Package evaluation of LaplaceRedux on Julia 1.11.4 (a71dd056e0*) started at 2025-04-08T19:57:57.887 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 9.04s ################################################################################ # Installation # Installing LaplaceRedux... Resolving package versions... Updating `~/.julia/environments/v1.11/Project.toml` [c52c1a26] + LaplaceRedux v1.2.0 Updating `~/.julia/environments/v1.11/Manifest.toml` [621f4979] + AbstractFFTs v1.5.0 [7d9f7c33] + Accessors v0.1.42 [79e6a3ab] + Adapt v4.3.0 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [a9b6321e] + Atomix v1.1.1 [198e06fe] + BangBang v0.4.4 [9718e550] + Baselet v0.1.1 [fa961155] + CEnum v0.5.0 [324d7699] + CategoricalArrays v0.10.8 [af321ab8] + CategoricalDistributions v0.1.15 [082447d4] + ChainRules v1.72.3 [d360d2e6] + ChainRulesCore v1.25.1 [3da002f7] + ColorTypes v0.12.1 [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 v0.18.22 [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 v0.25.118 [ffbed154] + DocStringExtensions v0.9.4 [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 v1.0.1 ⌅ [d9f16b24] + Functors v0.4.12 [0c68f7d7] + GPUArrays v11.2.2 [46192b85] + GPUArraysCore v0.2.0 [076d061b] + HashArrayMappedTries v0.2.0 [34004b35] + HypergeometricFunctions v0.3.28 [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 v1.0.1 [2ab3a3ac] + LogExpFunctions v0.3.29 [c2834f40] + MLCore v1.0.0 ⌃ [7e8f7934] + MLDataDevices v1.5.3 [a7f614a8] + MLJBase v1.8.1 [e80e1ace] + MLJModelInterface v1.11.0 [d8e11817] + MLStyle v0.4.17 [f1d291b0] + MLUtils v0.4.8 [1914dd2f] + MacroTools v0.5.15 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [872c559c] + NNlib v0.9.30 [77ba4419] + NaNMath v1.1.3 [71a1bf82] + NameResolution v0.1.5 [0b1bfda6] + OneHotArrays v0.2.7 ⌅ [3bd65402] + Optimisers v0.3.4 [bac558e1] + OrderedCollections v1.8.0 [90014a1f] + PDMats v0.11.33 [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 v1.10.4 [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 v0.8.0 [321657f4] + ScientificTypes v3.1.0 [30f210dd] + ScientificTypesBase v3.0.0 [7e506255] + ScopedValues v1.3.0 [efcf1570] + Setfield v1.1.2 [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 v1.9.13 [1e83bf80] + StaticArraysCore v1.4.3 [c062fc1d] + StatisticalMeasuresBase v0.1.2 [64bff920] + StatisticalTraits v3.4.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.0 [2913bbd2] + StatsBase v0.34.4 [4c63d2b9] + StatsFuns v1.4.0 [892a3eda] + StringManipulation v0.4.1 [09ab397b] + StructArrays v0.7.1 [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 v0.6.76 [700de1a5] + ZygoteRules v0.2.7 [dad2f222] + LLVMExtra_jll v0.0.35+0 [efe28fd5] + OpenSpecFun_jll 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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 5.68s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 491.38s ################################################################################ # Testing # Testing LaplaceRedux Status `/tmp/jl_6gcRXg/Project.toml` [4c88cf16] Aqua v0.8.11 [336ed68f] CSV v0.10.15 [a93c6f00] DataFrames v1.7.0 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.118 ⌅ [587475ba] Flux v0.14.25 [682c06a0] JSON v0.21.4 [c52c1a26] LaplaceRedux v1.2.0 [a7f614a8] MLJBase v1.8.1 [e80e1ace] MLJModelInterface v1.11.0 [72560011] MLJTestInterface v0.2.8 [f1d291b0] MLUtils v0.4.8 [91a5bcdd] Plots v1.40.11 [860ef19b] StableRNGs v1.0.2 [a19d573c] StatisticalMeasures v0.2.1 [10745b16] Statistics v1.11.1 [9d524318] TaijaData v1.1.5 [592b5752] Trapz v2.0.3 [bc48ee85] Tullio v0.3.8 ⌅ [e88e6eb3] Zygote v0.6.76 [37e2e46d] LinearAlgebra v1.11.0 [9a3f8284] Random v1.11.0 [9e88b42a] Serialization v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_6gcRXg/Manifest.toml` [621f4979] AbstractFFTs v1.5.0 [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] 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[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... Precompiling Plots... 7302.5 ms ✓ ColorSchemes 21632.9 ms ✓ PlotUtils 10210.2 ms ✓ PlotThemes 9113.9 ms ✓ RecipesPipeline 132673.6 ms ✓ Plots 18295.4 ms ✓ Plots → UnitfulExt 6 dependencies successfully precompiled in 202 seconds. 180 already precompiled. Precompiling ZygoteColorsExt... 12629.3 ms ✓ GPUArrays 5311.5 ms ✓ Zygote → ZygoteColorsExt 2 dependencies successfully precompiled in 20 seconds. 105 already precompiled. Precompiling SpecialFunctionsExt... 1820.9 ms ✓ ColorVectorSpace → SpecialFunctionsExt 1 dependency successfully precompiled in 2 seconds. 20 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) 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... 85279.0 ms ✓ DataFrames 1 dependency successfully precompiled in 86 seconds. 32 already precompiled. Precompiling BangBangDataFramesExt... 4956.8 ms ✓ BangBang → BangBangDataFramesExt 1 dependency successfully precompiled in 6 seconds. 45 already precompiled. Precompiling TransducersDataFramesExt... 4895.6 ms ✓ Transducers → TransducersDataFramesExt 1 dependency successfully precompiled in 6 seconds. 61 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 MLJTestInterface... 16286.2 ms ✓ MLJTestInterface 1 dependency successfully precompiled in 20 seconds. 158 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/1bSKz/src/direct_mlj.jl:161 [ Info: Iteration 10: P₀=1.0955933375326827, σ=2.248688180171472 loss(exp.(logP₀), exp.(logσ)) = 363.6551505005559 Log likelihood: -274.2743995587516 Log det ratio: 124.82341400131008 Scatter: 53.93808788229851 [ Info: Iteration 20: P₀=1.0789628903855257, σ=2.675969144660933 loss(exp.(logP₀), exp.(logσ)) = 370.3736183463188 Log likelihood: -280.98954752571456 Log det ratio: 125.64880145389445 Scatter: 53.119340187313995 [ Info: Iteration 30: P₀=1.0793279518249557, σ=2.190537064963684 loss(exp.(logP₀), exp.(logσ)) = 363.15196683682547 Log likelihood: -273.7680564692326 Log det ratio: 125.63050789750243 Scatter: 53.1373128376833 [ Info: Iteration 40: P₀=1.0843347565553878, σ=1.8856857624066448 loss(exp.(logP₀), exp.(logσ)) = 363.45962498849906 Log likelihood: -274.077515495967 Log det ratio: 125.38041188321539 Scatter: 53.383807101848625 [ Info: Iteration 50: P₀=1.0884385605562195, σ=2.0098092453205796 loss(exp.(logP₀), exp.(logσ)) = 362.60910479568037 Log likelihood: -273.2279192951667 Log det ratio: 125.17652603233016 Scatter: 53.585844968697174 [ Info: Iteration 60: P₀=1.0916710735613038, σ=2.1144267327484245 loss(exp.(logP₀), exp.(logσ)) = 362.7098492105775 Log likelihood: -273.32904427987387 Log det ratio: 125.01662229428791 Scatter: 53.744987567119296 [ Info: Iteration 70: P₀=1.0942020032360695, σ=2.0446112426565413 loss(exp.(logP₀), exp.(logσ)) = 362.56983401030834 Log likelihood: -273.1891148871597 Log det ratio: 124.89184832669483 Scatter: 53.86958991960244 [ Info: Iteration 80: P₀=1.0952992222271494, σ=2.0214811395322783 loss(exp.(logP₀), exp.(logσ)) = 362.586972707127 Log likelihood: -273.2062331510831 Log det ratio: 124.83787107066999 Scatter: 53.92360804141784 [ Info: Iteration 90: P₀=1.0946943002909002, σ=2.054288855216052 loss(exp.(logP₀), exp.(logσ)) = 362.5725110771765 Log likelihood: -273.19178708881833 Log det ratio: 124.8676213618932 Scatter: 53.89382661482318 [ Info: Iteration 100: P₀=1.0937869106449447, σ=2.0490380166631987 loss(exp.(logP₀), exp.(logσ)) = 362.57035379187175 Log likelihood: -273.18963332361756 Log det ratio: 124.91228679347776 Scatter: 53.849154143030624 [ Info: Updating machine(LaplaceRegressor(model = Chain(Dense(4 => 20, relu), Dense(20 => 20, relu), Dense(20 => 20, relu), Dense(20 => 1)), …), …). Epoch 100: Loss: 350.4542104075099 [ Info: Iteration 10: P₀=0.9169030893378418, σ=2.032121332391582 loss(exp.(logP₀), exp.(logσ)) = 488.726004052451 Log likelihood: -250.67551622290114 Log det ratio: 377.1819833201433 Scatter: 98.91899233895639 [ Info: Iteration 20: P₀=0.8391758346930068, σ=1.8080972908088788 loss(exp.(logP₀), exp.(logσ)) = 484.81970421254744 Log likelihood: -246.8529276063322 Log det ratio: 385.4000727079833 Scatter: 90.53348050444714 [ Info: Iteration 30: P₀=0.8474896914360058, σ=1.5061594693592137 loss(exp.(logP₀), exp.(logσ)) = 484.98646078487593 Log likelihood: -247.0296213179848 Log det ratio: 384.4832679455892 Scatter: 91.43041098819303 [ Info: Iteration 40: P₀=0.8705657454189593, σ=1.704348858114286 loss(exp.(logP₀), exp.(logσ)) = 483.94427178988246 Log likelihood: -245.99045107542577 Log det ratio: 381.98769797682434 Scatter: 93.91994345208907 [ Info: Iteration 50: P₀=0.8675514498886404, σ=1.6724772928927814 loss(exp.(logP₀), exp.(logσ)) = 483.83952233524354 Log likelihood: -245.88731252715584 Log det ratio: 382.3096698295219 Scatter: 93.59474978665351 [ Info: Iteration 60: P₀=0.8608499464887783, σ=1.621376454377644 loss(exp.(logP₀), exp.(logσ)) = 483.8635237765161 Log likelihood: -245.9127589336997 Log det ratio: 383.02976357214936 Scatter: 92.87176611348345 [ Info: Iteration 70: P₀=0.8588670258289542, σ=1.672847202761923 loss(exp.(logP₀), exp.(logσ)) = 483.83891967195433 Log likelihood: -245.88801215155087 Log det ratio: 383.2439739493229 Scatter: 92.65784109148406 [ Info: Iteration 80: P₀=0.8592566923680454, σ=1.6424382732425218 loss(exp.(logP₀), exp.(logσ)) = 483.82285062333324 Log likelihood: -245.87199183237888 Log det ratio: 383.20183778225226 Scatter: 92.69987979965654 [ Info: Iteration 90: P₀=0.8599465909227665, σ=1.6547413360263603 loss(exp.(logP₀), exp.(logσ)) = 483.8189703877607 Log likelihood: -245.86817303112724 Log det ratio: 383.1272860337836 Scatter: 92.77430867948328 [ Info: Iteration 100: P₀=0.860391384129259, σ=1.6518578020479688 loss(exp.(logP₀), exp.(logσ)) = 483.81855999235813 Log likelihood: -245.86778543094695 Log det ratio: 383.07925446063587 Scatter: 92.82229466218647 ┌ 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 30m57.4s Testing LaplaceRedux tests passed Testing completed after 1879.93s PkgEval succeeded after 2411.62s