Add alternative formula for verifying geometric mean

Signed-off-by: Martin Gaievski <[email protected]>

src/test/java/org/opensearch/neuralsearch/processor/combination/ArithmeticMeanScoreCombinationTechniqueTests.java

@@ -32,17 +32,6 @@ public void testLogic_whenNotAllScoresPresentAndNoWeights_thenCorrectScores() {
         testLogic_whenNotAllScoresPresentAndNoWeights_thenCorrectScores(technique);
     }
 
-    public void testLogic_whenAllScoresAndWeightsPresent_thenCorrectScores() {
-        List<Float> scores = List.of(1.0f, 0.5f, 0.3f);
-        List<Double> weights = List.of(0.9, 0.2, 0.7);
-        ScoreCombinationTechnique technique = new ArithmeticMeanScoreCombinationTechnique(
-            Map.of(PARAM_NAME_WEIGHTS, weights),
-            scoreCombinationUtil
-        );
-        float expectedScore = 0.6722f;
-        testLogic_whenAllScoresAndWeightsPresent_thenCorrectScores(technique, scores, expectedScore);
-    }
-
     public void testRandomValues_whenAllScoresAndWeightsPresent_thenCorrectScores() {
         List<Double> weights = IntStream.range(0, RANDOM_SCORES_SIZE)
             .mapToObj(i -> RandomizedTest.randomDouble())

src/test/java/org/opensearch/neuralsearch/processor/combination/GeometricMeanScoreCombinationTechniqueTests.java

@@ -76,17 +76,23 @@ public void testRandomValues_whenNotAllScoresAndWeightsPresent_thenCorrectScores
         testRandomValues_whenNotAllScoresAndWeightsPresent_thenCorrectScores(technique, weights);
     }
 
+    /**
+     * Verify score correctness by using alternative formula for geometric mean as n-th root of product of weighted scores,
+     * more details in here https://en.wikipedia.org/wiki/Weighted_geometric_mean
+     */
     private float geometricMean(List<Float> scores, List<Double> weights) {
-        assertEquals(scores.size(), weights.size());
-        float sumOfWeights = 0;
-        float weightedSumOfLn = 0;
-        for (int i = 0; i < scores.size(); i++) {
-            float score = scores.get(i), weight = weights.get(i).floatValue();
-            if (score > 0) {
-                sumOfWeights += weight;
-                weightedSumOfLn += weight * Math.log(score);
+        float product = 1.0f;
+        float sumOfWeights = 0.0f;
+        for (int indexOfSubQuery = 0; indexOfSubQuery < scores.size(); indexOfSubQuery++) {
+            float score = scores.get(indexOfSubQuery);
+            if (score <= 0) {
+                // scores 0.0 need to be skipped, ln() of 0 is not defined
+                continue;
             }
+            float weight = weights.get(indexOfSubQuery).floatValue();
+            product *= Math.pow(score, weight);
+            sumOfWeights += weight;
         }
-        return sumOfWeights == 0 ? 0f : (float) Math.exp(weightedSumOfLn / sumOfWeights);
+        return sumOfWeights == 0 ? 0f : (float) Math.pow(product, (float) 1 / sumOfWeights);
     }
 }

src/test/java/org/opensearch/neuralsearch/processor/combination/HarmonicMeanScoreCombinationTechniqueTests.java

@@ -43,17 +43,6 @@ public void testLogic_whenAllScoresAndWeightsPresent_thenCorrectScores() {
         testLogic_whenAllScoresAndWeightsPresent_thenCorrectScores(technique, scores, expecteScore);
     }
 
-    public void testRandomValues_whenAllScoresAndWeightsPresent_thenCorrectScores() {
-        List<Double> weights = IntStream.range(0, RANDOM_SCORES_SIZE)
-            .mapToObj(i -> RandomizedTest.randomDouble())
-            .collect(Collectors.toList());
-        ScoreCombinationTechnique technique = new HarmonicMeanScoreCombinationTechnique(
-            Map.of(PARAM_NAME_WEIGHTS, weights),
-            scoreCombinationUtil
-        );
-        testRandomValues_whenAllScoresAndWeightsPresent_thenCorrectScores(technique, weights);
-    }
-
     public void testLogic_whenNotAllScoresAndWeightsPresent_thenCorrectScores() {
         List<Float> scores = List.of(1.0f, -1.0f, 0.6f);
         List<Double> weights = List.of(0.9, 0.2, 0.7);