Package evaluation of SimilaritySearch on Julia 1.13.0-DEV.1265 (cd62cf9a08*) started at 2025-10-05T19:55:04.540


################################################################################
# Set-up
#

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 9.58s


################################################################################
# Installation
#

Installing SimilaritySearch...
   Resolving package versions...
   Installed SimilaritySearch ─ v0.13.1
    Updating `~/.julia/environments/v1.13/Project.toml`
  [053f045d] + SimilaritySearch v0.13.1
    Updating `~/.julia/environments/v1.13/Manifest.toml`
  [7d9f7c33] + Accessors v0.1.42
  [79e6a3ab] + Adapt v4.4.0
  [4fba245c] + ArrayInterface v7.20.0
  [62783981] + BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] + CPUSummary v0.2.7
  [fb6a15b2] + CloseOpenIntervals v0.1.13
  [da1fd8a2] + CodeTracking v2.0.1
  [f70d9fcc] + CommonWorldInvalidations v1.0.0
  [34da2185] + Compat v4.18.1
  [807dbc54] + Compiler v0.1.1
  [a33af91c] + CompositionsBase v0.1.2
  [187b0558] + ConstructionBase v1.6.0
  [adafc99b] + CpuId v0.3.1
  [9a962f9c] + DataAPI v1.16.0
⌅ [864edb3b] + DataStructures v0.18.22
  [b4f34e82] + Distances v0.10.12
  [ffbed154] + DocStringExtensions v0.9.5
  [5789e2e9] + FileIO v1.17.0
  [076d061b] + HashArrayMappedTries v0.2.0
  [615f187c] + IfElse v0.1.1
  [3587e190] + InverseFunctions v0.1.17
  [92d709cd] + IrrationalConstants v0.2.4
  [c3a54625] + JET v0.10.8
⌅ [033835bb] + JLD2 v0.5.15
  [aa1ae85d] + JuliaInterpreter v0.10.5
  [70703baa] + JuliaSyntax v1.0.2
  [10f19ff3] + LayoutPointers v0.1.17
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [6f1432cf] + LoweredCodeUtils v3.4.4
  [1914dd2f] + MacroTools v0.5.16
  [d125e4d3] + ManualMemory v0.1.8
  [e1d29d7a] + Missings v1.2.0
  [bac558e1] + OrderedCollections v1.8.1
  [d96e819e] + Parameters v0.12.3
  [f517fe37] + Polyester v0.7.18
  [1d0040c9] + PolyesterWeave v0.2.2
  [aea7be01] + PrecompileTools v1.3.3
  [21216c6a] + Preferences v1.5.0
  [92933f4c] + ProgressMeter v1.11.0
  [ae029012] + Requires v1.3.1
  [94e857df] + SIMDTypes v0.1.0
  [431bcebd] + SciMLPublic v1.0.0
  [7e506255] + ScopedValues v1.5.0
  [0e966ebe] + SearchModels v0.4.1
  [053f045d] + SimilaritySearch v0.13.1
  [a2af1166] + SortingAlgorithms v1.2.2
  [aedffcd0] + Static v1.3.0
  [0d7ed370] + StaticArrayInterface v1.8.0
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.7.1
⌅ [2913bbd2] + StatsBase v0.33.21
  [7792a7ef] + StrideArraysCore v0.5.8
  [8290d209] + ThreadingUtilities v0.5.5
  [3bb67fe8] + TranscodingStreams v0.11.3
  [3a884ed6] + UnPack v1.0.2
  [0dad84c5] + ArgTools v1.1.2
  [56f22d72] + Artifacts v1.11.0
  [2a0f44e3] + Base64 v1.11.0
  [ade2ca70] + Dates v1.11.0
  [8ba89e20] + Distributed v1.11.0
  [f43a241f] + Downloads v1.7.0
  [7b1f6079] + FileWatching v1.11.0
  [b77e0a4c] + InteractiveUtils v1.11.0
  [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0
  [b27032c2] + LibCURL v0.6.4
  [76f85450] + LibGit2 v1.11.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.13.0
  [56ddb016] + Logging v1.11.0
  [d6f4376e] + Markdown v1.11.0
  [a63ad114] + Mmap v1.11.0
  [ca575930] + NetworkOptions v1.3.0
  [44cfe95a] + Pkg v1.13.0
  [de0858da] + Printf v1.11.0
  [9a3f8284] + Random v1.11.0
  [ea8e919c] + SHA v0.7.0
  [9e88b42a] + Serialization v1.11.0
  [6462fe0b] + Sockets v1.11.0
  [2f01184e] + SparseArrays v1.13.0
  [f489334b] + StyledStrings v1.11.0
  [fa267f1f] + TOML v1.0.3
  [a4e569a6] + Tar v1.10.0
  [8dfed614] + Test v1.11.0
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1
  [deac9b47] + LibCURL_jll v8.16.0+0
  [e37daf67] + LibGit2_jll v1.9.1+0
  [29816b5a] + LibSSH2_jll v1.11.3+1
  [14a3606d] + MozillaCACerts_jll v2025.9.9
  [4536629a] + OpenBLAS_jll v0.3.29+0
  [458c3c95] + OpenSSL_jll v3.5.4+0
  [efcefdf7] + PCRE2_jll v10.46.0+0
  [bea87d4a] + SuiteSparse_jll v7.10.1+0
  [83775a58] + Zlib_jll v1.3.1+2
  [3161d3a3] + Zstd_jll v1.5.7+1
  [8e850b90] + libblastrampoline_jll v5.13.1+0
  [8e850ede] + nghttp2_jll v1.67.1+0
  [3f19e933] + p7zip_jll v17.6.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`

Installation completed after 5.53s


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# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 43.37s


################################################################################
# Testing
#

     Testing SimilaritySearch
      Status `/tmp/jl_uwk0h4/Project.toml`
  [7d9f7c33] Accessors v0.1.42
  [4c88cf16] Aqua v0.8.14
  [b4f34e82] Distances v0.10.12
  [c3a54625] JET v0.10.8
⌅ [033835bb] JLD2 v0.5.15
  [d96e819e] Parameters v0.12.3
  [f517fe37] Polyester v0.7.18
  [92933f4c] ProgressMeter v1.11.0
  [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.1
  [10745b16] Statistics v1.11.1
⌅ [2913bbd2] StatsBase v0.33.21
  [7792a7ef] StrideArraysCore v0.5.8
  [ade2ca70] Dates v1.11.0
  [37e2e46d] LinearAlgebra v1.13.0
  [9a3f8284] Random v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_uwk0h4/Manifest.toml`
  [7d9f7c33] Accessors v0.1.42
  [79e6a3ab] Adapt v4.4.0
  [4c88cf16] Aqua v0.8.14
  [4fba245c] ArrayInterface v7.20.0
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] CPUSummary v0.2.7
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [da1fd8a2] CodeTracking v2.0.1
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.18.1
  [807dbc54] Compiler v0.1.1
  [a33af91c] CompositionsBase v0.1.2
  [187b0558] ConstructionBase v1.6.0
  [adafc99b] CpuId v0.3.1
  [9a962f9c] DataAPI v1.16.0
⌅ [864edb3b] DataStructures v0.18.22
  [b4f34e82] Distances v0.10.12
  [ffbed154] DocStringExtensions v0.9.5
  [5789e2e9] FileIO v1.17.0
  [076d061b] HashArrayMappedTries v0.2.0
  [615f187c] IfElse v0.1.1
  [3587e190] InverseFunctions v0.1.17
  [92d709cd] IrrationalConstants v0.2.4
  [c3a54625] JET v0.10.8
⌅ [033835bb] JLD2 v0.5.15
  [aa1ae85d] JuliaInterpreter v0.10.5
  [70703baa] JuliaSyntax v1.0.2
  [10f19ff3] LayoutPointers v0.1.17
  [2ab3a3ac] LogExpFunctions v0.3.29
  [6f1432cf] LoweredCodeUtils v3.4.4
  [1914dd2f] MacroTools v0.5.16
  [d125e4d3] ManualMemory v0.1.8
  [e1d29d7a] Missings v1.2.0
  [bac558e1] OrderedCollections v1.8.1
  [d96e819e] Parameters v0.12.3
  [f517fe37] Polyester v0.7.18
  [1d0040c9] PolyesterWeave v0.2.2
  [aea7be01] PrecompileTools v1.3.3
  [21216c6a] Preferences v1.5.0
  [92933f4c] ProgressMeter v1.11.0
  [ae029012] Requires v1.3.1
  [94e857df] SIMDTypes v0.1.0
  [431bcebd] SciMLPublic v1.0.0
  [7e506255] ScopedValues v1.5.0
  [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.1
  [a2af1166] SortingAlgorithms v1.2.2
  [aedffcd0] Static v1.3.0
  [0d7ed370] StaticArrayInterface v1.8.0
  [10745b16] Statistics v1.11.1
  [82ae8749] StatsAPI v1.7.1
⌅ [2913bbd2] StatsBase v0.33.21
  [7792a7ef] StrideArraysCore v0.5.8
  [8290d209] ThreadingUtilities v0.5.5
  [3bb67fe8] TranscodingStreams v0.11.3
  [3a884ed6] UnPack v1.0.2
  [0dad84c5] ArgTools v1.1.2
  [56f22d72] Artifacts v1.11.0
  [2a0f44e3] Base64 v1.11.0
  [ade2ca70] Dates v1.11.0
  [8ba89e20] Distributed v1.11.0
  [f43a241f] Downloads v1.7.0
  [7b1f6079] FileWatching v1.11.0
  [b77e0a4c] InteractiveUtils v1.11.0
  [ac6e5ff7] JuliaSyntaxHighlighting v1.12.0
  [b27032c2] LibCURL v0.6.4
  [76f85450] LibGit2 v1.11.0
  [8f399da3] Libdl v1.11.0
  [37e2e46d] LinearAlgebra v1.13.0
  [56ddb016] Logging v1.11.0
  [d6f4376e] Markdown v1.11.0
  [a63ad114] Mmap v1.11.0
  [ca575930] NetworkOptions v1.3.0
  [44cfe95a] Pkg v1.13.0
  [de0858da] Printf v1.11.0
  [9a3f8284] Random v1.11.0
  [ea8e919c] SHA v0.7.0
  [9e88b42a] Serialization v1.11.0
  [6462fe0b] Sockets v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [f489334b] StyledStrings v1.11.0
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test v1.11.0
  [cf7118a7] UUIDs v1.11.0
  [4ec0a83e] Unicode v1.11.0
  [e66e0078] CompilerSupportLibraries_jll v1.3.0+1
  [deac9b47] LibCURL_jll v8.16.0+0
  [e37daf67] LibGit2_jll v1.9.1+0
  [29816b5a] LibSSH2_jll v1.11.3+1
  [14a3606d] MozillaCACerts_jll v2025.9.9
  [4536629a] OpenBLAS_jll v0.3.29+0
  [458c3c95] OpenSSL_jll v3.5.4+0
  [efcefdf7] PCRE2_jll v10.46.0+0
  [bea87d4a] SuiteSparse_jll v7.10.1+0
  [83775a58] Zlib_jll v1.3.1+2
  [3161d3a3] Zstd_jll v1.5.7+1
  [8e850b90] libblastrampoline_jll v5.13.1+0
  [8e850ede] nghttp2_jll v1.67.1+0
  [3f19e933] p7zip_jll v17.6.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading.
     Testing Running tests...
Test Summary:              | Pass  Total   Time
test database abstractions |   56     56  15.9s
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 20005  20005  3.7s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.5s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.1s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.813948 seconds (1000 allocations: 78.125 KiB)
 10.624236 seconds (1000 allocations: 78.125 KiB)
  3.792338 seconds (1000 allocations: 78.125 KiB)
  3.835275 seconds (1000 allocations: 78.125 KiB)
  3.824420 seconds (1000 allocations: 78.125 KiB)
  3.826216 seconds (1000 allocations: 78.125 KiB)
  3.782515 seconds (1000 allocations: 78.125 KiB)
  3.797705 seconds (1000 allocations: 78.125 KiB)
 15.422005 seconds (1000 allocations: 78.125 KiB)
 15.340214 seconds (1000 allocations: 78.125 KiB)
 28.165447 seconds (1000 allocations: 78.125 KiB)
 28.673645 seconds (1000 allocations: 78.125 KiB)
 20.695365 seconds (6.23 k allocations: 388.672 KiB)
 20.780378 seconds (1000 allocations: 78.125 KiB)
 18.550220 seconds (1.00 k allocations: 78.141 KiB)
 17.790103 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m41.1s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  2.831306 seconds (1000 allocations: 78.125 KiB)
  2.885754 seconds (1000 allocations: 78.125 KiB)
 29.979964 seconds (1000 allocations: 78.125 KiB)
 28.871831 seconds (1000 allocations: 78.125 KiB)
 28.726626 seconds (1000 allocations: 78.125 KiB)
 29.008498 seconds (1000 allocations: 78.125 KiB)
  4.185438 seconds (1000 allocations: 78.125 KiB)
  4.157818 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m14.4s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  8.282028 seconds (1000 allocations: 78.125 KiB)
  8.337818 seconds (1000 allocations: 78.125 KiB)
  8.372825 seconds (1000 allocations: 78.125 KiB)
  8.061173 seconds (1000 allocations: 78.125 KiB)
  7.969613 seconds (1000 allocations: 78.125 KiB)
  7.992160 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total   Time
indexing sets with ExhaustiveSearch | 3000   3000  51.7s
  0.049809 seconds (1.00 k allocations: 78.141 KiB)
  0.048821 seconds (1000 allocations: 78.125 KiB)
  0.040209 seconds (1000 allocations: 78.125 KiB)
  0.040066 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  3.0s
  0.054176 seconds (1000 allocations: 78.125 KiB)
  0.053673 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.4s
ExhaustiveSearch allknn: 3.983144 seconds (2.22 M allocations: 123.651 MiB, 2.96% gc time, 99.95% compilation time)
ParallelExhaustiveSearch allknn: 1.182310 seconds (610.09 k allocations: 31.953 MiB, 99.84% compilation time)
Test Summary: | Pass  Total   Time
allknn        |    5      5  58.0s
quantile(length.(hsp_knns), 0:0.1:1) = [2.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0]
Test Summary: | Total  Time
HSP           |     0  2.9s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:10.480
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-05T20:05:10.717
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=2 ep=2 n=2 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-05T20:05:11.895
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:12.283
D.map = UInt32[0x00000001, 0x00000002, 0x00000009, 0x0000000d, 0x0000000e, 0x00000010, 0x00000013, 0x0000001c, 0x0000005b, 0x0000005f]
D.nn = Int32[1, 2, 1, 1, 1, 1, 1, 1, 9, 9, 1, 1, 13, 14, 1, 16, 16, 16, 19, 19, 14, 2, 2, 14, 1, 1, 19, 28, 28, 1, 14, 28, 16, 28, 16, 16, 2, 2, 1, 1, 16, 16, 1, 14, 16, 1, 1, 19, 19, 2, 14, 2, 14, 16, 16, 16, 19, 16, 1, 13, 13, 1, 2, 13, 2, 28, 19, 1, 1, 28, 1, 16, 13, 1, 1, 16, 1, 1, 1, 19, 9, 1, 16, 2, 28, 1, 1, 16, 1, 1, 91, 1, 1, 28, 95, 1, 1, 9, 1, 16]
D.dist = Float32[0.0, 0.0, 0.02964753, 0.09617543, 0.02758038, 0.06566042, 0.05970186, 0.029565334, 0.0, 0.090733886, 0.052600086, 0.050735295, 0.0, 0.0, 0.0034168363, 0.0, 0.029526174, 0.072537065, 0.0, 0.042692244, 0.08296162, 0.0477646, 0.036386132, 0.012775481, 0.061964333, 0.026016474, 0.017789066, 0.0, 0.013723552, 0.029159367, 0.04973501, 0.0036650896, 0.025710821, 0.07560563, 0.03829068, 0.029923737, 0.057374954, 0.015566707, 0.058216333, 0.035560787, 0.05921656, 0.018956244, 0.0463019, 0.050911903, 0.04097569, 0.028906822, 0.03504461, 0.018271148, 0.010820448, 0.031562805, 0.01537317, 0.043145835, 0.001506865, 0.038546026, 0.040350854, 0.019049048, 0.04409474, 0.09359902, 0.021031141, 0.013049424, 0.056151927, 0.048101366, 0.049418032, 0.039772153, 0.07144475, 0.029774308, 0.09680563, 0.06201023, 0.018442094, 0.013809383, 0.031808138, 0.054539084, 0.07441741, 0.00835228, 0.06966579, 0.006434977, 0.06199038, 0.015256524, 0.03041017, 0.02155763, 0.029822886, 0.059901237, 0.019394755, 0.02099675, 0.014197111, 0.097625434, 0.04031509, 0.037318766, 0.050180197, 0.016486645, 0.0, 0.06207794, 0.034353852, 0.008635461, 0.0, 0.008319795, 0.035300255, 0.050644338, 0.015541613, 0.052889943]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  17.0s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.446
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-05T20:05:13.446
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.446
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.447
[ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.447
[ Info: neardup> range: 65:80, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.447
[ Info: neardup> range: 81:96, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.447
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.447
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.447
D.map = UInt32[0x00000001, 0x00000002, 0x00000009, 0x0000000d, 0x0000000e, 0x00000010, 0x00000013, 0x0000001c, 0x0000005b, 0x0000005f]
D.nn = Int32[1, 2, 1, 1, 1, 1, 1, 1, 9, 9, 1, 1, 13, 14, 1, 16, 16, 16, 19, 19, 14, 2, 2, 14, 1, 1, 19, 28, 16, 1, 14, 16, 16, 28, 16, 16, 2, 2, 1, 1, 16, 16, 1, 14, 16, 1, 1, 19, 19, 2, 14, 2, 14, 16, 16, 16, 19, 16, 1, 13, 13, 1, 2, 13, 2, 28, 19, 1, 1, 28, 1, 16, 13, 1, 1, 16, 1, 1, 1, 19, 9, 1, 16, 2, 28, 1, 1, 16, 1, 1, 91, 1, 1, 28, 95, 1, 1, 9, 1, 16]
D.dist = Float32[0.0, 0.0, 0.02964753, 0.09617543, 0.02758038, 0.06566042, 0.05970186, 0.029565334, 0.0, 0.090733886, 0.052600086, 0.050735295, 0.0, 0.0, 0.0034168363, 0.0, 0.029526174, 0.072537065, 0.0, 0.042692244, 0.08296162, 0.0477646, 0.036386132, 0.012775481, 0.061964333, 0.026016474, 0.017789066, 0.0, 0.07923961, 0.029159367, 0.04973501, 0.091567636, 0.025710821, 0.07560563, 0.03829068, 0.029923737, 0.057374954, 0.015566707, 0.058216333, 0.035560787, 0.05921656, 0.018956244, 0.0463019, 0.050911903, 0.04097569, 0.028906822, 0.03504461, 0.018271148, 0.010820448, 0.031562805, 0.01537317, 0.043145835, 0.001506865, 0.038546026, 0.040350854, 0.019049048, 0.04409474, 0.09359902, 0.021031141, 0.013049424, 0.056151927, 0.048101366, 0.049418032, 0.039772153, 0.07144475, 0.029774308, 0.09680563, 0.06201023, 0.018442094, 0.013809383, 0.031808138, 0.054539084, 0.07441741, 0.00835228, 0.06966579, 0.006434977, 0.06199038, 0.015256524, 0.03041017, 0.02155763, 0.029822886, 0.059901237, 0.019394755, 0.02099675, 0.014197111, 0.097625434, 0.04031509, 0.037318766, 0.050180197, 0.016486645, 0.0, 0.06207794, 0.034353852, 0.008635461, 0.0, 0.008319795, 0.035300255, 0.050644338, 0.015541613, 0.052889943]
Test Summary:       | Pass  Total  Time
neardup small block |    3      3  0.0s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.532
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-05T20:05:13.532
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.533
[ Info: neardup> range: 33:48, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.533
[ Info: neardup> range: 49:64, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.533
[ Info: neardup> range: 65:80, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.533
[ Info: neardup> range: 81:96, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.533
[ Info: neardup> range: 97:100, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.533
[ Info: neardup> finished current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:13.533
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000013, 0x0000001b, 0x0000001c]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 11, 11, 19, 12, 3, 2, 2, 14, 7, 1, 27, 28, 16, 8, 3, 16, 11, 28, 11, 11, 2, 2, 3, 3, 11, 16, 4, 3, 11, 1, 6, 19, 19, 2, 14, 12, 14, 11, 16, 16, 19, 7, 15, 13, 13, 8, 12, 13, 2, 28, 8, 3, 15, 28, 11, 11, 13, 1, 5, 16, 3, 1, 1, 27, 9, 7, 11, 2, 28, 7, 1, 11, 3, 15, 7, 8, 7, 28, 27, 1, 1, 9, 1, 16]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0045850873, 0.043676674, 0.0, 0.05825472, 0.04669285, 0.0477646, 0.036386132, 0.012775481, 0.008501053, 0.026016474, 0.0, 0.0, 0.07923961, 0.0049564242, 0.043899775, 0.091567636, 0.00555408, 0.07560563, 0.01101321, 0.010356247, 0.057374954, 0.015566707, 0.019508898, 0.0023070574, 0.007880986, 0.018956244, 0.044582248, 0.038161695, 0.040119886, 0.028906822, 0.007478535, 0.018271148, 0.010820448, 0.031562805, 0.01537317, 0.01761365, 0.001506865, 0.036670208, 0.040350854, 0.019049048, 0.04409474, 0.05336094, 0.011333168, 0.013049424, 0.056151927, 0.007617712, 0.026214004, 0.039772153, 0.07144475, 0.029774308, 0.022065997, 0.023972511, 0.014350891, 0.013809383, 0.011524618, 0.027168393, 0.07441741, 0.00835228, 0.014849305, 0.006434977, 0.011044741, 0.015256524, 0.03041017, 0.01774168, 0.029822886, 0.0110113025, 0.017247975, 0.02099675, 0.014197111, 0.04426217, 0.04031509, 0.024008334, 0.0060468316, 0.010456741, 0.054059744, 0.006127715, 0.004565537, 0.008635461, 0.05319047, 0.008319795, 0.035300255, 0.050644338, 0.015541613, 0.052889943]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  0.1s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:26.126
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=6 n=6 2025-10-05T20:05:26.126
[ Info: neardup> range: 17:32, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:26.130
[ Info: neardup> range: 33:48, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:26.130
[ Info: neardup> range: 49:64, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:26.130
[ Info: neardup> range: 65:80, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:26.130
[ Info: neardup> range: 81:96, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:26.130
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:26.130
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-10-05T20:05:26.130
D.map = UInt32[0x00000001, 0x00000002, 0x00000009, 0x0000000d, 0x0000000e, 0x00000010, 0x00000013, 0x0000001c, 0x0000005b, 0x0000005f]
D.nn = Int32[1, 2, 1, 1, 1, 1, 1, 1, 9, 9, 1, 1, 13, 14, 1, 16, 16, 16, 19, 19, 14, 2, 2, 14, 1, 1, 19, 28, 16, 1, 14, 16, 16, 28, 16, 16, 2, 2, 1, 1, 16, 16, 1, 14, 16, 1, 1, 19, 19, 2, 14, 2, 14, 16, 16, 16, 19, 16, 1, 13, 13, 1, 2, 13, 2, 28, 19, 1, 1, 28, 1, 16, 13, 1, 1, 16, 1, 1, 1, 19, 9, 1, 16, 2, 28, 1, 1, 16, 1, 1, 91, 1, 1, 28, 95, 1, 1, 9, 1, 16]
D.dist = Float32[0.0, 0.0, 0.02964753, 0.09617543, 0.02758038, 0.06566042, 0.05970186, 0.029565334, 0.0, 0.090733886, 0.052600086, 0.050735295, 0.0, 0.0, 0.0034168363, 0.0, 0.029526174, 0.072537065, 0.0, 0.042692244, 0.08296162, 0.0477646, 0.036386132, 0.012775481, 0.061964333, 0.026016474, 0.017789066, 0.0, 0.07923961, 0.029159367, 0.04973501, 0.091567636, 0.025710821, 0.07560563, 0.03829068, 0.029923737, 0.057374954, 0.015566707, 0.058216333, 0.035560787, 0.05921656, 0.018956244, 0.0463019, 0.050911903, 0.04097569, 0.028906822, 0.03504461, 0.018271148, 0.010820448, 0.031562805, 0.01537317, 0.043145835, 0.001506865, 0.038546026, 0.040350854, 0.019049048, 0.04409474, 0.09359902, 0.021031141, 0.013049424, 0.056151927, 0.048101366, 0.049418032, 0.039772153, 0.07144475, 0.029774308, 0.09680563, 0.06201023, 0.018442094, 0.013809383, 0.031808138, 0.054539084, 0.07441741, 0.00835228, 0.06966579, 0.006434977, 0.06199038, 0.015256524, 0.03041017, 0.02155763, 0.029822886, 0.059901237, 0.019394755, 0.02099675, 0.014197111, 0.097625434, 0.04031509, 0.037318766, 0.050180197, 0.016486645, 0.0, 0.06207794, 0.034353852, 0.008635461, 0.0, 0.008319795, 0.035300255, 0.050644338, 0.015541613, 0.052889943]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  6.2s
computing farthest point 1, dmax: Inf, imax: 22, n: 30
computing farthest point 2, dmax: 1.3327076, imax: 11, n: 30
computing farthest point 3, dmax: 0.9809202, imax: 10, n: 30
computing farthest point 4, dmax: 0.9468889, imax: 24, n: 30
computing farthest point 5, dmax: 0.79436624, imax: 26, n: 30
computing farthest point 6, dmax: 0.7171845, imax: 30, n: 30
computing farthest point 7, dmax: 0.63451844, imax: 13, n: 30
computing farthest point 8, dmax: 0.6285736, imax: 19, n: 30
computing farthest point 9, dmax: 0.60882217, imax: 9, n: 30
computing farthest point 10, dmax: 0.55080163, imax: 28, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.4s
Test Summary: | Pass  Total  Time
AdjacencyList |   15     15  1.7s
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-05T20:05:34.004
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch(bsize=5, Δ=0.8571428, maxvisits=106) 2025-10-05T20:05:45.952
LOG n.size quantiles:[2.0, 2.0, 2.0, 3.0, 3.0]
(i, j, d) = (614, 917, -2.3841858f-7)
(i, j, d, :parallel) = (614, 917, -2.3841858f-7, :parallel)
[ Info: NOTE: the exact method will be faster on small datasets due to the preprocessing step of the approximation method
[ Info: ("closestpair computation time", :approx => 19.230546996999998, :exact => 0.932496213)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  20.7s
  6.022316 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.005524 seconds
SEARCH Exhaustive 2: 0.005634 seconds
SEARCH Exhaustive 3: 0.005976 seconds
typeof(seq) = ExhaustiveSearch{SqL2Distance, MatrixDatabase{Matrix{Float32}}}
typeof(ectx) = GenericContext{KnnSorted}
typeof(q) = SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}
typeof(res) = KnnSorted{Vector{IdWeight}}
[ Info: ===================== minrecall ==============================
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-05T20:06:14.944
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch(bsize=10, Δ=1.21275, maxvisits=216) 2025-10-05T20:06:20.853
LOG n.size quantiles:[1.0, 1.0, 2.0, 2.0, 2.0]
[ Info: RECALL BAJO!! recall: 0.290625, #objects: 2219, #queries: 32
[ Info: [0.2778491973876953, 0.3561706840991974, 0.4492773115634918, 0.21197453141212463, 0.2966729402542114, 0.42496228218078613, 0.5683332681655884, 0.25955477356910706, 0.32855024933815, 0.8141998052597046, 0.30780571699142456, 0.27413153648376465, 0.5150328278541565, 0.47346657514572144, 0.5649875402450562, 0.3503963351249695, 0.35524943470954895, 0.8044920563697815, 0.3068603277206421, 0.32728683948516846, 0.5861053466796875, 0.37556031346321106, 0.36729565262794495, 0.6831424832344055, 0.4856611490249634, 0.6514014005661011, 0.5131964683532715, 0.2115810513496399, 0.2996685206890106, 0.6881325244903564, 0.2166748046875, 0.3902253806591034]
(g, r) = (Set(Int32[1450, 2038, 1641, 298, 1576, 2177, 1297, 1853, 358, 1829]), Set(Int32[527, 984, 1671, 1641, 1576, 2177, 358, 1853, 2182, 1920]))
(g, r) = (Set(Int32[447, 557, 1542, 1367, 1393, 1307, 1193, 1741, 63, 1067]), Set(Int32[447, 557, 1542, 283, 1367, 906, 1393, 1741, 1038, 1841]))
(g, r) = (Set(Int32[1075, 1588, 2114, 2153, 1236, 315, 964, 762, 1059, 1953]), Set(Int32[1950, 136, 1460, 964, 26, 2065, 2123, 863, 1939, 627]))
(g, r) = (Set(Int32[1511, 1237, 930, 1178, 1063, 1356, 1391, 1031, 1610, 1080]), Set(Int32[1181, 475, 984, 1178, 1063, 1356, 1031, 1610, 1080, 357]))
(g, r) = (Set(Int32[2216, 1397, 726, 1732, 167, 917, 1763, 1052, 659, 2077]), Set(Int32[1210, 1397, 1732, 1763, 1639, 1958, 1052, 659, 2077, 130]))
(g, r) = (Set(Int32[1919, 227, 452, 1168, 1652, 88, 1172, 1162, 1139, 720]), Set(Int32[681, 317, 1517, 812, 1652, 88, 1036, 1150, 1960, 1224]))
(g, r) = (Set(Int32[1753, 1755, 619, 1423, 2200, 431, 1600, 567, 1333, 739]), Set(Int32[1753, 2191, 1727, 1360, 1902, 1600, 1976, 1667, 1271, 1105]))
(g, r) = (Set(Int32[1988, 942, 1042, 612, 1798, 2120, 1816, 504, 2168, 1625]), Set(Int32[1988, 942, 1042, 1798, 2120, 1816, 504, 1167, 1395, 1625]))
(g, r) = (Set(Int32[511, 1955, 184, 2092, 1418, 323, 2063, 1354, 1977, 1581]), Set(Int32[99, 1421, 917, 892, 593, 1418, 323, 1005, 1354, 1093]))
(g, r) = (Set(Int32[309, 366, 693, 1940, 25, 1979, 760, 539, 130, 380]), Set(Int32[203, 986, 842, 926, 846, 869, 710, 346, 537, 934]))
(g, r) = (Set(Int32[2, 984, 1509, 2142, 1768, 1578, 1765, 290, 1407, 1522]), Set(Int32[1238, 2, 475, 1509, 601, 1578, 1765, 403, 290, 1522]))
(g, r) = (Set(Int32[1703, 1148, 1293, 1971, 860, 1986, 1390, 630, 2101, 289]), Set(Int32[1839, 1539, 1148, 1668, 1986, 1635, 1628, 1164, 1521, 1667]))
(g, r) = (Set(Int32[1351, 1658, 1457, 2107, 1041, 213, 353, 1896, 535, 89]), Set(Int32[549, 393, 304, 1948, 213, 353, 1547, 1980, 535, 89]))
(g, r) = (Set(Int32[2111, 866, 23, 409, 1245, 271, 623, 246, 200, 256]), Set(Int32[394, 1344, 1562, 92, 1006, 1167, 400, 39, 1463, 1351]))
(g, r) = (Set(Int32[745, 144, 1293, 1971, 126, 1850, 574, 728, 1977, 289]), Set(Int32[1396, 363, 1532, 2079, 1132, 587, 1983, 1773, 436, 1806]))
(g, r) = (Set(Int32[901, 141, 468, 261, 1662, 1279, 1512, 1410, 605, 730]), Set(Int32[141, 1920, 716, 468, 261, 1662, 489, 1512, 605, 730]))
(g, r) = (Set(Int32[423, 18, 714, 1307, 761, 1689, 1333, 1067, 63, 2100]), Set(Int32[447, 557, 283, 714, 1367, 1393, 38, 1741, 1079, 402]))
(g, r) = (Set(Int32[7, 253, 2211, 1401, 959, 1764, 1995, 2170, 1229, 1649]), Set(Int32[938, 876, 721, 909, 563, 556, 842, 1897, 346, 561]))
(g, r) = (Set(Int32[1533, 2137, 91, 79, 305, 191, 1221, 1160, 1206, 423]), Set(Int32[79, 91, 305, 191, 1221, 15, 1698, 1352, 1230, 1160]))
(g, r) = (Set(Int32[2218, 885, 2086, 1367, 2038, 1517, 729, 180, 1581, 1920]), Set(Int32[81, 1098, 1307, 729, 180, 1090, 1141, 298, 15, 9]))
(g, r) = (Set(Int32[553, 1128, 1728, 2006, 2141, 169, 1629, 1558, 1611, 448]), Set(Int32[445, 661, 1072, 1152, 362, 435, 157, 958, 1750, 674]))
(g, r) = (Set(Int32[2110, 1021, 1978, 637, 1571, 1417, 441, 1049, 795, 56]), Set(Int32[2110, 186, 1783, 1978, 219, 1571, 160, 846, 1796, 56]))
(g, r) = (Set(Int32[145, 1730, 1102, 932, 861, 1491, 1010, 222, 1964, 1463]), Set(Int32[2217, 1344, 2127, 849, 418, 480, 41, 39, 1463, 153]))
(g, r) = (Set(Int32[1050, 1504, 1245, 1979, 977, 429, 246, 869, 2112, 1138]), Set(Int32[2168, 1132, 634, 498, 842, 926, 1084, 1463, 537, 1206]))
(g, r) = (Set(Int32[1943, 1043, 2060, 917, 184, 1990, 2034, 2113, 845, 1421]), Set(Int32[1796, 2138, 1870, 1899, 2098, 846, 2034, 2113, 900, 659]))
(g, r) = (Set(Int32[2111, 1245, 226, 271, 246, 1089, 1525, 220, 200, 256]), Set(Int32[394, 1344, 1562, 842, 1006, 1167, 400, 39, 1463, 1351]))
(g, r) = (Set(Int32[1273, 1786, 386, 237, 1876, 2176, 572, 1568, 1581, 1920]), Set(Int32[1131, 276, 1186, 386, 1804, 364, 1494, 621, 1474, 1320]))
(g, r) = (Set(Int32[2081, 1591, 1486, 1054, 2128, 927, 1029, 414, 277, 1318]), Set(Int32[955, 1054, 927, 1028, 1029, 1980, 93, 1680, 277, 1318]))
(g, r) = (Set(Int32[1510, 746, 308, 440, 1413, 843, 1711, 1531, 739, 866]), Set(Int32[440, 204, 303, 117, 1413, 843, 12, 166, 100, 1252]))
(g, r) = (Set(Int32[502, 525, 1978, 125, 238, 512, 1361, 1903, 704, 1274]), Set(Int32[502, 1675, 997, 441, 1799, 554, 1929, 1635, 426, 594]))
(g, r) = (Set(Int32[322, 1780, 1176, 143, 125, 1807, 105, 1276, 1898, 2122]), Set(Int32[322, 1780, 1176, 143, 125, 105, 814, 1601, 1898, 2122]))
(g, r) = (Set(Int32[82, 858, 1429, 666, 611, 2035, 2047, 478, 356, 1614]), Set(Int32[831, 685, 1019, 1242, 2047, 1834, 1492, 1173, 1624, 100]))
collect(Int32, IdView(p)) = Int32[2177, 1576, 1853, 358, 1641, 1920, 527, 2182, 984, 1671]
collect(Int32, IdView(p)) = Int32[557, 1367, 1542, 447, 1741, 1393, 906, 1038, 1841, 283]
collect(Int32, IdView(p)) = Int32[964, 1460, 863, 136, 627, 1950, 2065, 1939, 2123, 26]
collect(Int32, IdView(p)) = Int32[1063, 1356, 1178, 1610, 1031, 1080, 1181, 475, 984, 357]
collect(Int32, IdView(p)) = Int32[1732, 659, 1052, 1397, 2077, 1763, 1639, 1210, 130, 1958]
collect(Int32, IdView(p)) = Int32[1652, 88, 1150, 317, 812, 1517, 1036, 1960, 681, 1224]
collect(Int32, IdView(p)) = Int32[1600, 1753, 1976, 1667, 1727, 1360, 1271, 2191, 1902, 1105]
collect(Int32, IdView(p)) = Int32[1798, 2120, 942, 1625, 1816, 1988, 504, 1042, 1167, 1395]
collect(Int32, IdView(p)) = Int32[1354, 1418, 323, 1093, 99, 917, 1005, 593, 1421, 892]
collect(Int32, IdView(p)) = Int32[842, 869, 537, 934, 346, 926, 710, 203, 986, 846]
collect(Int32, IdView(p)) = Int32[1578, 1509, 2, 1765, 1522, 290, 601, 403, 1238, 475]
collect(Int32, IdView(p)) = Int32[1148, 1986, 1668, 1839, 1635, 1164, 1628, 1521, 1539, 1667]
collect(Int32, IdView(p)) = Int32[535, 353, 89, 213, 393, 1948, 1547, 549, 304, 1980]
collect(Int32, IdView(p)) = Int32[394, 400, 1463, 1562, 39, 1344, 92, 1351, 1167, 1006]
collect(Int32, IdView(p)) = Int32[1806, 1773, 587, 436, 1532, 2079, 1396, 363, 1983, 1132]
collect(Int32, IdView(p)) = Int32[1662, 605, 261, 730, 1512, 468, 141, 1920, 489, 716]
collect(Int32, IdView(p)) = Int32[714, 1079, 447, 557, 283, 1741, 1367, 1393, 38, 402]
collect(Int32, IdView(p)) = Int32[876, 842, 1897, 556, 346, 563, 561, 938, 721, 909]
collect(Int32, IdView(p)) = Int32[1221, 191, 305, 91, 1160, 79, 15, 1698, 1230, 1352]
collect(Int32, IdView(p)) = Int32[729, 180, 1090, 1141, 1307, 298, 81, 1098, 15, 9]
collect(Int32, IdView(p)) = Int32[1750, 362, 445, 157, 661, 958, 674, 435, 1072, 1152]
collect(Int32, IdView(p)) = Int32[56, 1571, 2110, 1978, 1796, 160, 186, 1783, 219, 846]
collect(Int32, IdView(p)) = Int32[1463, 2217, 2127, 480, 849, 1344, 153, 418, 41, 39]
collect(Int32, IdView(p)) = Int32[537, 634, 498, 1132, 926, 1206, 1463, 842, 2168, 1084]
collect(Int32, IdView(p)) = Int32[2113, 2034, 2138, 900, 846, 2098, 1870, 659, 1899, 1796]
collect(Int32, IdView(p)) = Int32[400, 394, 1562, 1463, 1351, 842, 1344, 39, 1006, 1167]
collect(Int32, IdView(p)) = Int32[386, 1474, 276, 621, 1186, 1320, 1131, 364, 1804, 1494]
collect(Int32, IdView(p)) = Int32[277, 1029, 927, 1054, 1318, 1028, 1980, 93, 955, 1680]
collect(Int32, IdView(p)) = Int32[1413, 440, 843, 204, 100, 303, 1252, 166, 12, 117]
collect(Int32, IdView(p)) = Int32[502, 1799, 594, 441, 554, 1675, 426, 997, 1929, 1635]
collect(Int32, IdView(p)) = Int32[125, 1898, 143, 1176, 2122, 322, 1780, 105, 814, 1601]
collect(Int32, IdView(p)) = Int32[2047, 831, 1834, 1173, 1242, 1624, 100, 685, 1492, 1019]
quantile(neighbors_length.(Ref(index.adj), 1:length(index)), 0:0.1:1.0) = [1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 5.0, 6.0, 8.0, 34.0]
     Testing SimilaritySearch tests passed 

Testing completed after 602.85s

PkgEval succeeded after 684.67s