, Float>> splitTokens = PruneUtils.pruneSparseVector(pruneType, pruneRatio, tokens, true);
             this.queryTokensSupplier(() -> splitTokens.v1());
             copy.queryTokensSupplier(() -> splitTokens.v2());
         } else {
@@ -348,7 +348,7 @@ private BiConsumer<Client, ActionListener<?>> getModelInferenceAsync(SetOnce<Map
             ActionListener.wrap(mapResultList -> {
                 Map<String, Float> queryTokens = TokenWeightUtil.fetchListOfTokenWeightMap(mapResultList).get(0);
                 if (Objects.nonNull(twoPhaseSharedQueryToken)) {
-                    Tuple<Map<String, Float>, Map<String, Float>> splitQueryTokens = PruneUtils.pruningSparseVector(
+                    Tuple<Map<String, Float>, Map<String, Float>> splitQueryTokens = PruneUtils.pruneSparseVector(
                         twoPhasePruneType,
                         twoPhasePruneRatio,
                         queryTokens,

diff --git a/.../neuralsearch/util/pruning/PruneType.java → ...ch/neuralsearch/util/prune/PruneType.java b/.../neuralsearch/util/pruning/PruneType.java → ...ch/neuralsearch/util/prune/PruneType.java
@@ -2,12 +2,12 @@
  * Copyright OpenSearch Contributors
  * SPDX-License-Identifier: Apache-2.0
  */
-package org.opensearch.neuralsearch.util.pruning;
+package org.opensearch.neuralsearch.util.prune;
 
 import org.apache.commons.lang.StringUtils;
 
 /**
- * Enum representing different types of pruning methods for sparse vectors
+ * Enum representing different types of prune methods for sparse vectors
  */
 public enum PruneType {
     NONE("none"),
@@ -29,9 +29,9 @@ public String getValue() {
     /**
      * Get PruneType from string value
      *
-     * @param value string representation of pruning type
+     * @param value string representation of prune type
      * @return corresponding PruneType enum
-     * @throws IllegalArgumentException if value doesn't match any pruning type
+     * @throws IllegalArgumentException if value doesn't match any prune type
      */
     public static PruneType fromString(String value) {
         if (StringUtils.isEmpty(value)) return NONE;
@@ -40,6 +40,6 @@ public static PruneType fromString(String value) {
                 return type;
             }
         }
-        throw new IllegalArgumentException("Unknown pruning type: " + value);
+        throw new IllegalArgumentException("Unknown prune type: " + value);
     }
 }
diff --git a/...neuralsearch/util/pruning/PruneUtils.java → ...h/neuralsearch/util/prune/PruneUtils.java b/...neuralsearch/util/pruning/PruneUtils.java → ...h/neuralsearch/util/prune/PruneUtils.java
@@ -2,7 +2,7 @@
  * Copyright OpenSearch Contributors
  * SPDX-License-Identifier: Apache-2.0
  */
-package org.opensearch.neuralsearch.util.pruning;
+package org.opensearch.neuralsearch.util.prune;
 
 import org.opensearch.common.collect.Tuple;
 
@@ -15,8 +15,8 @@
 import java.util.PriorityQueue;
 
 /**
- * Utility class providing methods for pruning sparse vectors using different strategies.
- * Pruning helps reduce the dimensionality of sparse vectors by removing less significant elements
+ * Utility class providing methods for prune sparse vectors using different strategies.
+ * Prune helps reduce the dimensionality of sparse vectors by removing less significant elements
  * based on various criteria.
  */
 public class PruneUtils {
@@ -31,7 +31,7 @@ public class PruneUtils {
      * @param requiresPrunedEntries Whether to return pruned entries
      * @return A tuple containing two maps: the first with top K elements, the second with remaining elements (or null)
      */
-    private static Tuple<Map<String, Float>, Map<String, Float>> pruningByTopK(
+    private static Tuple<Map<String, Float>, Map<String, Float>> pruneByTopK(
         Map<String, Float> sparseVector,
         int k,
         boolean requiresPrunedEntries
@@ -71,7 +71,7 @@ private static Tuple<Map<String, Float>, Map<String, Float>> pruningByTopK(
      * @return A tuple containing two maps: the first with elements meeting the ratio threshold,
      *         the second with elements below the threshold (or null)
      */
-    private static Tuple<Map<String, Float>, Map<String, Float>> pruningByMaxRatio(
+    private static Tuple<Map<String, Float>, Map<String, Float>> pruneByMaxRatio(
         Map<String, Float> sparseVector,
         float ratio,
         boolean requiresPrunedEntries
@@ -101,7 +101,7 @@ private static Tuple<Map<String, Float>, Map<String, Float>> pruningByMaxRatio(
      * @return A tuple containing two maps: the first with elements above the threshold,
      *         the second with elements below the threshold (or null)
      */
-    private static Tuple<Map<String, Float>, Map<String, Float>> pruningByValue(
+    private static Tuple<Map<String, Float>, Map<String, Float>> pruneByValue(
         Map<String, Float> sparseVector,
         float thresh,
         boolean requiresPrunedEntries
@@ -130,7 +130,7 @@ private static Tuple<Map<String, Float>, Map<String, Float>> pruningByValue(
      * @return A tuple containing two maps: the first with elements meeting the alpha mass threshold,
      *         the second with remaining elements (or null)
      */
-    private static Tuple<Map<String, Float>, Map<String, Float>> pruningByAlphaMass(
+    private static Tuple<Map<String, Float>, Map<String, Float>> pruneByAlphaMass(
         Map<String, Float> sparseVector,
         float alpha,
         boolean requiresPrunedEntries
@@ -159,16 +159,16 @@ private static Tuple<Map<String, Float>, Map<String, Float>> pruningByAlphaMass(
     }
 
     /**
-     * Prunes a sparse vector using the specified pruning type and ratio.
+     * Prunes a sparse vector using the specified prune type and ratio.
      *
-     * @param pruneType The type of pruning strategy to use
-     * @param pruneRatio The ratio or threshold for pruning
+     * @param pruneType The type of prune strategy to use
+     * @param pruneRatio The ratio or threshold for prune
      * @param sparseVector The input sparse vector as a map of string keys to float values
      * @param requiresPrunedEntries Whether to return pruned entries
      * @return A tuple containing two maps: the first with high-scoring elements,
      *         the second with low-scoring elements (or null if requiresPrunedEntries is false)
      */
-    public static Tuple<Map<String, Float>, Map<String, Float>> pruningSparseVector(
+    public static Tuple<Map<String, Float>, Map<String, Float>> pruneSparseVector(
         PruneType pruneType,
         float pruneRatio,
         Map<String, Float> sparseVector,
@@ -190,29 +190,29 @@ public static Tuple<Map<String, Float>, Map<String, Float>> pruningSparseVector(
 
         switch (pruneType) {
             case TOP_K:
-                return pruningByTopK(sparseVector, (int) pruneRatio, requiresPrunedEntries);
+                return pruneByTopK(sparseVector, (int) pruneRatio, requiresPrunedEntries);
             case ALPHA_MASS:
-                return pruningByAlphaMass(sparseVector, pruneRatio, requiresPrunedEntries);
+                return pruneByAlphaMass(sparseVector, pruneRatio, requiresPrunedEntries);
             case MAX_RATIO:
-                return pruningByMaxRatio(sparseVector, pruneRatio, requiresPrunedEntries);
+                return pruneByMaxRatio(sparseVector, pruneRatio, requiresPrunedEntries);
             case ABS_VALUE:
-                return pruningByValue(sparseVector, pruneRatio, requiresPrunedEntries);
+                return pruneByValue(sparseVector, pruneRatio, requiresPrunedEntries);
             default:
                 return new Tuple<>(new HashMap<>(sparseVector), requiresPrunedEntries ? new HashMap<>() : null);
         }
     }
 
     /**
-     * Validates whether a prune ratio is valid for a given pruning type.
+     * Validates whether a prune ratio is valid for a given prune type.
      *
-     * @param pruneType The type of pruning strategy
+     * @param pruneType The type of prune strategy
      * @param pruneRatio The ratio or threshold to validate
-     * @return true if the ratio is valid for the given pruning type, false otherwise
-     * @throws IllegalArgumentException if pruning type is null
+     * @return true if the ratio is valid for the given prune type, false otherwise
+     * @throws IllegalArgumentException if prune type is null
      */
     public static boolean isValidPruneRatio(PruneType pruneType, float pruneRatio) {
         if (pruneType == null) {
-            throw new IllegalArgumentException("Pruning type cannot be null");
+            throw new IllegalArgumentException("Prune type cannot be null");
         }
 
         switch (pruneType) {

diff --git a/src/test/java/org/opensearch/neuralsearch/processor/NeuralSparseTwoPhaseProcessorTests.java b/src/test/java/org/opensearch/neuralsearch/processor/NeuralSparseTwoPhaseProcessorTests.java
@@ -9,8 +9,8 @@
 import org.opensearch.index.query.BoolQueryBuilder;
 import org.opensearch.index.query.MatchAllQueryBuilder;
 import org.opensearch.neuralsearch.query.NeuralSparseQueryBuilder;
-import org.opensearch.neuralsearch.util.pruning.PruneType;
-import org.opensearch.neuralsearch.util.pruning.PruneUtils;
+import org.opensearch.neuralsearch.util.prune.PruneType;
+import org.opensearch.neuralsearch.util.prune.PruneUtils;
 import org.opensearch.search.builder.SearchSourceBuilder;
 import org.opensearch.search.rescore.QueryRescorerBuilder;
 import org.opensearch.test.OpenSearchTestCase;

diff --git a/src/test/java/org/opensearch/neuralsearch/processor/SparseEncodingProcessorTests.java b/src/test/java/org/opensearch/neuralsearch/processor/SparseEncodingProcessorTests.java
@@ -51,7 +51,7 @@
 import com.google.common.collect.ImmutableMap;
 
 import lombok.SneakyThrows;
-import org.opensearch.neuralsearch.util.pruning.PruneType;
+import org.opensearch.neuralsearch.util.prune.PruneType;
 
 public class SparseEncodingProcessorTests extends InferenceProcessorTestCase {
     @Mock
@@ -275,7 +275,7 @@ public void test_batchExecute_exception() {
     }
 
     @SuppressWarnings("unchecked")
-    public void testExecute_withPruningConfig_successful() {
+    public void testExecute_withPruneConfig_successful() {
         Map<String, Object> sourceAndMetadata = new HashMap<>();
         sourceAndMetadata.put(IndexFieldMapper.NAME, "my_index");
         sourceAndMetadata.put("key1", "value1");
@@ -317,7 +317,7 @@ public void testExecute_withPruningConfig_successful() {
         assertEquals(0.4f, second.get("low"), 0.001f);
     }
 
-    public void test_batchExecute_withPruning_successful() {
+    public void test_batchExecute_withPrune_successful() {
         SparseEncodingProcessor processor = createInstance(PruneType.MAX_RATIO, 0.5f);
 
         List<Map<String, ?>> mockMLResponse = Collections.singletonList(

diff --git a/...ensearch/neuralsearch/processor/factory/SparseEncodingEmbeddingProcessorFactoryTests.java b/...ensearch/neuralsearch/processor/factory/SparseEncodingEmbeddingProcessorFactoryTests.java
@@ -8,8 +8,8 @@
 import static org.opensearch.neuralsearch.processor.TextEmbeddingProcessor.MODEL_ID_FIELD;
 import static org.opensearch.neuralsearch.processor.TextEmbeddingProcessor.FIELD_MAP_FIELD;
 import static org.opensearch.neuralsearch.processor.SparseEncodingProcessor.TYPE;
-import static org.opensearch.neuralsearch.util.pruning.PruneUtils.PRUNE_TYPE_FIELD;
-import static org.opensearch.neuralsearch.util.pruning.PruneUtils.PRUNE_RATIO_FIELD;
+import static org.opensearch.neuralsearch.util.prune.PruneUtils.PRUNE_TYPE_FIELD;
+import static org.opensearch.neuralsearch.util.prune.PruneUtils.PRUNE_RATIO_FIELD;
 
 import lombok.SneakyThrows;
 import org.junit.Before;
@@ -18,7 +18,7 @@
 import org.opensearch.env.Environment;
 import org.opensearch.neuralsearch.ml.MLCommonsClientAccessor;
 import org.opensearch.neuralsearch.processor.SparseEncodingProcessor;
-import org.opensearch.neuralsearch.util.pruning.PruneType;
+import org.opensearch.neuralsearch.util.prune.PruneType;
 import org.opensearch.test.OpenSearchTestCase;
 
 import java.util.HashMap;
@@ -134,7 +134,7 @@ public void testCreateProcessor_whenInvalidPruneType_thenFail() {
             IllegalArgumentException.class,
             () -> sparseEncodingProcessorFactory.create(Map.of(), PROCESSOR_TAG, DESCRIPTION, config)
         );
-        assertEquals("Unknown pruning type: invalid_prune_type", exception.getMessage());
+        assertEquals("Unknown prune type: invalid_prune_type", exception.getMessage());
     }
 
     @SneakyThrows

diff --git a/src/test/java/org/opensearch/neuralsearch/query/NeuralSparseQueryBuilderTests.java b/src/test/java/org/opensearch/neuralsearch/query/NeuralSparseQueryBuilderTests.java
@@ -52,7 +52,7 @@
 import org.opensearch.neuralsearch.ml.MLCommonsClientAccessor;
 import org.opensearch.neuralsearch.util.NeuralSearchClusterTestUtils;
 import org.opensearch.neuralsearch.util.NeuralSearchClusterUtil;
-import org.opensearch.neuralsearch.util.pruning.PruneType;
+import org.opensearch.neuralsearch.util.prune.PruneType;
 import org.opensearch.test.OpenSearchTestCase;
 
 import lombok.SneakyThrows;

diff --git a/...alsearch/util/pruning/PruneTypeTests.java → ...uralsearch/util/prune/PruneTypeTests.java b/...alsearch/util/pruning/PruneTypeTests.java → ...uralsearch/util/prune/PruneTypeTests.java
@@ -2,7 +2,7 @@
  * Copyright OpenSearch Contributors
  * SPDX-License-Identifier: Apache-2.0
  */
-package org.opensearch.neuralsearch.util.pruning;
+package org.opensearch.neuralsearch.util.prune;
 
 import org.opensearch.test.OpenSearchTestCase;
 
@@ -25,6 +25,6 @@ public void testFromString() {
         assertEquals(PruneType.ABS_VALUE, PruneType.fromString("abs_value"));
 
         IllegalArgumentException exception = assertThrows(IllegalArgumentException.class, () -> PruneType.fromString("test_value"));
-        assertEquals("Unknown pruning type: test_value", exception.getMessage());
+        assertEquals("Unknown prune type: test_value", exception.getMessage());
     }
 }