Package evaluation of LaplaceRedux on Julia 1.10.8 (92f03a4775*) started at 2025-02-25T14:12:07.178 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 3.89s ################################################################################ # Installation # Installing LaplaceRedux... Resolving package versions... Updating `~/.julia/environments/v1.10/Project.toml` [c52c1a26] + LaplaceRedux v1.2.0 Updating `~/.julia/environments/v1.10/Manifest.toml` [621f4979] + AbstractFFTs v1.5.0 [7d9f7c33] + Accessors v0.1.41 [79e6a3ab] + Adapt v4.2.0 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.4.0 [a9b6321e] + Atomix v1.1.0 [198e06fe] + BangBang v0.4.3 [9718e550] + Baselet v0.1.1 [fa961155] + CEnum v0.5.0 [324d7699] + CategoricalArrays v0.10.8 [af321ab8] + CategoricalDistributions v0.1.15 [082447d4] + ChainRules v1.72.2 [d360d2e6] + ChainRulesCore v1.25.1 [3da002f7] + ColorTypes v0.12.0 [bbf7d656] + CommonSubexpressions v0.3.1 [34da2185] + Compat v4.16.0 [a33af91c] + CompositionsBase v0.1.2 [ed09eef8] + ComputationalResources v0.3.2 [187b0558] + ConstructionBase v1.5.8 [6add18c4] + ContextVariablesX v0.1.3 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.18.20 [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 v0.25.117 [ffbed154] + DocStringExtensions v0.9.3 [cc61a311] + FLoops v0.2.2 [b9860ae5] + FLoopsBase v0.1.1 [1a297f60] + FillArrays v1.13.0 [53c48c17] + FixedPointNumbers v0.8.5 ⌅ [587475ba] + Flux v0.14.25 [f6369f11] + ForwardDiff v0.10.38 ⌅ [d9f16b24] + Functors v0.4.12 [0c68f7d7] + GPUArrays v11.2.2 [46192b85] + GPUArraysCore v0.2.0 [076d061b] + HashArrayMappedTries v0.2.0 [34004b35] + HypergeometricFunctions v0.3.27 [7869d1d1] + IRTools v0.4.14 [22cec73e] + InitialValues v0.3.1 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.4 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.7.0 [b14d175d] + JuliaVariables v0.2.4 [63c18a36] + KernelAbstractions v0.9.34 [929cbde3] + LLVM v9.2.0 [b964fa9f] + LaTeXStrings v1.4.0 [c52c1a26] + LaplaceRedux v1.2.0 ⌅ [92ad9a40] + LearnAPI v0.1.0 [2ab3a3ac] + LogExpFunctions v0.3.29 [c2834f40] + MLCore v1.0.0 ⌃ [7e8f7934] + MLDataDevices v1.5.3 [a7f614a8] + MLJBase v1.7.0 [e80e1ace] + MLJModelInterface v1.11.0 [d8e11817] + MLStyle v0.4.17 [f1d291b0] + MLUtils v0.4.7 [1914dd2f] + MacroTools v0.5.15 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [872c559c] + NNlib v0.9.27 [77ba4419] + NaNMath v1.1.2 [71a1bf82] + NameResolution v0.1.5 [0b1bfda6] + OneHotArrays v0.2.6 ⌅ [3bd65402] + Optimisers v0.3.4 [bac558e1] + OrderedCollections v1.8.0 [90014a1f] + PDMats v0.11.32 [d96e819e] + Parameters v0.12.3 [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.4.3 [8162dcfd] + PrettyPrint v0.2.0 [08abe8d2] + PrettyTables v2.4.0 [33c8b6b6] + ProgressLogging v0.1.4 [92933f4c] + ProgressMeter v1.10.2 [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 v1.3.0 [79098fc4] + Rmath v0.8.0 [321657f4] + ScientificTypes v3.1.0 [30f210dd] + ScientificTypesBase v3.0.0 [7e506255] + ScopedValues v1.3.0 [efcf1570] + Setfield v1.1.1 [605ecd9f] + ShowCases v0.1.0 [699a6c99] + SimpleTraits v0.9.4 [a2af1166] + SortingAlgorithms v1.2.1 [dc90abb0] + SparseInverseSubset v0.1.2 [276daf66] + SpecialFunctions v2.5.0 [171d559e] + SplittablesBase v0.1.15 [90137ffa] + StaticArrays v1.9.12 [1e83bf80] + StaticArraysCore v1.4.3 [c062fc1d] + StatisticalMeasuresBase v0.1.2 [64bff920] + StatisticalTraits v3.4.0 [82ae8749] + StatsAPI v1.7.0 [2913bbd2] + StatsBase v0.34.4 [4c63d2b9] + StatsFuns v1.3.2 [892a3eda] + StringManipulation v0.4.1 ⌃ [09ab397b] + StructArrays v0.6.21 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.0 [28d57a85] + Transducers v0.4.84 [bc48ee85] + Tullio v0.3.8 [3a884ed6] + UnPack v1.0.2 [013be700] + UnsafeAtomics v0.3.0 ⌅ [e88e6eb3] + Zygote v0.6.75 [700de1a5] + ZygoteRules v0.2.7 [dad2f222] + LLVMExtra_jll v0.0.35+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [0dad84c5] + ArgTools v1.1.1 [56f22d72] + Artifacts [2a0f44e3] + Base64 [ade2ca70] + Dates [8ba89e20] + Distributed [f43a241f] + Downloads v1.6.0 [7b1f6079] + FileWatching [9fa8497b] + Future [b77e0a4c] + InteractiveUtils [4af54fe1] + LazyArtifacts [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 [8f399da3] + Libdl [37e2e46d] + LinearAlgebra [56ddb016] + Logging [d6f4376e] + Markdown [a63ad114] + Mmap [ca575930] + NetworkOptions v1.2.0 [44cfe95a] + Pkg v1.10.0 [de0858da] + Printf [3fa0cd96] + REPL [9a3f8284] + Random [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization [6462fe0b] + Sockets [2f01184e] + SparseArrays v1.10.0 [10745b16] + Statistics v1.10.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test [cf7118a7] + UUIDs [4ec0a83e] + Unicode [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] + LibCURL_jll v8.4.0+0 [e37daf67] + LibGit2_jll v1.6.4+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.2+1 [14a3606d] + MozillaCACerts_jll v2023.1.10 [4536629a] + OpenBLAS_jll v0.3.23+4 [05823500] + OpenLibm_jll v0.8.1+4 [bea87d4a] + SuiteSparse_jll v7.2.1+1 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.52.0+1 [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 11.42s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 281.1s ################################################################################ # Testing # Testing LaplaceRedux Status `/tmp/jl_XGaQYX/Project.toml` [4c88cf16] Aqua v0.8.11 [336ed68f] CSV v0.10.15 [a93c6f00] DataFrames v1.7.0 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.117 ⌅ [587475ba] Flux v0.14.25 [682c06a0] JSON v0.21.4 [c52c1a26] LaplaceRedux v1.2.0 [a7f614a8] MLJBase v1.7.0 [e80e1ace] MLJModelInterface v1.11.0 [72560011] MLJTestInterface v0.2.8 [f1d291b0] MLUtils v0.4.7 [91a5bcdd] Plots v1.40.9 [860ef19b] StableRNGs v1.0.2 ⌃ [a19d573c] StatisticalMeasures v0.1.7 [9d524318] TaijaData v1.1.2 [592b5752] Trapz v2.0.3 [bc48ee85] Tullio v0.3.8 ⌅ [e88e6eb3] Zygote v0.6.75 [37e2e46d] LinearAlgebra [9a3f8284] Random [9e88b42a] Serialization [10745b16] Statistics v1.10.0 [8dfed614] Test Status `/tmp/jl_XGaQYX/Manifest.toml` [621f4979] AbstractFFTs v1.5.0 [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.41 [79e6a3ab] Adapt 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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 ┌ 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 [ 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/1bSKz/src/direct_mlj.jl:161 [ Info: Iteration 10: P₀=0.9519822453215802, σ=0.9164845001358662 loss(exp.(logP₀), exp.(logσ)) = 233.51112281827983 Log likelihood: -162.805898423036 Log det ratio: 96.39740639387051 Scatter: 45.01304239661709 [ Info: Iteration 20: P₀=0.9637783325108955, σ=0.8597675155988811 loss(exp.(logP₀), exp.(logσ)) = 233.00787445261025 Log likelihood: -162.30792391365839 Log det ratio: 95.82909851782111 Scatter: 45.57080256008261 [ Info: Iteration 30: P₀=0.9707516224139598, σ=0.8410127795657238 loss(exp.(logP₀), exp.(logσ)) = 233.07119751056626 Log likelihood: -162.37218277017598 Log det ratio: 95.49750544154949 Scatter: 45.900524039231094 [ Info: Iteration 40: P₀=0.975723100873275, σ=0.8566231652581491 loss(exp.(logP₀), exp.(logσ)) = 233.00914014017457 Log likelihood: -162.30982094187635 Log det ratio: 95.26304550837916 Scatter: 46.13559288821728 [ Info: Iteration 50: P₀=0.975693113619404, σ=0.864620803939604 loss(exp.(logP₀), exp.(logσ)) = 233.01101383675604 Log likelihood: -162.31169887695856 Log det ratio: 95.26445493337795 Scatter: 46.134174986217026 [ Info: Iteration 60: P₀=0.971226055828289, σ=0.8642168339066328 loss(exp.(logP₀), exp.(logσ)) = 233.01008789660622 Log likelihood: -162.31107880725494 Log det ratio: 95.47506127215851 Scatter: 45.922956906544044 [ Info: Iteration 70: P₀=0.9698089261451718, σ=0.8623218850135012 loss(exp.(logP₀), exp.(logσ)) = 233.00795560843196 Log likelihood: -162.30890780797677 Log det ratio: 95.542145530703 Scatter: 45.85595007020739 [ Info: Iteration 80: P₀=0.972065394575899, σ=0.8611370034349806 loss(exp.(logP₀), exp.(logσ)) = 233.0071894827437 Log likelihood: -162.30817240240958 Log det ratio: 95.43539038935654 Scatter: 45.962643771311704 [ Info: Iteration 90: P₀=0.9712975522628131, σ=0.860601885035668 loss(exp.(logP₀), exp.(logσ)) = 233.00700715484794 Log likelihood: -162.30799827998467 Log det ratio: 95.47168024227923 Scatter: 45.926337507447315 [ Info: Iteration 100: P₀=0.9712309644744638, σ=0.860370577308769 loss(exp.(logP₀), exp.(logσ)) = 233.00696263229312 Log likelihood: -162.3079535630009 Log det ratio: 95.47482913412108 Scatter: 45.92318900446337 [ Info: Updating machine(LaplaceRegressor(model = Chain(Dense(4 => 20, relu), Dense(20 => 20, relu), Dense(20 => 20, relu), Dense(20 => 1)), …), …). Epoch 100: Loss: 76.54288410427961 [ Info: Iteration 10: P₀=1.1005556526142888, σ=0.7788126178522721 loss(exp.(logP₀), exp.(logσ)) = 346.44812034778124 Log likelihood: -148.45707964426487 Log det ratio: 307.4358860397621 Scatter: 88.54619536727056 [ Info: Iteration 20: P₀=1.0837634015722213, σ=0.7499658140065316 loss(exp.(logP₀), exp.(logσ)) = 346.5403382625095 Log likelihood: -148.55243591351464 Log det ratio: 308.78064499883385 Scatter: 87.19515969915584 [ Info: Iteration 30: P₀=1.0803500436662754, σ=0.7977195781713706 loss(exp.(logP₀), exp.(logσ)) = 346.57298561850087 Log likelihood: -148.58431873925915 Log det ratio: 309.0567988411643 Scatter: 86.92053491731917 [ Info: Iteration 40: P₀=1.0830254719924661, σ=0.7560973360900122 loss(exp.(logP₀), exp.(logσ)) = 346.4882091194804 Log likelihood: -148.50018207678409 Log det ratio: 308.84026517460273 Scatter: 87.1357889107899 [ Info: Iteration 50: P₀=1.0865971830193373, σ=0.7739547923132377 loss(exp.(logP₀), exp.(logσ)) = 346.4350218310646 Log likelihood: -148.44739105833548 Log det ratio: 308.5521074099934 Scatter: 87.42315413546484 [ Info: Iteration 60: P₀=1.0892175910525963, σ=0.7768834536380274 loss(exp.(logP₀), exp.(logσ)) = 346.43972653045444 Log likelihood: -148.45205534185789 Log det ratio: 308.3413609554355 Scatter: 87.63398142175767 [ Info: Iteration 70: P₀=1.0896428086847039, σ=0.7691205142340207 loss(exp.(logP₀), exp.(logσ)) = 346.435377400127 Log likelihood: -148.44767333298324 Log det ratio: 308.307215445306 Scatter: 87.66819268898145 [ Info: Iteration 80: P₀=1.0880528009404145, σ=0.7700471750777893 loss(exp.(logP₀), exp.(logσ)) = 346.43445640655716 Log likelihood: -148.4468376759641 Log det ratio: 308.43497027604167 Scatter: 87.54026718514444 [ Info: Iteration 90: P₀=1.0868472416925665, σ=0.7724342755672748 loss(exp.(logP₀), exp.(logσ)) = 346.4340204822579 Log likelihood: -148.44639792023688 Log det ratio: 308.5319722930071 Scatter: 87.44327283103505 [ Info: Iteration 100: P₀=1.0873905379949933, σ=0.7724369567829937 loss(exp.(logP₀), exp.(logσ)) = 346.43401231216944 Log likelihood: -148.44639880389158 Log det ratio: 308.4882427926232 Scatter: 87.48698422393257 ┌ 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/1bSKz/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 17m33.4s Testing LaplaceRedux tests passed Testing completed after 1074.89s PkgEval succeeded after 1394.32s