Package evaluation to test LaplaceRedux on Julia 1.12.4 (0f21d93eaa*) started at 2026-01-27T07:28:41.509 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.12` Set-up completed after 8.09s ################################################################################ # Installation # Installing LaplaceRedux... Resolving package versions... Updating `~/.julia/environments/v1.12/Project.toml` [c52c1a26] + LaplaceRedux v1.2.2 Updating `~/.julia/environments/v1.12/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 v0.4.7 [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 v1.2.2 [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 v1.3.3 [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] + 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v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] + LibCURL_jll v8.15.0+0 [e37daf67] + LibGit2_jll v1.9.0+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.11.4 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.4+0 [bea87d4a] + SuiteSparse_jll v7.8.3+2 [83775a58] + Zlib_jll v1.3.1+2 [8e850b90] + libblastrampoline_jll v5.15.0+0 [8e850ede] + nghttp2_jll v1.64.0+1 [3f19e933] + p7zip_jll v17.7.0+0 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 7.51s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompiling packages... 188228.4 ms ✓ LaplaceRedux 1 dependency successfully precompiled in 204 seconds. 353 already precompiled. Precompilation completed after 220.22s ################################################################################ # Testing # Testing LaplaceRedux Status `/tmp/jl_VACvQj/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 v1.2.2 [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 v0.3.4 [10745b16] Statistics v1.11.1 [592b5752] Trapz v2.0.3 [bc48ee85] Tullio v0.3.8 ⌅ [e88e6eb3] Zygote v0.6.77 [37e2e46d] LinearAlgebra v1.12.0 [9a3f8284] Random v1.11.0 [9e88b42a] Serialization v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_VACvQj/Manifest.toml` [621f4979] AbstractFFTs v1.5.0 [7d9f7c33] Accessors v0.1.43 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v7.8.3+2 [83775a58] Zlib_jll v1.3.1+2 [8e850b90] libblastrampoline_jll v5.15.0+0 [8e850ede] nghttp2_jll v1.64.0+1 [3f19e933] p7zip_jll v17.7.0+0 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) [ 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 packages... 4896.9 ms ✓ CategoricalArrays → CategoricalArraysSentinelArraysExt 1 dependency successfully precompiled in 5 seconds. 14 already precompiled. Precompiling packages... 91734.7 ms ✓ DataFrames 1 dependency successfully precompiled in 93 seconds. 35 already precompiled. Precompiling packages... 8125.1 ms ✓ BangBang → BangBangDataFramesExt 1 dependency successfully precompiled in 9 seconds. 48 already precompiled. Precompiling packages... 8229.1 ms ✓ Transducers → TransducersDataFramesExt 1 dependency successfully precompiled in 10 seconds. 63 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 packages... 50552.3 ms ✓ StatisticalMeasures 13338.0 ms ✓ StatisticalMeasures → ScientificTypesExt 2 dependencies successfully precompiled in 70 seconds. 141 already precompiled. Precompiling packages... 14123.3 ms ✓ MLJBase → DefaultMeasuresExt 1 dependency successfully precompiled in 18 seconds. 152 already precompiled. Precompiling packages... 21253.9 ms ✓ MLJTestInterface 1 dependency successfully precompiled in 25 seconds. 166 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/FWHkB/src/direct_mlj.jl:161 [ Info: Iteration 10: P₀=1.0518629558924644, σ=0.6439921061303262 loss(exp.(logP₀), exp.(logσ)) = 218.3213360052978 Log likelihood: -137.08420878336202 Log det ratio: 111.55870545793346 Scatter: 50.915548985938145 [ Info: Iteration 20: P₀=1.0407886527374277, σ=0.742009363333431 loss(exp.(logP₀), exp.(logσ)) = 217.74906461709782 Log likelihood: -136.5181694992114 Log det ratio: 112.08229418488361 Scatter: 50.379496050889216 [ Info: Iteration 30: P₀=1.0335884800186388, σ=0.68120318772498 loss(exp.(logP₀), exp.(logσ)) = 217.44942566943513 Log likelihood: -136.22063618583226 Log det ratio: 112.4266081160272 Scatter: 50.03097085117851 [ Info: Iteration 40: P₀=1.0271886317181804, σ=0.7091995331204428 loss(exp.(logP₀), exp.(logσ)) = 217.3619720444717 Log likelihood: -136.13374375053723 Log det ratio: 112.73527113964545 Scatter: 49.72118544822353 [ Info: Iteration 50: P₀=1.023584059434537, σ=0.7015072846729302 loss(exp.(logP₀), exp.(logσ)) = 217.34430444599315 Log likelihood: -136.1158425294327 Log det ratio: 112.9102181264388 Scatter: 49.546705706682026 [ Info: Iteration 60: P₀=1.0250778661208164, σ=0.6962142674076651 loss(exp.(logP₀), exp.(logσ)) = 217.34956665378445 Log likelihood: -136.1212499151925 Log det ratio: 112.83761988373327 Scatter: 49.61901359345064 [ Info: Iteration 70: P₀=1.0285588864435626, σ=0.7053162431550662 loss(exp.(logP₀), exp.(logσ)) = 217.34921790201395 Log likelihood: -136.12097412965622 Log det ratio: 112.66897475679187 Scatter: 49.78751278792361 [ Info: Iteration 80: P₀=1.0281250826681734, σ=0.6989365158441414 loss(exp.(logP₀), exp.(logσ)) = 217.34485243353748 Log likelihood: -136.11661975593177 Log det ratio: 112.68995088960003 Scatter: 49.76651446561138 [ Info: Iteration 90: P₀=1.0268544176464125, σ=0.7003006421211585 loss(exp.(logP₀), exp.(logσ)) = 217.34400985817157 Log likelihood: -136.1157766450999 Log det ratio: 112.75145864901998 Scatter: 49.70500777712335 [ Info: Iteration 100: P₀=1.0278029006221778, σ=0.7017752481631946 loss(exp.(logP₀), exp.(logσ)) = 217.344188005856 Log likelihood: -136.11595985586507 Log det ratio: 112.7055370938607 Scatter: 49.750919206121175 [ Info: Updating machine(LaplaceRegressor(model = Chain(Dense(4 => 20, relu), Dense(20 => 20, relu), Dense(20 => 20, relu), Dense(20 => 1)), …), …). Epoch 100: Loss: 37.59963903363531 [ Info: Iteration 10: P₀=1.0521815757037591, σ=0.4468016629407111 loss(exp.(logP₀), exp.(logσ)) = 308.91686112640775 Log likelihood: -107.86883693610184 Log det ratio: 313.6587614504782 Scatter: 88.43728693013368 [ Info: Iteration 20: P₀=1.0410712048876174, σ=0.5806461991715891 loss(exp.(logP₀), exp.(logσ)) = 304.36426411478806 Log likelihood: -103.32544115527051 Log det ratio: 314.57420070829306 Scatter: 87.50344521074211 [ Info: Iteration 30: P₀=1.0338495876576967, σ=0.5481258362855987 loss(exp.(logP₀), exp.(logσ)) = 303.7382686733742 Log likelihood: -102.70253615561109 Log det ratio: 315.17500653678286 Scatter: 86.89645849874337 [ Info: Iteration 40: P₀=1.0274472227621116, σ=0.5250002416512758 loss(exp.(logP₀), exp.(logσ)) = 303.80567815575426 Log likelihood: -102.77074993012113 Log det ratio: 315.7115253959003 Scatter: 86.35833105536595 [ Info: Iteration 50: P₀=1.023887015524627, σ=0.5532478200717432 loss(exp.(logP₀), exp.(logσ)) = 303.784970290254 Log likelihood: -102.74969323965644 Log det ratio: 316.0114632874971 Scatter: 86.05909081369798 [ Info: Iteration 60: P₀=1.025455269024796, σ=0.5308621109993988 loss(exp.(logP₀), exp.(logσ)) = 303.7418703084005 Log likelihood: -102.70681741921373 Log det ratio: 315.8792011333215 Scatter: 86.19090464505203 [ Info: Iteration 70: P₀=1.0289319249898365, σ=0.5452459523718538 loss(exp.(logP₀), exp.(logσ)) = 303.72027645073666 Log likelihood: -102.68532503284345 Log det ratio: 315.5867805467104 Scatter: 86.48312228907606 [ Info: Iteration 80: P₀=1.028433643590896, σ=0.5364050612967982 loss(exp.(logP₀), exp.(logσ)) = 303.71148578813217 Log likelihood: -102.6765531881092 Log det ratio: 315.6286241376208 Scatter: 86.44124106242506 [ Info: Iteration 90: P₀=1.0271943809724349, σ=0.5414932267119699 loss(exp.(logP₀), exp.(logσ)) = 303.70824942465526 Log likelihood: -102.67331527785143 Log det ratio: 315.73278893321327 Scatter: 86.33707936039438 [ Info: Iteration 100: P₀=1.028151345301163, σ=0.5387582504048062 loss(exp.(logP₀), exp.(logσ)) = 303.7071334091565 Log likelihood: -102.67220652569131 Log det ratio: 315.6523402582141 Scatter: 86.41751350871621 ┌ 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/FWHkB/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 30m10.7s Testing LaplaceRedux tests passed Testing completed after 1843.56s PkgEval succeeded after 2108.68s