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Adding L2 norm technique #236
Adding L2 norm technique #236
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Signed-off-by: Martin Gaievski <[email protected]>
Signed-off-by: Martin Gaievski <[email protected]>
Codecov Report
@@ Coverage Diff @@
## feature/normalization #236 +/- ##
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- Coverage 85.95% 82.43% -3.53%
- Complexity 310 323 +13
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Files 24 26 +2
Lines 904 979 +75
Branches 137 153 +16
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+ Hits 777 807 +30
- Misses 67 108 +41
- Partials 60 64 +4
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.../java/org/opensearch/neuralsearch/processor/normalization/L2ScoreNormalizationTechnique.java
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Signed-off-by: Martin Gaievski <[email protected]>
6ad641a
into
opensearch-project:feature/normalization
public float combine(final float[] scores) { | ||
float combinedScore = 0.0f; | ||
float weights = 0; | ||
for (int indexOfSubQuery = 0; indexOfSubQuery < scores.length; indexOfSubQuery++) { | ||
float score = scores[indexOfSubQuery]; | ||
if (score >= 0.0) { | ||
float weight = getWeightForSubQuery(indexOfSubQuery); | ||
score = score * weight; | ||
combinedScore += score; | ||
weights += weight; | ||
} | ||
} | ||
if (weights == 0.0f) { | ||
return ZERO_SCORE; | ||
} | ||
return combinedScore / weights; | ||
} |
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weighted harmonic mean = sum(wi) / sum(1/(wi*si))
https://en.wikipedia.org/wiki/Harmonic_mean
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Thanks @HenryL27 , that's what I'm working now and it will be a new PR for harmonic mean and weighted geometric combination. Probably checked in this class incidentally in a half-ready form.
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ah, no worries. Thanks!
Description
Adding L2 normalization technique for normalization processor. Essentially L2 norm is "score" divided by square root of sum of score squares (more details here). Example of pipeline with processor config:
Issues Resolved
#228, part of solution for #126
Check List
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