benwtrent commented on issue #14342:
URL: https://github.com/apache/lucene/issues/14342#issuecomment-2734567137
OK, a colleague and I spent some time digging into this and Option 0 (a bug)
turned out to be the case. Its a 5 character change (like all good bugs), but
here are the new recall numbers for fashion-minst:
Still not mid 90s at 5x oversampling, but WAY better than the abysmal
results from before.
```
FLAT Results:
recall latency(ms) nDoc topK fanout quantized index(s) index_docs/s
num_segments index_size(MB) overSample vec_disk(MB) vec_RAM(MB) indexType
0.444 4.110 60000 10 50 1 bits 0.00 Infinity
1 186.43 1.000 181.646 2.203 FLAT
0.629 4.383 60000 10 50 1 bits 0.00 Infinity
1 186.43 2.000 181.646 2.203 FLAT
0.730 4.437 60000 10 50 1 bits 0.00 Infinity
1 186.43 3.000 181.646 2.203 FLAT
0.792 4.455 60000 10 50 1 bits 0.00 Infinity
1 186.43 4.000 181.646 2.203 FLAT
0.833 4.445 60000 10 50 1 bits 0.00 Infinity
1 186.43 5.000 181.646 2.203 FLAT
0.926 4.607 60000 10 50 1 bits 0.00 Infinity
1 186.43 10.000 181.646 2.203 FLAT
```
```
HNSW
recall latency(ms) nDoc topK fanout maxConn beamWidth quantized
index(s) index_docs/s num_segments index_size(MB) overSample vec_disk(MB)
vec_RAM(MB) indexType
0.443 0.188 60000 10 50 64 250 1 bits
0.00 Infinity 1 189.55 1.000 181.646
2.203 HNSW
0.629 0.274 60000 10 50 64 250 1 bits
0.00 Infinity 1 189.55 2.000 181.646
2.203 HNSW
0.730 0.349 60000 10 50 64 250 1 bits
0.00 Infinity 1 189.55 3.000 181.646
2.203 HNSW
0.792 0.471 60000 10 50 64 250 1 bits
0.00 Infinity 1 189.55 4.000 181.646
2.203 HNSW
0.833 0.479 60000 10 50 64 250 1 bits
0.00 Infinity 1 189.55 5.000 181.646
2.203 HNSW
0.926 0.786 60000 10 50 64 250 1 bits
0.00 Infinity 1 189.55 10.000 181.646
2.203 HNSW
```
For the curious, it had to do with shifting the normal distribution
initialization parameters correctly given the standard deviation of the actual
vector distribution. We had the mean & std flipped. When these are well
behaved, this sort of bug has a tiny effect (which is why we never caught it),
but minst isn't well behaved and brought this nasty little bug to light.
I am gonna run some more benchmarks and will open a PR soon with the fix.
As an aside, there is likely even more gains for non-normal distribution
vectors like minst, but they will take more time and effort.
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