Package evaluation to test LaplaceRedux on Julia 1.12.4 (01a2eadb04*) started at 2026-01-09T12:18:11.361 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 8.42s ################################################################################ # Installation # Installing LaplaceRedux... Resolving package versions... Updating `~/.julia/environments/v1.12/Project.toml` [c52c1a26] + LaplaceRedux v1.2.1 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 v0.4.6 [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 v1.15.0 [53c48c17] + FixedPointNumbers v0.8.5 ⌅ [587475ba] + Flux v0.14.25 [f6369f11] + ForwardDiff v1.3.1 ⌅ [d9f16b24] + Functors v0.4.12 [0c68f7d7] + GPUArrays v11.3.3 [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 v0.9.32 [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] + 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 v0.34.9 [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 v1.7.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [4af54fe1] + LazyArtifacts v1.11.0 [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.12.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.12.1 [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 v1.12.0 [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 v1.3.0+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 6.97s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... ┌ Error: Failed to use TestEnv.jl; test dependencies will not be precompiled │ exception = │ UndefVarError: `project_rel_path` not defined in `TestEnv` │ Suggestion: this global was defined as `Pkg.Operations.project_rel_path` but not assigned a value. │ Stacktrace: │ [1] get_test_dir(ctx::Pkg.Types.Context, pkgspec::PackageSpec) │ @ TestEnv ~/.julia/packages/TestEnv/iseFl/src/julia-1.11/common.jl:75 │ [2] test_dir_has_project_file │ @ ~/.julia/packages/TestEnv/iseFl/src/julia-1.11/common.jl:52 [inlined] │ [3] maybe_gen_project_override! │ @ ~/.julia/packages/TestEnv/iseFl/src/julia-1.11/common.jl:83 [inlined] │ [4] activate(pkg::String; allow_reresolve::Bool) │ @ TestEnv ~/.julia/packages/TestEnv/iseFl/src/julia-1.11/activate_set.jl:12 │ [5] activate(pkg::String) │ @ TestEnv ~/.julia/packages/TestEnv/iseFl/src/julia-1.11/activate_set.jl:9 │ [6] top-level scope │ @ /PkgEval.jl/scripts/precompile.jl:24 │ [7] include(mod::Module, _path::String) │ @ Base ./Base.jl:306 │ [8] exec_options(opts::Base.JLOptions) │ @ Base ./client.jl:317 │ [9] _start() │ @ Base ./client.jl:550 └ @ Main /PkgEval.jl/scripts/precompile.jl:26 Precompiling package dependencies... Precompiling packages... 153735.2 ms ✓ LaplaceRedux 1 dependency successfully precompiled in 159 seconds. 212 already precompiled. Precompilation completed after 173.82s ################################################################################ # Testing # Testing LaplaceRedux Status `/tmp/jl_VhCqOC/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 v1.3.0 [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 v1.12.0 [9a3f8284] Random v1.11.0 [9e88b42a] Serialization v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_VhCqOC/Manifest.toml` [621f4979] AbstractFFTs v1.5.0 [7d9f7c33] Accessors v0.1.43 [79e6a3ab] Adapt v4.4.0 [66dad0bd] AliasTables v1.1.3 [4c88cf16] Aqua v0.8.14 [dce04be8] ArgCheck v2.5.0 [a9b6321e] Atomix v1.1.2 [198e06fe] BangBang v0.4.6 [9718e550] Baselet v0.1.1 [d1d4a3ce] BitFlags v0.1.9 [fa961155] CEnum v0.5.0 [336ed68f] CSV v0.10.15 [324d7699] CategoricalArrays v1.0.2 [af321ab8] CategoricalDistributions v0.2.1 [082447d4] ChainRules v1.72.6 [d360d2e6] ChainRulesCore v1.26.0 [944b1d66] CodecZlib v0.7.8 [35d6a980] ColorSchemes v3.31.0 [3da002f7] ColorTypes v0.12.1 [c3611d14] ColorVectorSpace v0.11.0 [5ae59095] Colors v0.13.1 [bbf7d656] CommonSubexpressions v0.3.1 [34da2185] Compat v4.18.1 [a33af91c] CompositionsBase v0.1.2 [ed09eef8] ComputationalResources v0.3.2 [f0e56b4a] ConcurrentUtilities v2.5.0 [187b0558] ConstructionBase v1.6.0 [6add18c4] ContextVariablesX v0.1.3 [d38c429a] Contour v0.6.3 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.8.1 [864edb3b] DataStructures v0.19.3 [e2d170a0] DataValueInterfaces v1.0.0 [244e2a9f] 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Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... Precompiling packages... 12799.5 ms ✓ Latexify 8921.3 ms ✓ RecipesPipeline 2608.8 ms ✓ Latexify → SparseArraysExt 127972.8 ms ✓ Plots 4 dependencies successfully precompiled in 154 seconds. 173 already precompiled. Precompiling packages... 6792.0 ms ✓ Zygote → ZygoteColorsExt 1 dependency successfully precompiled in 8 seconds. 109 already precompiled. Precompiling packages... 1360.7 ms ✓ CategoricalArrays → CategoricalArraysJSONExt 1 dependency successfully precompiled in 1 seconds. 19 already precompiled. 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... 34010.3 ms ✓ CSV 1 dependency successfully precompiled in 34 seconds. 29 already precompiled. Precompiling packages... 102728.2 ms ✓ DataFrames 1 dependency successfully precompiled in 104 seconds. 35 already precompiled. Precompiling packages... 7725.2 ms ✓ BangBang → BangBangDataFramesExt 1 dependency successfully precompiled in 8 seconds. 48 already precompiled. Precompiling packages... 7582.2 ms ✓ Transducers → TransducersDataFramesExt 1 dependency successfully precompiled in 8 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... 58681.0 ms ✓ StatisticalMeasures 11701.2 ms ✓ StatisticalMeasures → ScientificTypesExt 2 dependencies successfully precompiled in 74 seconds. 140 already precompiled. Precompiling packages... 11887.2 ms ✓ MLJBase → DefaultMeasuresExt 1 dependency successfully precompiled in 18 seconds. 151 already precompiled. Precompiling packages... 19739.4 ms ✓ MLJTestInterface 1 dependency successfully precompiled in 22 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=1.0688519010898239, σ=0.921569886156277 loss(exp.(logP₀), exp.(logσ)) = 248.6849079172489 Log likelihood: -166.2843677578102 Log det ratio: 113.35571284649092 Scatter: 51.445367472386486 [ Info: Iteration 20: P₀=1.0503290322427792, σ=0.9110504372023588 loss(exp.(logP₀), exp.(logσ)) = 248.58161444840562 Log likelihood: -166.18790367784115 Log det ratio: 114.23358618231909 Scatter: 50.553835358809835 [ Info: Iteration 30: P₀=1.041057628168598, σ=0.886706120267148 loss(exp.(logP₀), exp.(logσ)) = 248.48209520171542 Log likelihood: -166.0880235756446 Log det ratio: 114.68055380363512 Scatter: 50.10758944850654 [ Info: Iteration 40: P₀=1.040537241292704, σ=0.8788972599774384 loss(exp.(logP₀), exp.(logσ)) = 248.48967516097048 Log likelihood: -166.0955070433012 Log det ratio: 114.70579374986853 Scatter: 50.082542485469965 [ Info: Iteration 50: P₀=1.047682909961122, σ=0.8799243732966412 loss(exp.(logP₀), exp.(logσ)) = 248.48691384929126 Log likelihood: -166.09336105490593 Log det ratio: 114.36063187194128 Scatter: 50.42647371682939 [ Info: Iteration 60: P₀=1.0490198444572174, σ=0.8820326762266608 loss(exp.(logP₀), exp.(logσ)) = 248.48367494132017 Log likelihood: -166.09006823803657 Log det ratio: 114.29639112435848 Scatter: 50.490822282208754 [ Info: Iteration 70: P₀=1.0455633042715773, σ=0.8835002316775333 loss(exp.(logP₀), exp.(logσ)) = 248.48222756288357 Log likelihood: -166.08865142472843 Log det ratio: 114.46269820643477 Scatter: 50.32445406987549 [ Info: Iteration 80: P₀=1.0476875561358856, σ=0.8844953329475334 loss(exp.(logP₀), exp.(logσ)) = 248.4816488409727 Log likelihood: -166.08809595103628 Log det ratio: 114.360408436021 Scatter: 50.42669734385182 [ Info: Iteration 90: P₀=1.046622647797419, σ=0.8852497289196518 loss(exp.(logP₀), exp.(logσ)) = 248.48143952340752 Log likelihood: -166.08789176180824 Log det ratio: 114.41165373752179 Scatter: 50.37544178567677 [ Info: Iteration 100: P₀=1.0472494214255066, σ=0.8857667977924667 loss(exp.(logP₀), exp.(logσ)) = 248.48140610231533 Log likelihood: -166.0878594028542 Log det ratio: 114.38148410398992 Scatter: 50.40560929493234 [ Info: Updating machine(LaplaceRegressor(model = Chain(Dense(4 => 20, relu), Dense(20 => 20, relu), Dense(20 => 20, relu), Dense(20 => 1)), …), …). Epoch 100: Loss: 54.7659318872872 [ Info: Iteration 10: P₀=0.9441408077716851, σ=0.5652248160937033 loss(exp.(logP₀), exp.(logσ)) = 351.43554069916604 Log likelihood: -129.44718017572353 Log det ratio: 357.1833683806245 Scatter: 86.79335266626047 [ Info: Iteration 20: P₀=0.9596647753605552, σ=0.7173535623018904 loss(exp.(logP₀), exp.(logσ)) = 349.76267084686265 Log likelihood: -127.78277237070833 Log det ratio: 355.7393508031381 Scatter: 88.22044614917053 [ Info: Iteration 30: P₀=0.967446872030457, σ=0.6151262600306872 loss(exp.(logP₀), exp.(logσ)) = 349.0483753841902 Log likelihood: -127.06799720712613 Log det ratio: 355.02491451906633 Scatter: 88.93584183506175 [ Info: Iteration 40: P₀=0.9687377232852737, σ=0.6741820856749203 loss(exp.(logP₀), exp.(logσ)) = 348.78132987999004 Log likelihood: -126.80057251265893 Log det ratio: 354.9070070107026 Scatter: 89.05450772395965 [ Info: Iteration 50: P₀=0.9629440207433348, σ=0.6373065246430502 loss(exp.(logP₀), exp.(logσ)) = 348.681889796818 Log likelihood: -126.70216848025152 Log det ratio: 355.437540706769 Scatter: 88.52190192636391 [ Info: Iteration 60: P₀=0.9600181307373223, σ=0.6586253034441436 loss(exp.(logP₀), exp.(logσ)) = 348.6429373201979 Log likelihood: -126.66308463107242 Log det ratio: 355.70677583509956 Scatter: 88.25292954315142 [ Info: Iteration 70: P₀=0.963305869765953, σ=0.6475110270823454 loss(exp.(logP₀), exp.(logσ)) = 348.628100007553 Log likelihood: -126.64836429707549 Log det ratio: 355.4043052933474 Scatter: 88.55516612760755 [ Info: Iteration 80: P₀=0.9622403737000326, σ=0.6512203089705995 loss(exp.(logP₀), exp.(logσ)) = 348.6247912480876 Log likelihood: -126.64507864198166 Log det ratio: 355.5022084318339 Scatter: 88.45721678037805 [ Info: Iteration 90: P₀=0.9622663951115134, σ=0.651830945479802 loss(exp.(logP₀), exp.(logσ)) = 348.625051283478 Log likelihood: -126.64533880902148 Log det ratio: 355.49981606184394 Scatter: 88.45960888706908 [ Info: Iteration 100: P₀=0.9624047916726127, σ=0.6497484908363551 loss(exp.(logP₀), exp.(logσ)) = 348.62508934342566 Log likelihood: -126.64537698334605 Log det ratio: 355.4870932588338 Scatter: 88.4723314613254 ┌ 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 33m16.2s Testing LaplaceRedux tests passed Testing completed after 2022.44s PkgEval succeeded after 2234.61s