Package evaluation to test LaplaceRedux on Julia 1.11.8 (29b3528cce*) started at 2026-01-20T14:58:26.842 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.11` Set-up completed after 8.77s ################################################################################ # Installation # Installing LaplaceRedux... Resolving package versions... Updating `~/.julia/environments/v1.11/Project.toml` [c52c1a26] + LaplaceRedux v1.2.1 Updating `~/.julia/environments/v1.11/Manifest.toml` [621f4979] + AbstractFFTs v1.5.0 [7d9f7c33] + Accessors v0.1.43 [79e6a3ab] + Adapt v4.4.0 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [a9b6321e] + Atomix v1.1.2 [198e06fe] + BangBang v0.4.6 [9718e550] + Baselet v0.1.1 [fa961155] + CEnum v0.5.0 [324d7699] + CategoricalArrays v1.0.2 [af321ab8] + CategoricalDistributions v0.2.1 [082447d4] + ChainRules v1.72.6 [d360d2e6] + ChainRulesCore v1.26.0 [3da002f7] + ColorTypes v0.12.1 [bbf7d656] + CommonSubexpressions v0.3.1 [34da2185] + Compat v4.18.1 [a33af91c] + CompositionsBase v0.1.2 [ed09eef8] + ComputationalResources v0.3.2 [187b0558] + ConstructionBase v1.6.0 [6add18c4] + ContextVariablesX v0.1.3 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.19.3 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [8bb1440f] + DelimitedFiles v1.9.1 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.15.1 [31c24e10] + Distributions v0.25.123 [ffbed154] + DocStringExtensions v0.9.5 [cc61a311] + FLoops v0.2.2 [b9860ae5] + FLoopsBase v0.1.1 [1a297f60] + FillArrays v1.16.0 [53c48c17] + FixedPointNumbers v0.8.5 ⌅ [587475ba] + Flux v0.14.25 [f6369f11] + ForwardDiff v1.3.1 ⌅ [d9f16b24] + Functors v0.4.12 [0c68f7d7] + GPUArrays v11.3.4 [46192b85] + GPUArraysCore v0.2.0 [076d061b] + HashArrayMappedTries v0.2.0 [34004b35] + HypergeometricFunctions v0.3.28 [7869d1d1] + IRTools v0.4.15 [22cec73e] + InitialValues v0.3.1 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.6 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.7.1 [b14d175d] + JuliaVariables v0.2.4 [63c18a36] + KernelAbstractions v0.9.39 [929cbde3] + LLVM v9.4.4 [b964fa9f] + LaTeXStrings v1.4.0 [c52c1a26] + LaplaceRedux v1.2.1 [92ad9a40] + LearnAPI v2.0.1 [2ab3a3ac] + LogExpFunctions v0.3.29 [c2834f40] + MLCore v1.0.0 ⌃ [7e8f7934] + MLDataDevices v1.5.3 [a7f614a8] + MLJBase v1.12.1 [e80e1ace] + MLJModelInterface v1.12.1 [d8e11817] + MLStyle v0.4.17 [f1d291b0] + MLUtils v0.4.8 [1914dd2f] + MacroTools v0.5.16 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [872c559c] + NNlib v0.9.33 [77ba4419] + NaNMath v1.1.3 [71a1bf82] + NameResolution v0.1.5 [0b1bfda6] + OneHotArrays v0.2.10 ⌅ [3bd65402] + Optimisers v0.3.4 [bac558e1] + OrderedCollections v1.8.1 [90014a1f] + PDMats v0.11.37 [d96e819e] + Parameters v0.12.3 ⌅ [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.5.1 [8162dcfd] + PrettyPrint v0.2.0 [08abe8d2] + PrettyTables v3.1.2 [33c8b6b6] + ProgressLogging v0.1.6 [92933f4c] + ProgressMeter v1.11.0 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [c1ae055f] + RealDot v0.1.0 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [79098fc4] + Rmath v0.9.0 [321657f4] + ScientificTypes v3.1.2 [30f210dd] + ScientificTypesBase v3.0.0 [7e506255] + ScopedValues v1.5.0 [efcf1570] + Setfield v1.1.2 [605ecd9f] + ShowCases v0.1.0 [699a6c99] + SimpleTraits v0.9.5 [a2af1166] + SortingAlgorithms v1.2.2 [dc90abb0] + SparseInverseSubset v0.1.2 [276daf66] + SpecialFunctions v2.6.1 [171d559e] + SplittablesBase v0.1.15 [90137ffa] + StaticArrays v1.9.16 [1e83bf80] + StaticArraysCore v1.4.4 [c062fc1d] + StatisticalMeasuresBase v0.1.3 [64bff920] + StatisticalTraits v3.5.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.8.0 [2913bbd2] + StatsBase v0.34.10 [4c63d2b9] + StatsFuns v1.5.2 [892a3eda] + StringManipulation v0.4.2 [09ab397b] + StructArrays v0.7.2 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [28d57a85] + Transducers v0.4.85 [bc48ee85] + Tullio v0.3.8 [3a884ed6] + UnPack v1.0.2 [013be700] + UnsafeAtomics v0.3.0 ⌅ [e88e6eb3] + Zygote v0.6.77 [700de1a5] + ZygoteRules v0.2.7 [dad2f222] + LLVMExtra_jll v0.0.38+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [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 v1.6.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [4af54fe1] + LazyArtifacts v1.11.0 [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.11.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.2.0 [44cfe95a] + Pkg v1.11.0 [de0858da] + Printf v1.11.0 [3fa0cd96] + REPL 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 v1.11.0 [f489334b] + StyledStrings v1.11.0 [4607b0f0] + SuiteSparse [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 v1.1.1+0 [deac9b47] + LibCURL_jll v8.6.0+0 [e37daf67] + LibGit2_jll v1.7.2+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.6+0 [14a3606d] + MozillaCACerts_jll v2023.12.12 [4536629a] + OpenBLAS_jll v0.3.27+1 [05823500] + OpenLibm_jll v0.8.5+0 [bea87d4a] + SuiteSparse_jll v7.7.0+0 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.59.0+0 [3f19e933] + p7zip_jll v17.4.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Installation completed after 6.98s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompiling project... 82768.7 ms ✓ LaplaceRedux 1 dependency successfully precompiled in 95 seconds. 352 already precompiled. Precompilation completed after 115.21s ################################################################################ # Testing # Testing LaplaceRedux Status `/tmp/jl_zKoY4J/Project.toml` [4c88cf16] Aqua v0.8.14 [336ed68f] CSV v0.10.15 [af321ab8] CategoricalDistributions v0.2.1 [a93c6f00] DataFrames v1.8.1 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.123 ⌅ [587475ba] Flux v0.14.25 [682c06a0] JSON v1.4.0 [c52c1a26] LaplaceRedux v1.2.1 [a7f614a8] MLJBase v1.12.1 [e80e1ace] MLJModelInterface v1.12.1 [72560011] MLJTestInterface v0.2.9 [f1d291b0] MLUtils v0.4.8 [91a5bcdd] Plots v1.41.4 [860ef19b] StableRNGs v1.0.4 [a19d573c] StatisticalMeasures v0.3.3 [10745b16] Statistics v1.11.1 [592b5752] Trapz v2.0.3 [bc48ee85] Tullio v0.3.8 ⌅ [e88e6eb3] Zygote v0.6.77 [37e2e46d] LinearAlgebra v1.11.0 [9a3f8284] Random v1.11.0 [9e88b42a] Serialization v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_zKoY4J/Manifest.toml` [621f4979] AbstractFFTs v1.5.0 [7d9f7c33] Accessors v0.1.43 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libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.59.0+0 [3f19e933] p7zip_jll v17.4.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... Training networks. Running workflows. [ Info: Running workflow for: (regression, 0, GGN, all) [ Info: Running workflow for: (regression, 0, EmpiricalFisher, all) [ Info: Running workflow for: (regression, 32, GGN, all) [ Info: Running workflow for: (regression, 32, EmpiricalFisher, all) [ Info: Running workflow for: (regression, 0, GGN, last_layer) [ Info: Running workflow for: (regression, 0, EmpiricalFisher, last_layer) [ Info: Running workflow for: (regression, 32, GGN, last_layer) [ Info: Running workflow for: (regression, 32, EmpiricalFisher, last_layer) [ Info: Running workflow for: (regression, 0, GGN, subnetwork) [ Info: Running workflow for: (regression, 0, EmpiricalFisher, subnetwork) [ Info: Running workflow for: (regression, 32, GGN, subnetwork) [ Info: Running workflow for: (regression, 32, EmpiricalFisher, subnetwork) [ Info: Running workflow for: (classification_multi, 0, GGN, all) [ Info: Running workflow for: (classification_multi, 0, EmpiricalFisher, all) [ Info: Running workflow for: (classification_multi, 32, GGN, all) [ Info: Running workflow for: (classification_multi, 32, EmpiricalFisher, all) [ Info: Running workflow for: (classification_multi, 0, GGN, last_layer) [ Info: Running workflow for: (classification_multi, 0, EmpiricalFisher, last_layer) [ Info: Running workflow for: (classification_multi, 32, GGN, last_layer) [ Info: Running workflow for: (classification_multi, 32, EmpiricalFisher, last_layer) [ Info: Running workflow for: (classification_multi, 0, GGN, subnetwork) [ Info: Running workflow for: (classification_multi, 0, EmpiricalFisher, subnetwork) [ Info: Running workflow for: (classification_multi, 32, GGN, subnetwork) [ Info: Running workflow for: (classification_multi, 32, EmpiricalFisher, subnetwork) [ Info: Running workflow for: (classification_binary, 0, GGN, all) [ Info: Running workflow for: (classification_binary, 0, EmpiricalFisher, all) [ Info: Running workflow for: (classification_binary, 32, GGN, all) [ Info: Running workflow for: (classification_binary, 32, EmpiricalFisher, all) [ Info: Running workflow for: (classification_binary, 0, GGN, last_layer) [ Info: Running workflow for: (classification_binary, 0, EmpiricalFisher, last_layer) [ Info: Running workflow for: (classification_binary, 32, GGN, last_layer) [ Info: Running workflow for: (classification_binary, 32, EmpiricalFisher, last_layer) [ Info: Running workflow for: (classification_binary, 0, GGN, subnetwork) [ Info: Running workflow for: (classification_binary, 0, EmpiricalFisher, subnetwork) [ Info: Running workflow for: (classification_binary, 32, GGN, subnetwork) [ Info: Running workflow for: (classification_binary, 32, EmpiricalFisher, subnetwork) [ Info: Running workflow for: (regression, GGN, all) [ Info: Running workflow for: (classification_multi, GGN, all) [ Info: Running workflow for: (classification_binary, GGN, all) [ Info: Running workflow for: (regression, EmpiricalFisher, all) [ Info: Running workflow for: (classification_multi, EmpiricalFisher, all) [ Info: Running workflow for: (classification_binary, EmpiricalFisher, all) [ Info: Running workflow for: (regression, GGN, last_layer) [ Info: Running workflow for: (classification_multi, GGN, last_layer) [ Info: Running workflow for: (classification_binary, GGN, last_layer) [ Info: Running workflow for: (regression, EmpiricalFisher, last_layer) [ Info: Running workflow for: (classification_multi, EmpiricalFisher, last_layer) [ Info: Running workflow for: (classification_binary, EmpiricalFisher, last_layer) [ Info: Running workflow for: (regression, GGN, subnetwork) [ Info: Running workflow for: (classification_multi, GGN, subnetwork) [ Info: Running workflow for: (classification_binary, GGN, subnetwork) [ Info: Running workflow for: (regression, EmpiricalFisher, subnetwork) [ Info: Running workflow for: (classification_multi, EmpiricalFisher, subnetwork) [ Info: Running workflow for: (classification_binary, EmpiricalFisher, subnetwork) WARNING: using Distributions.Laplace in module Main conflicts with an existing identifier. [ Info: testing sharpness_regression with distributions [ Info: testing empirical_frequency_regression with distributions [ Info: testing sharpness_classification with distributions [ Info: testing empirical_frequency_classification with distributions [ Info: testing sigma scaling technique Precompiling DataFrames... 90750.0 ms ✓ DataFrames 1 dependency successfully precompiled in 92 seconds. 34 already precompiled. Precompiling BangBangDataFramesExt... 6954.6 ms ✓ BangBang → BangBangDataFramesExt 1 dependency successfully precompiled in 9 seconds. 47 already precompiled. Precompiling TransducersDataFramesExt... 5165.6 ms ✓ Transducers → TransducersDataFramesExt 1 dependency successfully precompiled in 6 seconds. 62 already precompiled. ┌ Warning: Layer with Float32 parameters got Float64 input. │ The input will be converted, but any earlier layers may be very slow. │ layer = Dense(2 => 3, σ) # 9 parameters │ summary(x) = "2-element Vector{Float64}" └ @ Flux ~/.julia/packages/Flux/vwk6M/src/layers/stateless.jl:59 Precompiling StatisticalMeasures... 47129.0 ms ✓ StatisticalMeasures 11151.9 ms ✓ StatisticalMeasures → ScientificTypesExt 2 dependencies successfully precompiled in 63 seconds. 140 already precompiled. Precompiling DefaultMeasuresExt... 11422.0 ms ✓ MLJBase → DefaultMeasuresExt 1 dependency successfully precompiled in 15 seconds. 151 already precompiled. Precompiling MLJTestInterface... 19138.2 ms ✓ MLJTestInterface 1 dependency successfully precompiled in 23 seconds. 165 already precompiled. [ Info: testing interface for LaplaceRegressor The number of epochs inserted is lower than the number of epochs already been trained. No update is necessary updating only the laplace optimization part [ Info: Training machine(LaplaceRegressor(model = nothing, …), …). ┌ Warning: Warning: no Flux model has been provided in the model. LaplaceRedux will use a standard MLP with 2 hidden layers with 20 neurons each and input and output layers compatible with the dataset. └ @ LaplaceRedux ~/.julia/packages/LaplaceRedux/fXmos/src/direct_mlj.jl:161 [ Info: Iteration 10: P₀=1.053476433290318, σ=1.0957901517780557 loss(exp.(logP₀), exp.(logσ)) = 267.92214254045825 Log likelihood: -189.36867770281168 Log det ratio: 106.35821065142454 Scatter: 50.74871902386853 [ Info: Iteration 20: P₀=1.0414678217909514, σ=1.0820808179291947 loss(exp.(logP₀), exp.(logσ)) = 267.8298831387939 Log likelihood: -189.288049355429 Log det ratio: 106.91343481136258 Scatter: 50.17023275536726 [ Info: Iteration 30: P₀=1.030567826607429, σ=1.0736187225343918 loss(exp.(logP₀), exp.(logσ)) = 267.79241378122987 Log likelihood: -189.25746868349646 Log det ratio: 107.42473875701369 Scatter: 49.645151438453105 [ Info: Iteration 40: P₀=1.0243988754938445, σ=1.0661946934945148 loss(exp.(logP₀), exp.(logσ)) = 267.7758957129878 Log likelihood: -189.24326945459646 Log det ratio: 107.71727561099328 Scatter: 49.347976905789395 [ Info: Iteration 50: P₀=1.021103427653353, σ=1.0596879556251826 loss(exp.(logP₀), exp.(logσ)) = 267.77274012553926 Log likelihood: -189.24087763536943 Log det ratio: 107.8744984283598 Scatter: 49.1892265519798 [ Info: Iteration 60: P₀=1.0182168924277746, σ=1.0585789159752217 loss(exp.(logP₀), exp.(logσ)) = 267.77289670563937 Log likelihood: -189.24142900558886 Log det ratio: 108.01276081025941 Scatter: 49.05017458984159 [ Info: Iteration 70: P₀=1.015404531162447, σ=1.0619896047414867 loss(exp.(logP₀), exp.(logσ)) = 267.7719615725929 Log likelihood: -189.24063059967062 Log det ratio: 108.14796616420182 Scatter: 48.91469578164277 [ Info: Iteration 80: P₀=1.0140852937801244, σ=1.0619226887374995 loss(exp.(logP₀), exp.(logσ)) = 267.77197236475405 Log likelihood: -189.24062077948918 Log det ratio: 108.21155850889843 Scatter: 48.85114466163131 [ Info: Iteration 90: P₀=1.0148358908900912, σ=1.0607044378750432 loss(exp.(logP₀), exp.(logσ)) = 267.771952927917 Log likelihood: -189.24061972337694 Log det ratio: 108.17536351872143 Scatter: 48.88730289035871 [ Info: Iteration 100: P₀=1.0156679629548613, σ=1.0616582158260301 loss(exp.(logP₀), exp.(logσ)) = 267.7719252528881 Log likelihood: -189.24059190109577 Log det ratio: 108.13528072252902 Scatter: 48.927385981055615 [ Info: Updating machine(LaplaceRegressor(model = Chain(Dense(4 => 20, relu), Dense(20 => 20, relu), Dense(20 => 20, relu), Dense(20 => 1)), …), …). Epoch 100: Loss: 110.19311485488993 [ Info: Iteration 10: P₀=0.951752652082614, σ=0.903706505262829 loss(exp.(logP₀), exp.(logσ)) = 369.82642810756647 Log likelihood: -171.79956470300846 Log det ratio: 314.5555538612126 Scatter: 81.4981729479034 [ Info: Iteration 20: P₀=0.9657259271513703, σ=0.9154591672866268 loss(exp.(logP₀), exp.(logσ)) = 369.7442284657263 Log likelihood: -171.74076142219923 Log det ratio: 313.3122355652947 Scatter: 82.69469852175942 [ Info: Iteration 30: P₀=0.9798781583355601, σ=0.9199880044853417 loss(exp.(logP₀), exp.(logσ)) = 369.71971701463804 Log likelihood: -171.72989470168028 Log det ratio: 312.07309659459713 Scatter: 83.90654803131842 [ Info: Iteration 40: P₀=0.9866814446911637, σ=0.9204925057334046 loss(exp.(logP₀), exp.(logσ)) = 369.71585328749217 Log likelihood: -171.72907808793747 Log det ratio: 311.484439863962 Scatter: 84.48911053514738 [ Info: Iteration 50: P₀=0.9886160759538908, σ=0.9211320482415273 loss(exp.(logP₀), exp.(logσ)) = 369.71447375120783 Log likelihood: -171.728155229415 Log det ratio: 311.3178648600098 Scatter: 84.65477218357582 [ Info: Iteration 60: P₀=0.989267129717716, σ=0.923688771047978 loss(exp.(logP₀), exp.(logσ)) = 369.7119176568201 Log likelihood: -171.72571227374166 Log det ratio: 311.26188912695676 Scatter: 84.71052163920015 [ Info: Iteration 70: P₀=0.9903748184092751, σ=0.9264414530753183 loss(exp.(logP₀), exp.(logσ)) = 369.71134711421684 Log likelihood: -171.72528742203434 Log det ratio: 311.1667468357825 Scatter: 84.80537254858251 [ Info: Iteration 80: P₀=0.9920412832063279, σ=0.9264166874405426 loss(exp.(logP₀), exp.(logσ)) = 369.71123245137005 Log likelihood: -171.72528114685437 Log det ratio: 311.02383139189806 Scatter: 84.94807121713325 [ Info: Iteration 90: P₀=0.9931559441169273, σ=0.9251143154296092 loss(exp.(logP₀), exp.(logσ)) = 369.71116172421046 Log likelihood: -171.72520882146335 Log det ratio: 310.9283866508411 Scatter: 85.04351915465307 [ Info: Iteration 100: P₀=0.9929482026293633, σ=0.9255350181053382 loss(exp.(logP₀), exp.(logσ)) = 369.71112490729513 Log likelihood: -171.72517679764096 Log det ratio: 310.9461658794749 Scatter: 85.02573033983347 ┌ Warning: Warning: no Flux model has been provided in the model. LaplaceRedux will use a standard MLP with 2 hidden layers with 20 neurons each and input and output layers compatible with the dataset. └ @ LaplaceRedux ~/.julia/packages/LaplaceRedux/fXmos/src/direct_mlj.jl:161 [ Info: testing interface for LaplaceClassifier The number of epochs inserted is lower than the number of epochs already been trained. No update is necessary updating only the laplace optimization part Test Summary: | Pass Total Time LaplaceRedux.jl | 143 143 29m21.1s Testing LaplaceRedux tests passed Testing completed after 1783.51s PkgEval succeeded after 1932.17s