Package evaluation to test LaplaceRedux on Julia 1.12.4 (422f456051*) started at 2026-01-29T14:30:20.303 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.12` Set-up completed after 7.93s ################################################################################ # 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 6.98s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompiling packages... 240824.9 ms ✓ LaplaceRedux 1 dependency successfully precompiled in 254 seconds. 353 already precompiled. Precompilation completed after 265.16s ################################################################################ # Testing # Testing LaplaceRedux Status `/tmp/jl_HKXTrF/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_HKXTrF/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... 102672.6 ms ✓ DataFrames 1 dependency successfully precompiled in 104 seconds. 35 already precompiled. Precompiling packages... 8355.7 ms ✓ BangBang → BangBangDataFramesExt 1 dependency successfully precompiled in 12 seconds. 48 already precompiled. Precompiling packages... 7976.1 ms ✓ Transducers → TransducersDataFramesExt 1 dependency successfully precompiled in 9 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... 54751.1 ms ✓ StatisticalMeasures 13038.1 ms ✓ StatisticalMeasures → ScientificTypesExt 2 dependencies successfully precompiled in 72 seconds. 141 already precompiled. Precompiling packages... 15848.5 ms ✓ MLJBase → DefaultMeasuresExt 1 dependency successfully precompiled in 21 seconds. 152 already precompiled. Precompiling packages... 22655.3 ms ✓ MLJTestInterface 1 dependency successfully precompiled in 27 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=0.9082752181018001, σ=0.9071222127553831 loss(exp.(logP₀), exp.(logσ)) = 228.21109733880746 Log likelihood: -161.10459453002312 Log det ratio: 91.81126063975908 Scatter: 42.401744977809585 [ Info: Iteration 20: P₀=0.9205120216598781, σ=0.8337839901325743 loss(exp.(logP₀), exp.(logσ)) = 227.69045904841332 Log likelihood: -160.59064002132348 Log det ratio: 91.22663250086134 Scatter: 42.973005553318345 [ Info: Iteration 30: P₀=0.9251012419444348, σ=0.8318373851692603 loss(exp.(logP₀), exp.(logσ)) = 227.70046252080078 Log likelihood: -160.60181052689728 Log det ratio: 91.01005616047897 Scatter: 43.18724782732802 [ Info: Iteration 40: P₀=0.9254736338698407, σ=0.8544000943402262 loss(exp.(logP₀), exp.(logσ)) = 227.65783281886948 Log likelihood: -160.5592439230798 Log det ratio: 90.99254529148016 Scatter: 43.20463250009919 [ Info: Iteration 50: P₀=0.9258814938366524, σ=0.8557392143918828 loss(exp.(logP₀), exp.(logσ)) = 227.6609703521875 Log likelihood: -160.56244514545182 Log det ratio: 90.97337745740163 Scatter: 43.22367295606973 [ Info: Iteration 60: P₀=0.9282371276007536, σ=0.8503117291485085 loss(exp.(logP₀), exp.(logσ)) = 227.65156567122798 Log likelihood: -160.553297745234 Log det ratio: 90.86289294393025 Scatter: 43.33364290805772 [ Info: Iteration 70: P₀=0.9310247894615034, σ=0.8475413522880445 loss(exp.(logP₀), exp.(logσ)) = 227.650804822339 Log likelihood: -160.5525989563182 Log det ratio: 90.73263015571669 Scatter: 43.463781576324934 [ Info: Iteration 80: P₀=0.9311816072516018, σ=0.8470773458344945 loss(exp.(logP₀), exp.(logσ)) = 227.65095891853844 Log likelihood: -160.55274877076238 Log det ratio: 90.72531786778123 Scatter: 43.47110242777085 [ Info: Iteration 90: P₀=0.9298687878097924, σ=0.847341992398425 loss(exp.(logP₀), exp.(logσ)) = 227.65085370343957 Log likelihood: -160.5526538878448 Log det ratio: 90.78658461285598 Scatter: 43.409815018333596 [ Info: Iteration 100: P₀=0.9302035175285692, σ=0.847658227006051 loss(exp.(logP₀), exp.(logσ)) = 227.65077029780625 Log likelihood: -160.5525733554257 Log det ratio: 90.77095240884151 Scatter: 43.42544147591959 [ Info: Updating machine(LaplaceRegressor(model = Chain(Dense(4 => 20, relu), Dense(20 => 20, relu), Dense(20 => 20, relu), Dense(20 => 1)), …), …). Epoch 100: Loss: 65.01686274981304 [ Info: Iteration 10: P₀=1.0547658774383897, σ=0.6612273376105379 loss(exp.(logP₀), exp.(logσ)) = 329.99346751422866 Log likelihood: -138.58685454618222 Log det ratio: 294.3330143039993 Scatter: 88.48021163209364 [ Info: Iteration 20: P₀=1.038739608642847, σ=0.744085010945032 loss(exp.(logP₀), exp.(logσ)) = 329.53552340637975 Log likelihood: -138.15400518394648 Log det ratio: 295.6272062390531 Scatter: 87.13583020581335 [ Info: Iteration 30: P₀=1.0240008394688636, σ=0.6973461857034664 loss(exp.(logP₀), exp.(logσ)) = 329.30464317229394 Log likelihood: -137.93633227139026 Log det ratio: 296.83716968103204 Scatter: 85.8994521207754 [ Info: Iteration 40: P₀=1.0173693410751035, σ=0.7134447782072247 loss(exp.(logP₀), exp.(logσ)) = 329.25809134748727 Log likelihood: -137.89256022813464 Log det ratio: 297.387900754429 Scatter: 85.34316148427627 [ Info: Iteration 50: P₀=1.0155657776234919, σ=0.7153181169051412 loss(exp.(logP₀), exp.(logσ)) = 329.2612388273967 Log likelihood: -137.89611890968 Log det ratio: 297.53837228216497 Scatter: 85.1918675532685 [ Info: Iteration 60: P₀=1.0148937363330823, σ=0.7041780653862161 loss(exp.(logP₀), exp.(logσ)) = 329.2660618914529 Log likelihood: -137.90105725436894 Log det ratio: 297.5945166538328 Scatter: 85.13549262033519 [ Info: Iteration 70: P₀=1.0136539860765714, σ=0.7147399775302582 loss(exp.(logP₀), exp.(logσ)) = 329.25968214061135 Log likelihood: -137.8948359809794 Log det ratio: 297.6981975314462 Scatter: 85.03149478781766 [ Info: Iteration 80: P₀=1.0118826740730071, σ=0.7100614801126941 loss(exp.(logP₀), exp.(logσ)) = 329.2553095879992 Log likelihood: -137.89056761445252 Log det ratio: 297.8465776416055 Scatter: 84.88290630548785 [ Info: Iteration 90: P₀=1.0108271722375106, σ=0.7092918339903947 loss(exp.(logP₀), exp.(logσ)) = 329.2556710839308 Log likelihood: -137.89092262024064 Log det ratio: 297.93513257018685 Scatter: 84.7943643571935 [ Info: Iteration 100: P₀=1.0111627685247384, σ=0.711343139654308 loss(exp.(logP₀), exp.(logσ)) = 329.2553854024135 Log likelihood: -137.89064456418313 Log det ratio: 297.9069654505426 Scatter: 84.82251622591815 ┌ 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 30m11.0s Testing LaplaceRedux tests passed Testing completed after 1838.43s PkgEval succeeded after 2148.98s