Package evaluation of SimilaritySearch on Julia 1.13.0-DEV.1234 (bbaa34b41d*) started at 2025-10-01T20:29:45.353


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 10.02s


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

Installing SimilaritySearch...
   Resolving package versions...
    Updating `~/.julia/environments/v1.13/Project.toml`
  [053f045d] + SimilaritySearch v0.13.0
    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 v0.10.7
⌅ [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 v0.13.0
  [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 v3.5.2+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 5.26s


################################################################################
# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 117.84s


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

     Testing SimilaritySearch
      Status `/tmp/jl_8pRfo9/Project.toml`
  [7d9f7c33] Accessors v0.1.42
  [4c88cf16] Aqua v0.8.14
  [b4f34e82] Distances v0.10.12
  [c3a54625] JET v0.10.7
⌅ [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 v0.13.0
  [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_8pRfo9/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 v0.10.7
⌅ [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 v0.13.0
  [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 v3.5.2+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  14.8s
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  2.6s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.2s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.524358 seconds (1000 allocations: 78.125 KiB)
  9.016960 seconds (1000 allocations: 78.125 KiB)
  4.088133 seconds (1000 allocations: 78.125 KiB)
  4.163657 seconds (1000 allocations: 78.125 KiB)
  4.047460 seconds (1000 allocations: 78.125 KiB)
  4.204454 seconds (1000 allocations: 78.125 KiB)
  3.992902 seconds (1000 allocations: 78.125 KiB)
  3.938673 seconds (1000 allocations: 78.125 KiB)
 15.570326 seconds (1000 allocations: 78.125 KiB)
 15.492670 seconds (1000 allocations: 78.125 KiB)
 28.237656 seconds (1000 allocations: 78.125 KiB)
 28.776025 seconds (1000 allocations: 78.125 KiB)
 20.481700 seconds (6.23 k allocations: 388.672 KiB)
 20.359308 seconds (1000 allocations: 78.125 KiB)
 17.407037 seconds (1.00 k allocations: 78.141 KiB)
 16.504972 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m38.6s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  2.671444 seconds (1000 allocations: 78.125 KiB)
  2.948401 seconds (1000 allocations: 78.125 KiB)
 18.425651 seconds (1000 allocations: 78.125 KiB)
 18.753263 seconds (1000 allocations: 78.125 KiB)
 28.209586 seconds (1000 allocations: 78.125 KiB)
 27.124651 seconds (1000 allocations: 78.125 KiB)
  4.158568 seconds (1000 allocations: 78.125 KiB)
  4.650283 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  1m50.3s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  8.268049 seconds (1000 allocations: 78.125 KiB)
  8.488115 seconds (1000 allocations: 78.125 KiB)
  8.107579 seconds (1000 allocations: 78.125 KiB)
  8.066119 seconds (1000 allocations: 78.125 KiB)
  8.421255 seconds (1000 allocations: 78.125 KiB)
  8.280166 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total   Time
indexing sets with ExhaustiveSearch | 3000   3000  52.8s
  0.046829 seconds (1.00 k allocations: 78.141 KiB)
  0.046544 seconds (1000 allocations: 78.125 KiB)
  0.040629 seconds (1000 allocations: 78.125 KiB)
  0.041680 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  3.5s
  0.051641 seconds (1000 allocations: 78.125 KiB)
  0.052034 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.4s
ExhaustiveSearch allknn: 4.028705 seconds (2.32 M allocations: 128.850 MiB, 2.17% gc time, 99.96% compilation time)
ParallelExhaustiveSearch allknn: 0.916794 seconds (610.02 k allocations: 31.948 MiB, 99.87% compilation time)
Test Summary: | Pass  Total   Time
allknn        |    5      5  58.9s
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  2.7s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:29.870
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-01T20:42:30.124
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) 2025-10-01T20:42:31.436
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
[ Info: neardup> finished current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:31.836
D.map = UInt32[0x00000001, 0x00000002, 0x0000000d, 0x00000010, 0x00000013, 0x00000016, 0x00000027]
D.nn = Int32[1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 13, 1, 1, 16, 16, 1, 19, 1, 19, 22, 1, 22, 2, 19, 1, 22, 19, 13, 2, 1, 1, 2, 1, 19, 1, 1, 39, 1, 19, 16, 22, 22, 2, 39, 19, 16, 1, 1, 16, 19, 2, 13, 39, 2, 13, 1, 13, 16, 2, 2, 22, 39, 19, 1, 16, 22, 2, 2, 2, 13, 13, 39, 16, 2, 39, 22, 13, 2, 2, 1, 16, 13, 39, 22, 2, 1, 22, 22, 22, 22, 22, 2, 1, 1, 2, 39, 22, 2]
D.dist = Float32[0.0, 0.0, 0.025705397, 0.09928167, 0.046961904, 0.08203614, 0.017566442, 0.05994475, 0.06906146, 0.07785666, 0.09693742, 0.063736975, 0.0, 0.07112551, 0.0925284, 0.0, 0.07034165, 0.043538928, 0.0, 0.09945482, 0.07559878, 0.0, 0.0713768, 0.038290203, 0.053492904, 0.07202476, 0.00731498, 0.058181703, 0.047346175, 0.077836215, 0.009636819, 0.042470872, 0.08955556, 0.015649855, 0.02034992, 0.039551497, 0.047403157, 0.044629097, 0.0, 0.067299426, 0.030478418, 0.06579709, 0.029987395, 0.04917413, 0.02134651, 0.040436983, 0.04139793, 0.025715172, 0.076144576, 0.053366482, 0.032741666, 0.07489163, 0.005818367, 0.049910188, 0.07490158, 0.022665203, 0.018416107, 0.023382366, 0.08127105, 0.054370344, 0.037846267, 0.068838894, 0.03913778, 0.02736473, 0.08950645, 0.034570694, 0.01868987, 0.07068175, 0.015286803, 0.02174598, 0.069211364, 0.05251032, 0.037344575, 0.034237683, 0.09591645, 0.03006655, 0.05225873, 0.028239131, 0.005621493, 0.079380274, 0.03401488, 0.027224302, 0.0497455, 0.049042583, 0.021713376, 0.03639996, 0.0034038424, 0.06812012, 0.028338432, 0.027431846, 0.07046276, 0.06355941, 0.049936235, 0.025069535, 0.05777192, 0.055054724, 0.0778749, 0.08046681, 0.052648842, 0.025028646]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  16.6s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:32.974
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-01T20:42:32.974
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 4, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:32.974
[ Info: neardup> range: 33:48, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:32.974
[ Info: neardup> range: 49:64, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:32.974
[ Info: neardup> range: 65:80, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:32.975
[ Info: neardup> range: 81:96, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:32.975
[ Info: neardup> range: 97:100, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:32.975
[ Info: neardup> finished current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:32.975
D.map = UInt32[0x00000001, 0x00000002, 0x0000000d, 0x00000010, 0x00000013, 0x00000016, 0x00000027]
D.nn = Int32[1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 13, 1, 1, 16, 16, 1, 19, 1, 2, 22, 1, 22, 2, 19, 1, 13, 19, 13, 2, 1, 1, 2, 1, 19, 1, 1, 39, 1, 19, 16, 22, 22, 2, 39, 19, 16, 1, 1, 16, 19, 2, 13, 39, 2, 13, 1, 13, 16, 2, 2, 22, 39, 19, 1, 16, 22, 2, 2, 2, 13, 13, 39, 16, 2, 39, 22, 13, 2, 2, 1, 16, 13, 39, 22, 2, 1, 22, 22, 22, 22, 22, 2, 1, 1, 2, 39, 22, 2]
D.dist = Float32[0.0, 0.0, 0.025705397, 0.09928167, 0.046961904, 0.08203614, 0.017566442, 0.05994475, 0.06906146, 0.07785666, 0.09693742, 0.063736975, 0.0, 0.07112551, 0.0925284, 0.0, 0.07034165, 0.043538928, 0.0, 0.09945482, 0.098143935, 0.0, 0.0713768, 0.038290203, 0.053492904, 0.07202476, 0.00731498, 0.08585745, 0.047346175, 0.077836215, 0.009636819, 0.042470872, 0.08955556, 0.015649855, 0.02034992, 0.039551497, 0.047403157, 0.044629097, 0.0, 0.067299426, 0.030478418, 0.06579709, 0.029987395, 0.04917413, 0.02134651, 0.040436983, 0.04139793, 0.025715172, 0.076144576, 0.053366482, 0.032741666, 0.07489163, 0.005818367, 0.049910188, 0.07490158, 0.022665203, 0.018416107, 0.023382366, 0.08127105, 0.054370344, 0.037846267, 0.068838894, 0.03913778, 0.02736473, 0.08950645, 0.034570694, 0.01868987, 0.07068175, 0.015286803, 0.02174598, 0.069211364, 0.05251032, 0.037344575, 0.034237683, 0.09591645, 0.03006655, 0.05225873, 0.028239131, 0.005621493, 0.079380274, 0.03401488, 0.027224302, 0.0497455, 0.049042583, 0.021713376, 0.03639996, 0.0034038424, 0.06812012, 0.028338432, 0.027431846, 0.07046276, 0.06355941, 0.049936235, 0.025069535, 0.05777192, 0.055054724, 0.0778749, 0.08046681, 0.052648842, 0.025028646]
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: 2025-10-01T20:42:33.067
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-10-01T20:42:33.067
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: 2025-10-01T20:42:33.068
[ Info: neardup> range: 33:48, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:33.068
[ Info: neardup> range: 49:64, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:33.068
[ Info: neardup> range: 65:80, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:33.068
[ Info: neardup> range: 81:96, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:33.069
[ Info: neardup> range: 97:100, current elements: 21, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:33.069
[ Info: neardup> finished current elements: 21, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:33.069
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000013, 0x0000001d, 0x0000004e, 0x0000005a, 0x0000005c]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 15, 1, 19, 5, 8, 8, 14, 8, 8, 8, 7, 13, 29, 10, 2, 15, 14, 2, 5, 19, 10, 5, 4, 11, 19, 8, 8, 8, 2, 15, 29, 16, 11, 5, 3, 29, 2, 6, 4, 8, 13, 1, 10, 11, 9, 8, 2, 2, 29, 12, 16, 19, 2, 8, 6, 10, 13, 2, 4, 9, 15, 78, 13, 8, 78, 1, 11, 10, 4, 2, 2, 10, 8, 90, 8, 92, 19, 2, 11, 14, 3, 15, 92, 8]
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, 0.012044251, 0.043538928, 0.0, 0.083013594, 0.016314507, 0.08164722, 0.020565152, 0.06354189, 0.004380822, 0.0479753, 0.006268978, 0.08585745, 0.0, 0.014586508, 0.009636819, 0.011889696, 0.011449039, 0.015649855, 0.008494198, 0.039551497, 0.031015217, 0.026967049, 0.031789362, 0.006776154, 0.030478418, 0.03796941, 0.0420534, 0.018936753, 0.02134651, 0.023753464, 0.0004194975, 0.025715172, 0.030032873, 0.026122987, 0.016682684, 0.008721709, 0.005818367, 0.04740721, 0.035871863, 0.0137886405, 0.018416107, 0.023382366, 0.005411625, 0.024792373, 0.015006483, 0.010261714, 0.040477335, 0.030088246, 0.029489458, 0.012653291, 0.01868987, 0.07586819, 0.015286803, 0.013032377, 0.0033766031, 0.043470442, 0.037344575, 0.051541865, 0.04581529, 0.01017797, 0.030838251, 0.0, 0.005621493, 0.007887661, 0.032465637, 0.027224302, 0.0036880374, 0.011279583, 0.019023955, 0.060529053, 0.0034038424, 0.02554202, 0.029069126, 0.0, 0.00458163, 0.0, 0.058634758, 0.025069535, 0.011944056, 0.044193685, 0.029064894, 0.02401501, 0.024460137, 0.019359112]
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: 2025-10-01T20:42:39.372
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=4 n=4 2025-10-01T20:42:39.372
[ Info: neardup> range: 17:32, current elements: 4, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:39.378
[ Info: neardup> range: 33:48, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:39.378
[ Info: neardup> range: 49:64, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:39.378
[ Info: neardup> range: 65:80, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:39.378
[ Info: neardup> range: 81:96, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:39.379
[ Info: neardup> range: 97:100, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:39.379
[ Info: neardup> finished current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-10-01T20:42:39.379
D.map = UInt32[0x00000001, 0x00000002, 0x0000000d, 0x00000010, 0x00000013, 0x00000016, 0x00000027]
D.nn = Int32[1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 13, 1, 1, 16, 16, 1, 19, 1, 2, 22, 1, 22, 2, 19, 1, 13, 19, 13, 2, 1, 1, 2, 1, 19, 1, 1, 39, 1, 19, 16, 22, 22, 2, 39, 19, 16, 1, 1, 16, 19, 2, 13, 39, 2, 13, 1, 13, 16, 2, 2, 22, 39, 19, 1, 16, 22, 2, 2, 2, 13, 13, 39, 16, 2, 39, 22, 13, 2, 2, 1, 16, 13, 39, 22, 2, 1, 22, 22, 22, 22, 22, 2, 1, 1, 2, 39, 22, 2]
D.dist = Float32[0.0, 0.0, 0.025705397, 0.09928167, 0.046961904, 0.08203614, 0.017566442, 0.05994475, 0.06906146, 0.07785666, 0.09693742, 0.063736975, 0.0, 0.07112551, 0.0925284, 0.0, 0.07034165, 0.043538928, 0.0, 0.09945482, 0.098143935, 0.0, 0.0713768, 0.038290203, 0.053492904, 0.07202476, 0.00731498, 0.08585745, 0.047346175, 0.077836215, 0.009636819, 0.042470872, 0.08955556, 0.015649855, 0.02034992, 0.039551497, 0.047403157, 0.044629097, 0.0, 0.067299426, 0.030478418, 0.06579709, 0.029987395, 0.04917413, 0.02134651, 0.040436983, 0.04139793, 0.025715172, 0.076144576, 0.053366482, 0.032741666, 0.07489163, 0.005818367, 0.049910188, 0.07490158, 0.022665203, 0.018416107, 0.023382366, 0.08127105, 0.054370344, 0.037846267, 0.068838894, 0.03913778, 0.02736473, 0.08950645, 0.034570694, 0.01868987, 0.07068175, 0.015286803, 0.02174598, 0.069211364, 0.05251032, 0.037344575, 0.034237683, 0.09591645, 0.03006655, 0.05225873, 0.028239131, 0.005621493, 0.079380274, 0.03401488, 0.027224302, 0.0497455, 0.049042583, 0.021713376, 0.03639996, 0.0034038424, 0.06812012, 0.028338432, 0.027431846, 0.07046276, 0.06355941, 0.049936235, 0.025069535, 0.05777192, 0.055054724, 0.0778749, 0.08046681, 0.052648842, 0.025028646]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  6.3s
computing farthest point 1, dmax: Inf, imax: 29, n: 30
computing farthest point 2, dmax: 1.1020223, imax: 19, n: 30
computing farthest point 3, dmax: 0.878896, imax: 17, n: 30
computing farthest point 4, dmax: 0.74582237, imax: 18, n: 30
computing farthest point 5, dmax: 0.6821802, imax: 5, n: 30
computing farthest point 6, dmax: 0.64210385, imax: 13, n: 30
computing farthest point 7, dmax: 0.6137235, imax: 2, n: 30
computing farthest point 8, dmax: 0.5667938, imax: 3, n: 30
computing farthest point 9, dmax: 0.5650844, imax: 11, n: 30
computing farthest point 10, dmax: 0.5483126, imax: 22, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.5s
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) 2025-10-01T20:42:47.629
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=8, Δ=0.95, maxvisits=114) 2025-10-01T20:43:00.805
LOG n.size quantiles:[2.0, 2.0, 3.0, 3.0, 3.0]
(i, j, d) = (3, 635, -1.1920929f-7)
(i, j, d, :parallel) = (3, 635, -1.1920929f-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 => 20.728016583, :exact => 0.99083282)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  22.2s
  6.268739 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.005466 seconds
SEARCH Exhaustive 2: 0.005305 seconds
SEARCH Exhaustive 3: 0.005505 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) 2025-10-01T20:43:31.406
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=1.05, maxvisits=184) 2025-10-01T20:43:37.703
LOG n.size quantiles:[1.0, 2.0, 2.0, 2.0, 3.0]
[ Info: RECALL BAJO!! recall: 0.2967741935483871, #objects: 2219, #queries: 31
[ Info: [0.7027987837791443, 0.5176336169242859, 0.3095497786998749, 0.5713432431221008, 0.47859299182891846, 0.39058569073677063, 0.4919435679912567, 0.35981371998786926, 0.4749203026294708, 0.40266531705856323, 0.6034947633743286, 0.3351098597049713, 0.3880806565284729, 0.5783315300941467, 0.2586209177970886, 0.32079511880874634, 0.5426165461540222, 0.417905330657959, 0.3738495707511902, 0.5166314840316772, 0.4945385158061981, 0.3433776795864105, 0.2534436881542206, 0.39281395077705383, 0.34734413027763367, 0.35370099544525146, 0.3182341456413269, 0.24249646067619324, 0.7523260712623596, 0.2800306975841522, 0.38085615634918213]
(g, r) = (Set(Int32[772, 58, 86, 1666, 206, 1234, 816, 1687, 1806, 2124]), Set(Int32[910, 691, 642, 658, 1744, 1537, 917, 915, 1876, 393]))
(g, r) = (Set(Int32[775, 147, 289, 1403, 34, 274, 630, 424, 1286, 1388]), Set(Int32[281, 1180, 443, 1744, 216, 1737, 2067, 159, 331, 1069]))
(g, r) = (Set(Int32[186, 1130, 1999, 1786, 427, 859, 920, 571, 43, 1023]), Set(Int32[1786, 1597, 427, 920, 571, 43, 2175, 789, 492, 1026]))
(g, r) = (Set(Int32[1781, 1919, 279, 578, 1904, 1177, 1443, 1413, 436, 1803]), Set(Int32[1811, 2218, 1019, 590, 465, 1515, 456, 1516, 133, 975]))
(g, r) = (Set(Int32[1648, 1099, 408, 1899, 734, 864, 1872, 2143, 278, 2066]), Set(Int32[858, 140, 935, 974, 2071, 734, 1034, 864, 278, 1466]))
(g, r) = (Set(Int32[584, 339, 1591, 2031, 1541, 914, 49, 2150, 543, 1499]), Set(Int32[339, 1591, 2031, 1321, 23, 1211, 49, 1112, 1240, 1499]))
(g, r) = (Set(Int32[150, 1420, 600, 359, 2035, 1042, 1133, 725, 1052, 1875]), Set(Int32[872, 2035, 847, 725, 654, 1875, 969, 1469, 1228, 1638]))
(g, r) = (Set(Int32[1209, 1992, 373, 485, 1576, 354, 616, 1531, 210, 1203]), Set(Int32[27, 257, 340, 724, 226, 894, 485, 1358, 354, 36]))
(g, r) = (Set(Int32[1207, 1734, 872, 1991, 215, 1582, 489, 1244, 1002, 122]), Set(Int32[1469, 937, 872, 2035, 725, 1638, 969, 1577, 1339, 1875]))
(g, r) = (Set(Int32[474, 797, 942, 1768, 352, 121, 1601, 1303, 1354, 538]), Set(Int32[797, 2194, 1645, 1887, 447, 1768, 1106, 2207, 1299, 1871]))
(g, r) = (Set(Int32[1045, 642, 464, 291, 1981, 1687, 1003, 1512, 45, 127]), Set(Int32[910, 2216, 750, 929, 351, 1876, 1547, 1906, 1636, 809]))
(g, r) = (Set(Int32[202, 2212, 2117, 816, 162, 1193, 1502, 1694, 1308, 31]), Set(Int32[202, 608, 1744, 1168, 1187, 599, 162, 1193, 191, 31]))
(g, r) = (Set(Int32[1997, 2219, 2161, 532, 109, 1168, 241, 951, 2112, 2199]), Set(Int32[2219, 2161, 109, 532, 1168, 241, 1307, 2112, 2199, 832]))
(g, r) = (Set(Int32[1616, 1730, 636, 125, 129, 789, 1090, 1828, 1602, 1363]), Set(Int32[740, 155, 238, 432, 94, 148, 453, 161, 2146, 807]))
(g, r) = (Set(Int32[1289, 2081, 1622, 1894, 1911, 519, 1854, 44, 888, 1055]), Set(Int32[1289, 2081, 1622, 525, 519, 1854, 466, 44, 128, 652]))
(g, r) = (Set(Int32[1567, 2112, 668, 510, 1629, 1033, 1723, 709, 41, 753]), Set(Int32[2199, 1151, 519, 241, 1154, 466, 2112, 1898, 1649, 832]))
(g, r) = (Set(Int32[989, 576, 743, 2062, 1008, 1065, 1855, 595, 564, 10]), Set(Int32[1181, 743, 2062, 1814, 624, 1416, 185, 1161, 469, 2136]))
(g, r) = (Set(Int32[1674, 611, 286, 1943, 250, 2026, 2123, 1989, 323, 2157]), Set(Int32[151, 1425, 286, 533, 250, 1200, 1226, 323, 11, 1693]))
(g, r) = (Set(Int32[1345, 27, 909, 1677, 226, 1484, 1414, 1655, 265, 1431]), Set(Int32[1917, 446, 1677, 374, 1484, 1414, 1576, 1655, 1203, 1117]))
(g, r) = (Set(Int32[141, 1885, 211, 545, 521, 1335, 1558, 1451, 334, 2090]), Set(Int32[2110, 2137, 2115, 1595, 1250, 1770, 1558, 2090, 1954, 1885]))
(g, r) = (Set(Int32[1289, 391, 384, 712, 905, 1360, 1090, 1136, 888, 739]), Set(Int32[197, 391, 384, 5, 905, 146, 620, 36, 888, 739]))
(g, r) = (Set(Int32[699, 1109, 1323, 105, 1554, 2074, 2013, 1436, 2213, 1679]), Set(Int32[1403, 1101, 763, 729, 162, 2074, 630, 1286, 34, 1318]))
(g, r) = (Set(Int32[339, 1591, 881, 1499, 261, 1541, 1586, 1308, 1841, 1252]), Set(Int32[339, 1591, 31, 2031, 261, 1586, 109, 16, 1455, 1499]))
(g, r) = (Set(Int32[1811, 1996, 289, 1643, 465, 1515, 456, 1516, 975, 957]), Set(Int32[1811, 691, 465, 1515, 456, 2093, 1516, 975, 133, 1658]))
(g, r) = (Set(Int32[1047, 2193, 1729, 669, 1550, 812, 1934, 1272, 1296, 1761]), Set(Int32[1047, 2193, 669, 973, 812, 316, 1272, 330, 369, 1761]))
(g, r) = (Set(Int32[446, 1099, 963, 364, 1393, 1328, 212, 226, 1938, 1648]), Set(Int32[1099, 963, 1263, 935, 974, 1393, 734, 864, 278, 1466]))
(g, r) = (Set(Int32[994, 1833, 475, 1553, 588, 1325, 1586, 63, 1138, 399]), Set(Int32[745, 475, 932, 304, 397, 1270, 16, 114, 109, 31]))
(g, r) = (Set(Int32[393, 1537, 827, 1030, 129, 1165, 461, 1965, 1580, 1463]), Set(Int32[481, 393, 1537, 827, 642, 509, 1756, 461, 1965, 516]))
(g, r) = (Set(Int32[1096, 415, 2017, 495, 1224, 1797, 1010, 2210, 1705, 1116]), Set(Int32[1097, 1291, 1727, 687, 477, 385, 790, 789, 1253, 1160]))
(g, r) = (Set(Int32[1291, 1597, 427, 1030, 2130, 43, 1910, 1580, 492, 1036]), Set(Int32[1533, 412, 1597, 427, 2130, 43, 243, 163, 492, 221]))
(g, r) = (Set(Int32[24, 1266, 1856, 586, 2020, 1775, 2184, 1993, 818, 2122]), Set(Int32[24, 1103, 1856, 283, 1126, 1462, 180, 438, 51, 2122]))
collect(Int32, IdView(p)) = Int32[642, 658, 910, 1876, 1744, 691, 917, 1537, 915, 393]
collect(Int32, IdView(p)) = Int32[331, 1180, 1737, 2067, 1069, 159, 281, 1744, 443, 216]
collect(Int32, IdView(p)) = Int32[427, 1786, 920, 571, 43, 2175, 789, 492, 1026, 1597]
collect(Int32, IdView(p)) = Int32[1811, 590, 465, 456, 1515, 2218, 133, 1516, 1019, 975]
collect(Int32, IdView(p)) = Int32[734, 864, 278, 1034, 974, 1466, 935, 2071, 140, 858]
collect(Int32, IdView(p)) = Int32[1499, 339, 49, 1591, 2031, 1321, 1112, 1240, 23, 1211]
collect(Int32, IdView(p)) = Int32[2035, 1875, 725, 969, 1469, 654, 872, 1228, 1638, 847]
collect(Int32, IdView(p)) = Int32[354, 485, 340, 724, 27, 257, 894, 1358, 226, 36]
collect(Int32, IdView(p)) = Int32[872, 725, 969, 2035, 1638, 1577, 937, 1339, 1875, 1469]
collect(Int32, IdView(p)) = Int32[797, 1768, 2194, 1887, 1645, 447, 1106, 2207, 1871, 1299]
collect(Int32, IdView(p)) = Int32[351, 1876, 929, 910, 1547, 2216, 809, 1906, 1636, 750]
collect(Int32, IdView(p)) = Int32[202, 162, 31, 1193, 1187, 599, 1744, 191, 608, 1168]
collect(Int32, IdView(p)) = Int32[2199, 2112, 241, 532, 2219, 2161, 1168, 109, 1307, 832]
collect(Int32, IdView(p)) = Int32[155, 432, 94, 148, 807, 740, 161, 453, 238, 2146]
collect(Int32, IdView(p)) = Int32[519, 1289, 2081, 44, 1622, 1854, 525, 466, 652, 128]
collect(Int32, IdView(p)) = Int32[2112, 1151, 466, 519, 241, 1898, 1649, 832, 2199, 1154]
collect(Int32, IdView(p)) = Int32[2062, 743, 1181, 1416, 624, 1814, 469, 1161, 2136, 185]
collect(Int32, IdView(p)) = Int32[286, 250, 323, 1200, 1693, 151, 533, 11, 1425, 1226]
collect(Int32, IdView(p)) = Int32[1655, 1414, 1677, 1484, 1203, 1917, 1576, 446, 1117, 374]
collect(Int32, IdView(p)) = Int32[1558, 2090, 1885, 2110, 2115, 1770, 2137, 1595, 1250, 1954]
collect(Int32, IdView(p)) = Int32[905, 391, 384, 888, 739, 197, 36, 146, 620, 5]
collect(Int32, IdView(p)) = Int32[2074, 763, 1286, 630, 729, 1318, 1403, 162, 1101, 34]
collect(Int32, IdView(p)) = Int32[1591, 261, 339, 1586, 1499, 31, 2031, 16, 109, 1455]
collect(Int32, IdView(p)) = Int32[456, 1811, 975, 1515, 465, 1516, 691, 1658, 133, 2093]
collect(Int32, IdView(p)) = Int32[1272, 1761, 669, 2193, 812, 1047, 330, 369, 973, 316]
collect(Int32, IdView(p)) = Int32[1099, 1393, 963, 864, 974, 1263, 278, 935, 1466, 734]
collect(Int32, IdView(p)) = Int32[475, 932, 304, 397, 31, 1270, 16, 114, 745, 109]
collect(Int32, IdView(p)) = Int32[393, 1537, 1965, 827, 461, 642, 509, 1756, 516, 481]
collect(Int32, IdView(p)) = Int32[1291, 790, 1160, 1097, 789, 1727, 687, 385, 1253, 477]
collect(Int32, IdView(p)) = Int32[1597, 43, 427, 492, 2130, 221, 412, 243, 1533, 163]
collect(Int32, IdView(p)) = Int32[24, 2122, 1856, 180, 1103, 51, 283, 1126, 1462, 438]
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, 29.0]
     Testing SimilaritySearch tests passed 

Testing completed after 694.34s

PkgEval succeeded after 840.57s