Package evaluation to test CalibrateEmulateSample on Julia 1.14.0-DEV.2291 (26145852c4*) started at 2026-06-05T04:59:48.957 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.14` Set-up completed after 16.2s ################################################################################ # Installation # Installing CalibrateEmulateSample... Resolving package versions... Updating `~/.julia/environments/v1.14/Project.toml` [95e48a1f] + CalibrateEmulateSample v1.1.0 Updating `~/.julia/environments/v1.14/Manifest.toml` [47edcb42] + ADTypes v1.22.0 [14f7f29c] + AMD v0.5.3 [621f4979] + AbstractFFTs v1.5.0 [99985d1d] + AbstractGPs v0.5.24 [80f14c24] + AbstractMCMC v5.15.1 [1520ce14] + AbstractTrees v0.4.5 [7d9f7c33] + Accessors v0.1.44 [79e6a3ab] + Adapt v4.6.0 [5b7e9947] + AdvancedMH v0.8.10 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [ec485272] + ArnoldiMethod v0.4.0 [7d9fca2a] + Arpack v0.5.4 [4fba245c] + ArrayInterface v7.25.0 [13072b0f] + AxisAlgorithms v1.1.0 [39de3d68] + AxisArrays v0.4.8 [198e06fe] + BangBang v0.4.9 [6e4b80f9] + BenchmarkTools v1.8.0 [62783981] + BitTwiddlingConvenienceFunctions v0.1.6 [2a0fbf3d] + CPUSummary v0.2.7 [95e48a1f] + CalibrateEmulateSample v1.1.0 [d360d2e6] + ChainRulesCore v1.26.1 [ae650224] + ChunkSplitters v3.2.0 [fb6a15b2] + CloseOpenIntervals v0.1.13 [523fee87] + CodecBzip2 v0.8.5 [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 [f70d9fcc] + CommonWorldInvalidations v1.0.0 [34da2185] + Compat v4.18.1 [a33af91c] + CompositionsBase v0.1.2 [992eb4ea] + CondaPkg v0.2.36 [88cd18e8] + ConsoleProgressMonitor v0.1.2 [187b0558] + ConstructionBase v1.6.0 [f65535da] + Convex v0.16.6 [adafc99b] + CpuId v0.3.1 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.19.5 [e2d170a0] + DataValueInterfaces v1.0.0 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.16.0 [a0c0ee7d] + DifferentiationInterface v0.7.18 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.126 [ffbed154] + DocStringExtensions v0.9.5 [fdbdab4c] + ElasticArrays v1.2.12 [2904ab23] + ElasticPDMats v0.2.4 [aa8a2aa5] + EnsembleKalmanProcesses v2.7.1 [4e289a0a] + EnumX v1.0.7 [c87230d0] + FFMPEG v0.4.5 [b86e33f2] + FFTA v0.3.1 [7a1cc6ca] + FFTW v1.10.0 [442a2c76] + FastGaussQuadrature v1.2.0 [1a297f60] + FillArrays v1.16.0 [6a86dc24] + FiniteDiff v2.31.0 ⌅ [53c48c17] + FixedPointNumbers v0.8.6 [f6369f11] + ForwardDiff v1.3.3 [069b7b12] + FunctionWrappers v1.1.3 [d9f16b24] + Functors v0.5.2 [891a1506] + GaussianProcesses v0.12.6 [e4b2fa32] + GaussianRandomFields v2.2.7 [8f48dd54] + Glossaries v0.1.1 [86223c79] + Graphs v1.14.0 [3e5b6fbb] + HostCPUFeatures v0.1.18 [34004b35] + HypergeometricFunctions v0.3.28 [615f187c] + IfElse v0.1.1 [d25df0c9] + Inflate v0.1.5 [22cec73e] + InitialValues v0.3.1 [18e54dd8] + IntegerMathUtils v0.1.3 [a98d9a8b] + Interpolations v0.16.2 [8197267c] + IntervalSets v0.7.14 [3587e190] + InverseFunctions v0.1.17 [92d709cd] + IrrationalConstants v0.2.6 [c8e1da08] + IterTools v1.10.0 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.8.0 [682c06a0] + JSON v1.6.1 [5ab0869b] + KernelDensity v0.6.12 ⌅ [ec8451be] + KernelFunctions v0.10.67 [2c470bb0] + Kronecker v0.5.5 [40e66cde] + LDLFactorizations v0.10.2 [b964fa9f] + LaTeXStrings v1.4.0 [10f19ff3] + LayoutPointers v0.1.17 ⌅ [1d6d02ad] + LeftChildRightSiblingTrees v0.2.1 ⌃ [d3d80556] + LineSearches v7.5.1 [7a12625a] + LinearMaps v3.11.4 [6fdf6af0] + LogDensityProblems v2.2.0 ⌅ [2ab3a3ac] + LogExpFunctions v0.3.29 [e6f89c97] + LoggingExtras v1.2.0 [bdcacae8] + LoopVectorization v0.12.174 [898213cb] + LowRankApprox v0.5.5 [e65ccdef] + LowRankMatrices v1.0.2 [c7f686f2] + MCMCChains v7.7.0 [be115224] + MCMCDiagnosticTools v0.3.17 [e80e1ace] + MLJModelInterface v1.12.1 [1914dd2f] + MacroTools v0.5.16 [af67fdf4] + ManifoldDiff v0.4.5 [1cead3c2] + Manifolds v0.11.26 [3362f125] + ManifoldsBase v2.4.0 [0fc0a36d] + Manopt v0.5.39 [d125e4d3] + ManualMemory v0.1.8 [b8f27783] + MathOptInterface v1.51.1 [99c1a7ee] + MatrixEquations v2.5.8 [0b3b1443] + MicroMamba v0.1.15 [e1d29d7a] + Missings v1.2.0 [46d2c3a1] + MuladdMacro v0.2.4 [d8a4904e] + MutableArithmetics v1.8.0 ⌅ [d41bc354] + 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[856f044c] + MKL_jll v2025.2.0+0 [e7412a2a] + Ogg_jll v1.3.6+0 [656ef2d0] + OpenBLAS32_jll v0.3.33+1 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [91d4177d] + Opus_jll v1.6.1+0 [30392449] + Pixman_jll v0.46.4+0 [f50d1b31] + Rmath_jll v0.5.1+0 [f4f2fc5b] + SCS_jll v300.200.1100+0 [4f6342f7] + Xorg_libX11_jll v1.8.13+0 [0c0b7dd1] + Xorg_libXau_jll v1.0.13+0 [a3789734] + Xorg_libXdmcp_jll v1.1.6+0 [1082639a] + Xorg_libXext_jll v1.3.8+0 [d091e8ba] + Xorg_libXfixes_jll v6.0.2+0 [ea2f1a96] + Xorg_libXrender_jll v0.9.12+0 [a65dc6b1] + Xorg_libpciaccess_jll v0.19.0+0 [c7cfdc94] + Xorg_libxcb_jll v1.17.1+0 [c5fb5394] + Xorg_xtrans_jll v1.6.0+0 [a4ae2306] + libaom_jll v3.13.3+0 [0ac62f75] + libass_jll v0.17.4+0 [8e53e030] + libdrm_jll v2.4.125+1 [f638f0a6] + libfdk_aac_jll v2.0.4+0 [b53b4c65] + libpng_jll v1.6.58+0 [9a156e7d] + libva_jll v2.23.0+0 [f27f6e37] + libvorbis_jll v1.3.8+0 [f8abcde7] + micromamba_jll v2.3.1+0 [1317d2d5] + oneTBB_jll v2022.3.0+0 [4d7b5844] + pixi_jll v0.63.2+0 ⌅ [1270edf5] + x264_jll v10164.0.1+0 [dfaa095f] + x265_jll v4.1.0+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 v1.13.0 [4af54fe1] + LazyArtifacts v1.11.0 [b27032c2] + LibCURL v1.0.0 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.14.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.14.0 [de0858da] + Printf v1.11.0 [9abbd945] + Profile v1.11.0 [3fa0cd96] + REPL v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v1.13.0 [9e88b42a] + Serialization v1.11.0 [1a1011a3] + SharedArrays v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.13.0 [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 v1.5.2+0 [deac9b47] + LibCURL_jll v8.20.0+1 [e37daf67] + LibGit2_jll v1.9.4+0 [29816b5a] + LibSSH2_jll v1.11.101+0 [14a3606d] + MozillaCACerts_jll v2026.5.14 [4536629a] + OpenBLAS_jll v0.3.33+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.6+0 [efcefdf7] + PCRE2_jll v10.47.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.2+0 [3161d3a3] + Zstd_jll v1.5.7+1 [8e850b90] + libblastrampoline_jll v5.15.0+0 [8e850ede] + nghttp2_jll v1.69.0+0 [3f19e933] + p7zip_jll v17.8.0+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 6.5s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompiling project... 4.1 s ✓ PDMats → StatsBaseExt 2.5 s ✓ ElasticPDMats 2.3 s ✓ FillArrays → FillArraysPDMatsExt 11.0 s ✓ GaussianRandomFields 14.1 s ✓ Distributions 5.9 s ✓ Distributions → DistributionsTestExt 5.0 s ✓ Distributions → DistributionsChainRulesCoreExt 7.3 s ✓ MCMCDiagnosticTools 9.4 s ✓ GaussianProcesses 7.2 s ✓ AdvancedMH 7.1 s ✓ KernelDensity 8.7 s ✓ AbstractGPs 24.8 s ✓ EnsembleKalmanProcesses 6.0 s ✓ AdvancedMH → AdvancedMHForwardDiffExt 12.5 s ✓ MCMCChains 58.7 s ✓ RandomFeatures 11.4 s ✓ AdvancedMH → AdvancedMHMCMCChainsExt  CondaPkg Found dependencies: /home/pkgeval/.julia/packages/CondaPkg/lKlVY/CondaPkg.toml  CondaPkg Found dependencies: /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/CondaPkg.toml  CondaPkg Found dependencies: /home/pkgeval/.julia/packages/PythonCall/JksWe/CondaPkg.toml  CondaPkg Resolving changes  + openssl  + python  + scikit-learn  + scipy  CondaPkg Initialising pixi  │ /home/pkgeval/.julia/artifacts/4ae17cee0bd922f66b2c72bf2f01c22481a5ec19/bin/pixi  │ init  │ --format pixi  └ /tmp/jl_CInDVf/.CondaPkg ✔ Created /tmp/jl_CInDVf/.CondaPkg/pixi.toml  CondaPkg Wrote /tmp/jl_CInDVf/.CondaPkg/pixi.toml  │ [dependencies]  │ openssl = ">=3, <3.6"  │ scikit-learn = "=1.5.1"  │ scipy = "=1.14.1"  │  │ [dependencies.python]  │ version = "=3.11, >=3.10,!=3.14.0,!=3.14.1,<4"  │ build = "*cp*"  │ channel = "conda-forge"  │  │ [workspace]  │ name = ".CondaPkg"  │ description = "automatically generated by CondaPkg.jl"  │ platforms = ["linux-64"]  │ channel-priority = "strict"  └ channels = ["conda-forge"]  CondaPkg Installing packages  │ /home/pkgeval/.julia/artifacts/4ae17cee0bd922f66b2c72bf2f01c22481a5ec19/bin/pixi  │ install  └ --manifest-path /tmp/jl_CInDVf/.CondaPkg/pixi.toml ✔ The default environment has been installed. 115.2 s ✓ CalibrateEmulateSample 18 dependencies successfully precompiled in 318 seconds. 338 already precompiled. 1 dependency had output during precompilation: ┌ CalibrateEmulateSample │ CondaPkg Found dependencies: /home/pkgeval/.julia/packages/CondaPkg/lKlVY/CondaPkg.toml │ CondaPkg Found dependencies: /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/CondaPkg.toml │ CondaPkg Found dependencies: /home/pkgeval/.julia/packages/PythonCall/JksWe/CondaPkg.toml │ CondaPkg Resolving changes │ + openssl │ + python │ + scikit-learn │ + scipy │ CondaPkg Initialising pixi │ │ /home/pkgeval/.julia/artifacts/4ae17cee0bd922f66b2c72bf2f01c22481a5ec19/bin/pixi │ │ init │ │ --format pixi │ └ /tmp/jl_CInDVf/.CondaPkg │ ✔ Created /tmp/jl_CInDVf/.CondaPkg/pixi.toml │ CondaPkg Wrote /tmp/jl_CInDVf/.CondaPkg/pixi.toml │ │ [dependencies] │ │ openssl = ">=3, <3.6" │ │ scikit-learn = "=1.5.1" │ │ scipy = "=1.14.1" │ │ │ │ [dependencies.python] │ │ version = "=3.11, >=3.10,!=3.14.0,!=3.14.1,<4" │ │ build = "*cp*" │ │ channel = "conda-forge" │ │ │ │ [workspace] │ │ name = ".CondaPkg" │ │ description = "automatically generated by CondaPkg.jl" │ │ platforms = ["linux-64"] │ │ channel-priority = "strict" │ └ channels = ["conda-forge"] │ CondaPkg Installing packages │ │ /home/pkgeval/.julia/artifacts/4ae17cee0bd922f66b2c72bf2f01c22481a5ec19/bin/pixi │ │ install │ └ --manifest-path /tmp/jl_CInDVf/.CondaPkg/pixi.toml │ ✔ The default environment has been installed. └ Precompilation completed after 340.42s ################################################################################ # Testing # Testing CalibrateEmulateSample Status `/tmp/jl_HRDNtQ/Project.toml` [99985d1d] AbstractGPs v0.5.24 [80f14c24] AbstractMCMC v5.15.1 [5b7e9947] AdvancedMH v0.8.10 [95e48a1f] CalibrateEmulateSample v1.1.0 [ae650224] ChunkSplitters v3.2.0 [992eb4ea] CondaPkg v0.2.36 [31c24e10] Distributions v0.25.126 [ffbed154] DocStringExtensions v0.9.5 [aa8a2aa5] EnsembleKalmanProcesses v2.7.1 [f6369f11] ForwardDiff v1.3.3 [891a1506] GaussianProcesses v0.12.6 ⌅ [ec8451be] KernelFunctions v0.10.67 [7a12625a] LinearMaps v3.11.4 [898213cb] LowRankApprox v0.5.5 [c7f686f2] MCMCChains v7.7.0 [1cead3c2] Manifolds v0.11.26 [0fc0a36d] Manopt v0.5.39 ⌅ [90014a1f] PDMats v0.11.36 [49802e3a] ProgressBars v1.5.1 [6099a3de] PythonCall v0.9.34 [36c3bae2] RandomFeatures v0.3.5 [37e2e3b7] ReverseDiff v1.16.2 [860ef19b] StableRNGs v1.0.4 [10745b16] Statistics v1.11.1 [2913bbd2] StatsBase v0.34.11 [9449cd9e] TSVD v0.4.4 [37e2e46d] LinearAlgebra v1.14.0 [44cfe95a] Pkg v1.14.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_HRDNtQ/Manifest.toml` [47edcb42] ADTypes v1.22.0 [14f7f29c] AMD v0.5.3 [621f4979] AbstractFFTs v1.5.0 [99985d1d] AbstractGPs v0.5.24 [80f14c24] AbstractMCMC v5.15.1 [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.44 [79e6a3ab] Adapt v4.6.0 [5b7e9947] AdvancedMH v0.8.10 [66dad0bd] AliasTables v1.1.3 [dce04be8] ArgCheck v2.5.0 [ec485272] ArnoldiMethod v0.4.0 [7d9fca2a] Arpack v0.5.4 [4fba245c] ArrayInterface v7.25.0 [13072b0f] AxisAlgorithms v1.1.0 [39de3d68] 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Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... [ Info: [in test/runtest.jl], create plots? CES_TEST_PLOT_OUTPUT: false Starting tests for Emulator CondaPkg Found dependencies: /home/pkgeval/.julia/packages/CondaPkg/lKlVY/CondaPkg.toml CondaPkg Found dependencies: /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/CondaPkg.toml CondaPkg Found dependencies: /home/pkgeval/.julia/packages/PythonCall/JksWe/CondaPkg.toml CondaPkg Resolving changes + openssl + python + scikit-learn + scipy CondaPkg Initialising pixi │ /home/pkgeval/.julia/artifacts/4ae17cee0bd922f66b2c72bf2f01c22481a5ec19/bin/pixi │ init │ --format pixi └ /tmp/jl_HRDNtQ/.CondaPkg ✔ Created /tmp/jl_HRDNtQ/.CondaPkg/pixi.toml CondaPkg Wrote /tmp/jl_HRDNtQ/.CondaPkg/pixi.toml │ [dependencies] │ openssl = ">=3, <3.6" │ scikit-learn = "=1.5.1" │ scipy = "=1.14.1" │ │ [dependencies.python] │ version = "=3.11, >=3.10,!=3.14.0,!=3.14.1,<4" │ build = "*cp*" │ channel = "conda-forge" │ │ [workspace] │ name = ".CondaPkg" │ description = "automatically generated by CondaPkg.jl" │ platforms = ["linux-64"] │ channel-priority = "strict" └ channels = ["conda-forge"] CondaPkg Installing packages │ /home/pkgeval/.julia/artifacts/4ae17cee0bd922f66b2c72bf2f01c22481a5ec19/bin/pixi │ install └ --manifest-path /tmp/jl_HRDNtQ/.CondaPkg/pixi.toml ✔ The default environment has been installed. [ Info: fit successful [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 6, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 6, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 1 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 2 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 3 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 4 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 5 kernel in GaussianProcess: Type: GaussianProcesses.SumKernel{GaussianProcesses.SEArd{Float64}, GaussianProcesses.Noise{Float64}} Type: GaussianProcesses.SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 6, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat ┌ Warning: GaussianProcess already built. skipping... └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MachineLearningTools/GaussianProcess.jl:188 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 6, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 6, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 Completed tests for Emulator, 296 seconds elapsed Starting tests for GaussianProcess Using user-defined kernelType: SEIso{Float64}, Params: [0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: SumKernel{SEIso{Float64}, Noise{Float64}} Type: SEIso{Float64}, Params: [0.0, 0.0] Type: Noise{Float64}, Params: [0.0] created GP: 1 ┌ Warning: GaussianProcess already built. skipping... └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MachineLearningTools/GaussianProcess.jl:188 optimized hyperparameters of GP: 1 Type: SumKernel{SEIso{Float64}, Noise{Float64}} Type: SEIso{Float64}, Params: [0.4671112501513754, -0.11637219099834126] Type: Noise{Float64}, Params: [-2.779564795897494] optimised GP: 1 Sum of 2 kernels: Squared Exponential Kernel (metric = Distances.Euclidean(0.0)) - ARD Transform (dims: 1) - σ² = 0.7923560881211849 White Kernel - σ² = 0.0038521278625259676 [ Info: AbstractGP already built. Continuing... Using user-defined kernelType: SEIso{Float64}, Params: [0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: SumKernel{SEIso{Float64}, Noise{Float64}} Type: SEIso{Float64}, Params: [0.0, 0.0] Type: Noise{Float64}, Params: [0.0] created GP: 1 optimized hyperparameters of GP: 1 Type: SumKernel{SEIso{Float64}, Noise{Float64}} Type: SEIso{Float64}, Params: [0.46711125015097044, -0.11637219099977898] Type: Noise{Float64}, Params: [-2.9126145296277137] Using user-defined kernel1**2 * RBF(length_scale=1) Learning additive white noise [ Info: Training kernel 1, [ Info: 1**2 * RBF(length_scale=1) + WhiteKernel(noise_level=1) ┌ Warning: GaussianProcess already built. skipping... └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MachineLearningTools/GaussianProcess.jl:334 SKlearn, already trained. continuing... Using user-defined kernel1**2 * RBF(length_scale=1) Learning additive white noise [ Info: Training kernel 1, [ Info: 1**2 * RBF(length_scale=1) + WhiteKernel(noise_level=1) ┌ Warning: `SKLJL` is deprecated, use `SKLPy` instead. │ caller = top-level scope at runtests.jl:20 └ @ Core ~/.julia/packages/CalibrateEmulateSample/yapkx/test/GaussianProcess/runtests.jl:20 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 100, while the space dimension is 2, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: SumKernel{SEArd{Float64}, Noise{Float64}} Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0] Type: Noise{Float64}, Params: [0.0] created GP: 1 kernel in GaussianProcess: Type: SumKernel{SEArd{Float64}, Noise{Float64}} Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0] Type: Noise{Float64}, Params: [0.0] created GP: 2 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 100, while the space dimension is 2, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0] kernel in GaussianProcess: Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0] created GP: 1 kernel in GaussianProcess: Type: SEArd{Float64}, Params: [-0.0, -0.0, 0.0] created GP: 2 optimized hyperparameters of GP: 1 Type: SEArd{Float64}, Params: [-0.08095883666729817, 0.6591588380894285, 2.0163237790280433] optimized hyperparameters of GP: 2 Type: SEArd{Float64}, Params: [0.48546387823260384, 0.08009132351645844, 2.348678772768728] optimized hyperparameters of GP: 1 Type: SumKernel{SEArd{Float64}, Noise{Float64}} Type: SEArd{Float64}, Params: [-0.06076669570339724, 0.6629187475773616, 2.0713964932483395] Type: Noise{Float64}, Params: [-0.22010761514599403] optimized hyperparameters of GP: 2 Type: SumKernel{SEArd{Float64}, Noise{Float64}} Type: SEArd{Float64}, Params: [0.4806217980287883, 0.07991481116088309, 2.344620786302477] Type: Noise{Float64}, Params: [-0.09161176738899532] ┌ Warning: `transform_to_real` keyword is deprecated. Please use the `encode` and `add_obs_noise_cov` keywords instead. │ │ Recommended usage for users is now set by default as: │ - `encode=nothing`, `add_obs_noise_cov=false` │ This behaviour takes in non-encoded inputs, and returns non-encoded outputs. It gives only the uncertainty from the Machine Learning Tool (not inflated by observational noise) │ │ This simulation will continue with the old behavior: │ - `transform_to_real=true` replaced with `encode=nothing, add_obs_noise_cov=true` │ - `transform_to_real=false` replaced with `encode="out", add_obs_noise_cov=true` │ └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Emulator.jl:600 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 100, while the space dimension is 2, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat optimised GP: 1 Sum of 2 kernels: Squared Exponential Kernel (metric = Distances.Euclidean(0.0)) - ARD Transform (dims: 2) - σ² = 62.9784740649691 White Kernel - σ² = 0.6438978198523074 optimised GP: 2 Sum of 2 kernels: Squared Exponential Kernel (metric = Distances.Euclidean(0.0)) - ARD Transform (dims: 2) - σ² = 108.7706538727328 White Kernel - σ² = 0.8325820238965255 Completed tests for GaussianProcess, 104 seconds elapsed Starting tests for RandomFeature ┌ Info: Shrinkage scale: 0.9695302686500062, (0 = none, 1 = revert to scaled Identity) └ shrinkage covariance condition number: 1.1734122152074837 [ Info: NICE-adjusted covariance condition number: 2.6041819192515727 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 1, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat [ Info: hyperparameter learning for 1 models using 50 training points, 50 validation points and 100 features estimating covariances with 520 iterations... [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 1, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat [ Info: hyperparameter learning using 50 training points, 50 validation points and 100 features estimating covariances with 520 iterations... [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 1, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 1, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: hyperparameter learning for 1 models using 40 training points, 10 validation points and 100 features [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 1, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 1, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 1, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 1, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 1, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat ┌ Warning: ScalarRandomFeatureInterface already built. skipping... └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MachineLearningTools/ScalarRandomFeature.jl:356 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 1, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat ┌ Warning: VectorRandomFeatureInterface already built. skipping... └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MachineLearningTools/VectorRandomFeature.jl:383 [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("n_features_opt" => 100, "n_cross_val_sets" => 2, "n_iteration" => 10, "cov_sample_multiplier" => 10.0, "inflation" => 0.0001, "n_ensemble" => 30, "train_fraction" => 0.8, "overfit" => 1.0, "cov_correction" => "nice", "scheduler" => DataMisfitController (T=1000.0, "stop"), "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "verbose" => true, "multithread" => "ensemble", "accelerator" => NesterovAccelerator (θ_prev=1.0)) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("n_features_opt" => 100, "n_cross_val_sets" => 2, "n_iteration" => 10, "cov_sample_multiplier" => 10.0, "inflation" => 0.0001, "n_ensemble" => 70, "train_fraction" => 0.8, "overfit" => 1.0, "cov_correction" => "shrinkage", "scheduler" => DataMisfitController (T=1000.0, "stop"), "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "verbose" => true, "multithread" => "ensemble", "accelerator" => NesterovAccelerator (θ_prev=1.0)) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("n_features_opt" => 100, "n_cross_val_sets" => 2, "n_iteration" => 10, "cov_sample_multiplier" => 10.0, "inflation" => 0.0001, "n_ensemble" => 100, "train_fraction" => 0.8, "overfit" => 1.0, "cov_correction" => "nice", "scheduler" => DataMisfitController (T=1000.0, "stop"), "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "verbose" => true, "multithread" => "ensemble", "accelerator" => NesterovAccelerator (θ_prev=1.0)) [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 100, while the space dimension is 2, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat [ Info: hyperparameter learning for 2 models using 80 training points, 20 validation points and 100 features estimating covariances with 220 iterations... estimating covariances with 220 iterations... [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 100, while the space dimension is 2, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat [ Info: hyperparameter learning for 2 models using 80 training points, 20 validation points and 100 features [ Info: training model 1 / 2 estimating covariances with 220 iterations... estimate cov with 220 iterations... [ Info: NICE-adjusted covariance condition number: 1.4450373610612748e6 estimate cov with 220 iterations... [ Info: NICE-adjusted covariance condition number: 1.5797096201254022e6 ┌ Info: Initializing ensemble Kalman process of type TransformInversion │ Number of ensemble members: 30 │ Localization: NoLocalization │ Failure handler: SampleSuccGauss │ Scheduler: DataMisfitController └ Accelerator: NesterovAccelerator [ Info: Iteration 0 (prior) [ Info: Covariance trace: 0.0037326842632157504 [ Info: Iteration 1 (T=0.04900629729124674) ┌ Info: Covariance-weighted error: 6.458602997394118 │ Covariance trace: 0.003268791611484034 └ Covariance trace ratio (current/previous): 0.8757214328832443 [ Info: Iteration 2 (T=0.09260982185048838) ┌ Info: Covariance-weighted error: 6.5868768274475356 │ Covariance trace: 0.002123684375711355 └ Covariance trace ratio (current/previous): 0.6496848463053907 [ Info: Iteration 3 (T=0.1574309969865294) ┌ Info: Covariance-weighted error: 4.842803743396995 │ Covariance trace: 0.0012526311370023209 └ Covariance trace ratio (current/previous): 0.589838655559509 [ Info: Iteration 4 (T=0.3881416807781368) ┌ Info: Covariance-weighted error: 4.36076557377378 │ Covariance trace: 0.0007646066809060819 └ Covariance trace ratio (current/previous): 0.610400506837046 [ Info: Iteration 5 (T=0.9444609025896349) ┌ Info: Covariance-weighted error: 4.111579209664371 │ Covariance trace: 0.00040516714563797434 └ Covariance trace ratio (current/previous): 0.5299027012919101 [ Info: Iteration 6 (T=2.1445437275689634) ┌ Info: Covariance-weighted error: 4.001169209306949 │ Covariance trace: 0.00012495485634838384 └ Covariance trace ratio (current/previous): 0.3084032298611737 [ Info: Iteration 7 (T=3.891212050841548) ┌ Info: Covariance-weighted error: 3.976370393792952 │ Covariance trace: 5.954208840514999e-5 └ Covariance trace ratio (current/previous): 0.47650879801855817 [ Info: Iteration 8 (T=5.767944937566489) ┌ Info: Covariance-weighted error: 3.976381498364215 │ Covariance trace: 6.042878350054954e-5 └ Covariance trace ratio (current/previous): 1.0148919045191378 [ Info: Iteration 9 (T=7.374807030826902) ┌ Info: Covariance-weighted error: 3.940226915233263 │ Covariance trace: 6.922563084709237e-5 └ Covariance trace ratio (current/previous): 1.145573795084967 [ Info: Iteration 10 (T=8.87770883477228) ┌ Info: Covariance-weighted error: 3.943920508680159 │ Covariance trace: 6.324130022772125e-5 └ Covariance trace ratio (current/previous): 0.9135532526588384 [ Info: EKI Optimization result: 2×4 Matrix{Any}: "name" "number of hyperparameters" "optimized value range" "99% prior mass" "input_cholesky" 3 (-0.00425971, 0.188713) (-0.1, 0.1) nothing [ Info: training model 2 / 2 estimating covariances with 220 iterations... estimate cov with 220 iterations... [ Info: NICE-adjusted covariance condition number: 73576.21304603336 estimate cov with 220 iterations... [ Info: NICE-adjusted covariance condition number: 49758.312187429954 ┌ Info: Initializing ensemble Kalman process of type TransformInversion │ Number of ensemble members: 30 │ Localization: NoLocalization │ Failure handler: SampleSuccGauss │ Scheduler: DataMisfitController └ Accelerator: NesterovAccelerator [ Info: Iteration 0 (prior) [ Info: Covariance trace: 0.00269964860789342 [ Info: Iteration 1 (T=0.1514911843968429) ┌ Info: Covariance-weighted error: 20.189402644146533 │ Covariance trace: 0.002062732938187031 └ Covariance trace ratio (current/previous): 0.7640746029523506 [ Info: Iteration 2 (T=0.40351807008688906) ┌ Info: Covariance-weighted error: 20.208337640649585 │ Covariance trace: 0.0012434679651460194 └ Covariance trace ratio (current/previous): 0.6028254759139704 [ Info: Iteration 3 (T=0.5064291806251839) ┌ Info: Covariance-weighted error: 20.320658555927075 │ Covariance trace: 0.0007032954586947026 └ Covariance trace ratio (current/previous): 0.565591939967762 [ Info: Iteration 4 (T=0.994608081157873) ┌ Info: Covariance-weighted error: 20.264525695468652 │ Covariance trace: 0.000583515423768359 └ Covariance trace ratio (current/previous): 0.8296874614423757 [ Info: Iteration 5 (T=1.7291057692079042) ┌ Info: Covariance-weighted error: 20.197311294110087 │ Covariance trace: 0.0004617062038724336 └ Covariance trace ratio (current/previous): 0.7912493570276549 [ Info: Iteration 6 (T=2.6244435542692734) ┌ Info: Covariance-weighted error: 20.201328890355033 │ Covariance trace: 0.00030573425815326764 └ Covariance trace ratio (current/previous): 0.6621835608640424 [ Info: Iteration 7 (T=3.285211918462638) ┌ Info: Covariance-weighted error: 20.288115906899648 │ Covariance trace: 0.00019860751884031165 └ Covariance trace ratio (current/previous): 0.6496083233850349 [ Info: Iteration 8 (T=3.5770691326424506) ┌ Info: Covariance-weighted error: 20.421819626435713 │ Covariance trace: 0.000194104239885609 └ Covariance trace ratio (current/previous): 0.9773257378119533 [ Info: Iteration 9 (T=3.7983259328638366) ┌ Info: Covariance-weighted error: 20.807487115732783 │ Covariance trace: 0.0002716181547144903 └ Covariance trace ratio (current/previous): 1.3993416881288239 [ Info: Iteration 10 (T=3.996427357572677) ┌ Info: Covariance-weighted error: 20.991308730716007 │ Covariance trace: 0.0003187088286002509 └ Covariance trace ratio (current/previous): 1.1733708629869004 [ Info: EKI Optimization result: 2×4 Matrix{Any}: "name" "number of hyperparameters" "optimized value range" "99% prior mass" "input_cholesky" 3 (-0.0615047, 0.0412954) (-0.1, 0.1) nothing [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 100, while the space dimension is 2, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat [ Info: hyperparameter learning using 80 training points, 20 validation points and 100 features RF output structure matrix is not positive definite, correcting for use as a regularizer estimating covariances with 220 iterations... approx_σ2 not posdef approx_σ2 not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef blockcovmat not posdef [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 100, while the space dimension is 2, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat [ Info: hyperparameter learning using 80 training points, 20 validation points and 100 features RF output structure matrix is not positive definite, correcting for use as a regularizer estimating covariances with 420 iterations... estimate cov with 420 iterations... ┌ Info: Shrinkage scale: 0.019613111711241388, (0 = none, 1 = revert to scaled Identity) └ shrinkage covariance condition number: 617.7084799437175 approx_σ2 not posdef estimate cov with 420 iterations... ┌ Info: Shrinkage scale: 0.014902976819321907, (0 = none, 1 = revert to scaled Identity) └ shrinkage covariance condition number: 1118.614723866836 approx_σ2 not posdef ┌ Info: Initializing ensemble Kalman process of type TransformInversion │ Number of ensemble members: 70 │ Localization: NoLocalization │ Failure handler: SampleSuccGauss │ Scheduler: DataMisfitController └ Accelerator: NesterovAccelerator blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 0 (prior) [ Info: Covariance trace: 10.81173932420858 [ Info: Iteration 1 (T=0.0941977063879166) ┌ Info: Covariance-weighted error: 0.8717514649741129 │ Covariance trace: 4.023049677614634 └ Covariance trace ratio (current/previous): 0.37210013643286965 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 2 (T=0.29662709850000424) ┌ Info: Covariance-weighted error: 0.636356091687693 │ Covariance trace: 2.520207677262036 └ Covariance trace ratio (current/previous): 0.6264420972192244 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 3 (T=0.5109212507644515) ┌ Info: Covariance-weighted error: 0.618738883128214 │ Covariance trace: 1.780181920371887 └ Covariance trace ratio (current/previous): 0.7063631844443408 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 4 (T=1.689738788986221) ┌ Info: Covariance-weighted error: 0.5850127320557361 │ Covariance trace: 0.7851722035839324 └ Covariance trace ratio (current/previous): 0.4410629018296663 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 5 (T=2.6537711968998527) ┌ Info: Covariance-weighted error: 0.5911736229932223 │ Covariance trace: 0.3214747138919523 └ Covariance trace ratio (current/previous): 0.4094321123755723 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 6 (T=3.6903530654113585) ┌ Info: Covariance-weighted error: 0.5800494940320247 │ Covariance trace: 0.16399543254479587 └ Covariance trace ratio (current/previous): 0.5101347803047264 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 7 (T=4.63256456038489) ┌ Info: Covariance-weighted error: 0.5899204430802286 │ Covariance trace: 0.10627925576600866 └ Covariance trace ratio (current/previous): 0.6480622912286179 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 8 (T=5.603709955533751) ┌ Info: Covariance-weighted error: 0.5931397081938227 │ Covariance trace: 0.08135487031347619 └ Covariance trace ratio (current/previous): 0.7654821228010137 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 9 (T=6.641450156844092) ┌ Info: Covariance-weighted error: 0.5895114709587169 │ Covariance trace: 0.07085662090472637 └ Covariance trace ratio (current/previous): 0.8709573333680207 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 10 (T=7.678965062827431) ┌ Info: Covariance-weighted error: 0.5912071056770408 │ Covariance trace: 0.06613957731990001 └ Covariance trace ratio (current/previous): 0.933428329990942 [ Info: EKI Optimization result: 5×4 Matrix{Any}: "name" "number of hyperparameters" "optimized value range" "99% prior mass" "input_lowrank_Kchol" 1 (1.49069, 1.49069) (-3.0, 3.0) "input_lowrank_U" 2 (-0.0452195, 0.210291) (-0.67082, 0.67082) "output_lowrank_diagonal" 2 (0.0293724, 0.265316) (0.000554271, 90.2094) "output_lowrank_U" 2 (0.00191575, 0.106672) (-0.67082, 0.67082) nothing [ Info: hyperparameter learning using 80 training points, 20 validation points and 100 features RF output structure matrix is not positive definite, correcting for use as a regularizer estimating covariances with 420 iterations... estimate cov with 420 iterations... ┌ Info: Shrinkage scale: 0.009915891393390382, (0 = none, 1 = revert to scaled Identity) └ shrinkage covariance condition number: 2630.0350769105066 approx_σ2 not posdef estimate cov with 420 iterations... ┌ Info: Shrinkage scale: 0.00818107199245484, (0 = none, 1 = revert to scaled Identity) └ shrinkage covariance condition number: 3903.2097830955477 approx_σ2 not posdef ┌ Info: Initializing ensemble Kalman process of type TransformInversion │ Number of ensemble members: 70 │ Localization: NoLocalization │ Failure handler: SampleSuccGauss │ Scheduler: DataMisfitController └ Accelerator: NesterovAccelerator blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 0 (prior) [ Info: Covariance trace: 10.81173932420858 [ Info: Iteration 1 (T=0.08854868209149548) ┌ Info: Covariance-weighted error: 1.071173789682758 │ Covariance trace: 3.8199825899767883 └ Covariance trace ratio (current/previous): 0.35331804397313393 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 2 (T=0.23284831940231032) ┌ Info: Covariance-weighted error: 0.9114980732128777 │ Covariance trace: 1.9540924409337668 └ Covariance trace ratio (current/previous): 0.5115448552203062 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 3 (T=0.4042946702576427) ┌ Info: Covariance-weighted error: 0.8823817127004808 │ Covariance trace: 1.0471642026472971 └ Covariance trace ratio (current/previous): 0.5358826331403789 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 4 (T=0.6589056411522141) ┌ Info: Covariance-weighted error: 0.7988059406061185 │ Covariance trace: 0.7235066430081222 └ Covariance trace ratio (current/previous): 0.6909199542717863 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 5 (T=1.304341888184563) ┌ Info: Covariance-weighted error: 0.7262191241591299 │ Covariance trace: 0.4254661242985846 └ Covariance trace ratio (current/previous): 0.5880611165221993 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 6 (T=2.1487662113178962) ┌ Info: Covariance-weighted error: 0.7281659039634119 │ Covariance trace: 0.23582539215340134 └ Covariance trace ratio (current/previous): 0.5542753669100651 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 7 (T=3.0103884242042653) ┌ Info: Covariance-weighted error: 0.7196513200357553 │ Covariance trace: 0.13677984512655667 └ Covariance trace ratio (current/previous): 0.580004739428497 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 8 (T=3.7737968736439287) ┌ Info: Covariance-weighted error: 0.7124331051111917 │ Covariance trace: 0.0829054321224784 └ Covariance trace ratio (current/previous): 0.6061231612433066 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 9 (T=4.439681257928897) ┌ Info: Covariance-weighted error: 0.7193949747573056 │ Covariance trace: 0.05822012499313581 └ Covariance trace ratio (current/previous): 0.702247410122966 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 10 (T=5.067142244830865) ┌ Info: Covariance-weighted error: 0.7210806571618208 │ Covariance trace: 0.04515770805404166 └ Covariance trace ratio (current/previous): 0.7756374287991615 [ Info: EKI Optimization result: 5×4 Matrix{Any}: "name" "number of hyperparameters" "optimized value range" "99% prior mass" "input_lowrank_Kchol" 1 (-0.913754, -0.913754) (-3.0, 3.0) "input_lowrank_U" 2 (-0.0347712, 0.115204) (-0.67082, 0.67082) "output_lowrank_diagonal" 2 (0.294504, 0.499979) (0.000554271, 90.2094) "output_lowrank_U" 2 (-0.222387, 0.484032) (-0.67082, 0.67082) nothing [ Info: hyperparameter learning using 80 training points, 20 validation points and 100 features RF output structure matrix is not positive definite, correcting for use as a regularizer estimating covariances with 420 iterations... estimate cov with 420 iterations... [ Info: NICE-adjusted covariance condition number: 192088.086595586 approx_σ2 not posdef estimate cov with 420 iterations... [ Info: NICE-adjusted covariance condition number: 5.12290745000525e7 approx_σ2 not posdef ┌ Info: Initializing ensemble Kalman process of type TransformInversion │ Number of ensemble members: 100 │ Localization: NoLocalization │ Failure handler: SampleSuccGauss │ Scheduler: DataMisfitController └ Accelerator: NesterovAccelerator blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 0 (prior) [ Info: Covariance trace: 17.20122191075149 [ Info: Iteration 1 (T=0.10356691350377024) ┌ Info: Covariance-weighted error: 0.6169441263322528 │ Covariance trace: 6.164415194620439 └ Covariance trace ratio (current/previous): 0.35837077311161364 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 2 (T=0.24808221996671942) ┌ Info: Covariance-weighted error: 0.5239915735375407 │ Covariance trace: 2.585130331416895 └ Covariance trace ratio (current/previous): 0.41936343510295776 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 3 (T=0.6052830689022097) ┌ Info: Covariance-weighted error: 0.4920343984279715 │ Covariance trace: 0.8141216995038851 └ Covariance trace ratio (current/previous): 0.3149248181455013 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 4 (T=1.0168513072583016) ┌ Info: Covariance-weighted error: 0.5066319424350585 │ Covariance trace: 0.2504295242275076 └ Covariance trace ratio (current/previous): 0.30760698846390655 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 5 (T=1.7337862062429998) ┌ Info: Covariance-weighted error: 0.48275952145308465 │ Covariance trace: 0.10502613605283198 └ Covariance trace ratio (current/previous): 0.41938400185362706 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 6 (T=2.398512775874971) ┌ Info: Covariance-weighted error: 0.48264853156420495 │ Covariance trace: 0.07521600419390419 └ Covariance trace ratio (current/previous): 0.7161646331163491 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 7 (T=3.062796099198425) ┌ Info: Covariance-weighted error: 0.46524503446241156 │ Covariance trace: 0.0770313725005453 └ Covariance trace ratio (current/previous): 1.0241353994551632 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 8 (T=3.682950933458334) ┌ Info: Covariance-weighted error: 0.4547915544396698 │ Covariance trace: 0.0791857681561409 └ Covariance trace ratio (current/previous): 1.027967769308282 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 9 (T=4.183659918829124) ┌ Info: Covariance-weighted error: 0.43816489255459035 │ Covariance trace: 0.07782376051474224 └ Covariance trace ratio (current/previous): 0.9827998430385495 blockcovmat not posdef blockcovmat not posdef [ Info: Iteration 10 (T=4.810747722092995) ┌ Info: Covariance-weighted error: 0.4099704380039426 │ Covariance trace: 0.07260881599016553 └ Covariance trace ratio (current/previous): 0.9329903298159328 [ Info: EKI Optimization result: 3×4 Matrix{Any}: "name" "number of hyperparameters" "optimized value range" "99% prior mass" "full_lowrank_diagonal" 4 (0.432809, 0.625085) (0.000391928, 63.7877) "full_lowrank_U" 8 (-0.054687, 0.0757021) (-0.237171, 0.237171) nothing [ Info: [0.056236684000692684, 0.013532418781178161, 0.043061100565817065, 0.045023171420370205, 0.102757442171512, 0.10693291341444144] [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 100, while the space dimension is 2, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat [ Info: hyperparameter learning using 80 training points, 20 validation points and 100 features RF output structure matrix is not positive definite, correcting for use as a regularizer estimating covariances with 420 iterations... 0.0%┣ ┫ 0/420 [00:00<00:00, -0s/it]  0.2%┣ ┫ 1/420 [00:04 0.15 [ Info: Injecting nullspace noise: 0.20000000000184537 > 0.0 [ Info: Initialize encoding of data: "in" with ElementwiseScaler: MinMaxScaling Test the encode-decode for posterior samples: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:556 Got exception outside of a @test MethodError: no method matching +(::Vector{Float64}, ::Float64) For element-wise addition, use broadcasting with dot syntax: array .+ scalar The function `+` exists, but no method is defined for this combination of argument types. Closest candidates are: +(::Any, ::Any, !Matched::Any, !Matched::Any...) @ Base operators.jl:653 +(!Matched::Complex{Bool}, ::Real) @ Base complex.jl:316 +(!Matched::Py, ::Number) @ PythonCall ~/.julia/packages/PythonCall/JksWe/src/Core/Py.jl:429 ... Stacktrace: [1] (::Base.Splat{typeof(+)})(args::Tuple{Vector{Float64}, Float64}) @ Base operators.jl:1366 [2] iterate(::Base.Generator{Base.Iterators.Zip{Tuple{Matrix{Vector{Float64}}, Matrix{Float64}}}, Base.Splat{typeof(+)}}) @ Base generator.jl:48 [inlined] [3] collect(itr::Base.Generator{Base.Iterators.Zip{Tuple{Matrix{Vector{Float64}}, Matrix{Float64}}}, Base.Splat{typeof(+)}}) @ Base array.jl:833 [inlined] [4] map(f::typeof(+), it::Matrix{Vector{Float64}}, iters::Matrix{Float64}) @ Base abstractarray.jl:3622 [inlined] [5] _broadcast_preserving_zero_d(::typeof(+), ::Matrix{Vector{Float64}}, ::Matrix{Float64}) @ Base arraymath.jl:13 [inlined] [6] +(::Matrix{Vector{Float64}}, ::Matrix{Float64}) @ Base arraymath.jl:49 [7] create_noise_injector(encoder_schedule::Vector{Any}, prior::ParameterDistribution{Parameterized, Constraint{BoundedAbove}, String}, noise_injector_threshold::Float64, noise_injector_scaling::Float64) @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:1185 [8] top-level scope @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303 [9] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [10] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:559 [inlined] [11] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [12] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:640 [inlined] [13] include(mapexpr::Function, mod::Module, _path::String) @ Base Base.jl:327 [14] macro expansion @ timing.jl:505 [inlined] [15] include_test(_module::String) @ Main ~/.julia/packages/CalibrateEmulateSample/yapkx/test/runtests.jl:15 [16] top-level scope @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/runtests.jl:21 [17] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [18] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/runtests.jl:33 [inlined] [19] include(mapexpr::Function, mod::Module, _path::String) @ Base Base.jl:327 [20] top-level scope @ none:6 [21] eval(m::Module, e::Any) @ Core boot.jl:521 [22] exec_options(opts::Base.JLOptions) @ Base client.jl:321 [23] _start() @ Base client.jl:596 Autodiff MCMC variants: Test Failed at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:657 Expression: mcmc_test_template(prior, σ2_y, em_1; bad_mcmc_params...) Expected: ArgumentError Thrown: TypeError TypeError: in DataContainer, in FT, expected FT<:Real, got Type{Vector{Float64}} Stacktrace: [1] DataContainer(data::Matrix{Vector{Float64}}; data_are_columns::Bool) @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/DataContainers.jl:0 [2] DataContainer(data::Matrix{Vector{Float64}}) @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/DataContainers.jl:30 [3] encode_data(encoder_schedule::Vector{Any}, data::Matrix{Vector{Float64}}, in_or_out::String) @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:822 [4] MCMCWrapper(mcmc_alg::BarkerSampling{GradFreeProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; init_params::Vector{Any}, burnin::Int64, kwargs::@Kwargs{}) @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:603 [5] MCMCWrapper(mcmc_alg::BarkerSampling{GradFreeProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}) @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:571 [inlined] [6] MCMCWrapper(mcmc_alg::BarkerSampling{GradFreeProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; kwargs::@Kwargs{}) @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:655 [inlined] [7] MCMCWrapper(mcmc_alg::BarkerSampling{GradFreeProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}) @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:648 [inlined] [8] mcmc_test_template(prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, σ2_y::UniformScaling{Float64}, em::Emulator{Float64, Vector{Any}}; exp_name::String, mcmc_alg::BarkerSampling{GradFreeProtocol}, obs_sample::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, init_params::Vector{Float64}, step::Float64, rng::MersenneTwister, target_acc::Float64, return_samples::Bool) @ Main ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:260 [9] top-level scope @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303 [10] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [11] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined] [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [13] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:657 [inlined] [14] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:980 [inlined] [15] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:657 [inlined] Stacktrace: [1] top-level scope @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303 [2] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [3] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined] [4] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [5] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:657 [inlined] Autodiff MCMC variants: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:658 Test threw exception Expression: contains(thrown.value.msg, "autodiff_gradient") FieldError: type String has no field `msg`; String has no fields at all. Stacktrace: [1] getproperty(x::String, f::Symbol) @ Base Base_compiler.jl:58 [2] top-level scope @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303 [3] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [4] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined] [5] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [6] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:658 [inlined] [7] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined] Autodiff MCMC variants: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:659 Test threw exception Expression: contains(thrown.value.msg, "GradFreeProtocol") FieldError: type String has no field `msg`; String has no fields at all. Stacktrace: [1] getproperty(x::String, f::Symbol) @ Base Base_compiler.jl:58 [2] top-level scope @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303 [3] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [4] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined] [5] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [6] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:659 [inlined] [7] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined] Autodiff MCMC variants: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:660 Test threw exception Expression: contains(thrown.value.msg, "ForwardDiffProtocol") FieldError: type String has no field `msg`; String has no fields at all. Stacktrace: [1] getproperty(x::String, f::Symbol) @ Base Base_compiler.jl:58 [2] top-level scope @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303 [3] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [4] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined] [5] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [6] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:660 [inlined] [7] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined] Autodiff MCMC variants: Test Failed at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:666 Expression: mcmc_test_template(prior, σ2_y, em_1; bad_mcmc_params...) Expected: ArgumentError Thrown: TypeError TypeError: in DataContainer, in FT, expected FT<:Real, got Type{Vector{Float64}} Stacktrace: [1] DataContainer(data::Matrix{Vector{Float64}}; data_are_columns::Bool) @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/DataContainers.jl:0 [2] DataContainer(data::Matrix{Vector{Float64}}) @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/DataContainers.jl:30 [3] encode_data(encoder_schedule::Vector{Any}, data::Matrix{Vector{Float64}}, in_or_out::String) @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:822 [4] MCMCWrapper(mcmc_alg::BarkerSampling{ForwardDiffProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; init_params::Vector{Any}, burnin::Int64, kwargs::@Kwargs{}) @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:603 [5] MCMCWrapper(mcmc_alg::BarkerSampling{ForwardDiffProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}) @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:571 [inlined] [6] MCMCWrapper(mcmc_alg::BarkerSampling{ForwardDiffProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; kwargs::@Kwargs{}) @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:655 [inlined] [7] MCMCWrapper(mcmc_alg::BarkerSampling{ForwardDiffProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}) @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:648 [inlined] [8] mcmc_test_template(prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, σ2_y::UniformScaling{Float64}, em::Emulator{Float64, Vector{Any}}; exp_name::String, mcmc_alg::BarkerSampling{ForwardDiffProtocol}, obs_sample::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, init_params::Vector{Float64}, step::Float64, rng::MersenneTwister, target_acc::Float64, return_samples::Bool) @ Main ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:260 [9] top-level scope @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303 [10] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [11] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined] [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [13] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:666 [inlined] [14] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:980 [inlined] [15] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:666 [inlined] Stacktrace: [1] top-level scope @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303 [2] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [3] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined] [4] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [5] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:666 [inlined] Autodiff MCMC variants: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:667 Test threw exception Expression: contains(thrown.value.msg, "does not implement the required emulator interface") FieldError: type String has no field `msg`; String has no fields at all. Stacktrace: [1] getproperty(x::String, f::Symbol) @ Base Base_compiler.jl:58 [2] top-level scope @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303 [3] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [4] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined] [5] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [6] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:667 [inlined] [7] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined] [ Info: testing algorithm: RWMHSampling{GradFreeProtocol} Autodiff MCMC variants: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:646 Got exception outside of a @test TypeError: in DataContainer, in FT, expected FT<:Real, got Type{Vector{Float64}} Stacktrace: [1] DataContainer(data::Matrix{Vector{Float64}}; data_are_columns::Bool) @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/DataContainers.jl:0 [2] DataContainer(data::Matrix{Vector{Float64}}) @ EnsembleKalmanProcesses.DataContainers ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/DataContainers.jl:30 [3] encode_data(encoder_schedule::Vector{Any}, data::Matrix{Vector{Float64}}, in_or_out::String) @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:822 [4] MCMCWrapper(mcmc_alg::RWMHSampling{GradFreeProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; init_params::Vector{Any}, burnin::Int64, kwargs::@Kwargs{}) @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:603 [5] MCMCWrapper(mcmc_alg::RWMHSampling{GradFreeProtocol}, observation::Vector{Float64}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}) @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:571 [inlined] [6] MCMCWrapper(mcmc_alg::RWMHSampling{GradFreeProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}; kwargs::@Kwargs{}) @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:655 [inlined] [7] MCMCWrapper(mcmc_alg::RWMHSampling{GradFreeProtocol}, observation::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, em_or_fmw::Emulator{Float64, Vector{Any}}) @ CalibrateEmulateSample.MarkovChainMonteCarlo ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MarkovChainMonteCarlo.jl:648 [inlined] [8] mcmc_test_template(prior::ParameterDistribution{Parameterized, Constraint{Bounded}, String}, σ2_y::UniformScaling{Float64}, em::Emulator{Float64, Vector{Any}}; exp_name::String, mcmc_alg::RWMHSampling{GradFreeProtocol}, obs_sample::Observation{Vector{Vector{Float64}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{Diagonal{Float64, Vector{Float64}}}, Vector{String}, Vector{UnitRange{Int64}}, Nothing}, init_params::Vector{Float64}, step::Float64, rng::MersenneTwister, target_acc::Float64, return_samples::Bool) @ Main ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:260 [9] top-level scope @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303 [10] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [11] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:647 [inlined] [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [13] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:676 [inlined] [14] include(mapexpr::Function, mod::Module, _path::String) @ Base Base.jl:327 [15] macro expansion @ timing.jl:505 [inlined] [16] include_test(_module::String) @ Main ~/.julia/packages/CalibrateEmulateSample/yapkx/test/runtests.jl:15 [17] top-level scope @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/runtests.jl:21 [18] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2246 [inlined] [19] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/runtests.jl:33 [inlined] [20] include(mapexpr::Function, mod::Module, _path::String) @ Base Base.jl:327 [21] top-level scope @ none:6 [22] eval(m::Module, e::Any) @ Core boot.jl:521 [23] exec_options(opts::Base.JLOptions) @ Base client.jl:321 [24] _start() @ Base client.jl:596 Completed tests for MarkovChainMonteCarlo, 181 seconds elapsed Starting tests for Utilities ┌ Warning: For 2 parameters, the recommended minimum ensemble size (`N_ens`) is 20. Got `N_ens` = 10`. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/EnsembleKalmanProcess.jl:262 [ Info: extracting iterations 1:1 from EnsembleKalmanProcess [ Info: extracting iterations [1, 2] from EnsembleKalmanProcess [ Info: extracting iterations [1, 2] from EnsembleKalmanProcess [ Info: extracting iterations [1, 2] from EnsembleKalmanProcess [ Info: extracting iterations [1, 2] from EnsembleKalmanProcess ┌ Info: Detected fewer `samples_out` (1) than `samples_in` (2) and `dt` (2). Input-output structure vectors will be created from 1 samples. │ This commonly occurs when samples are built from `get_u(ekp), get_g(ekp)`. │ The final interation of output samples, (e.g., from evaluating `g=forward_map_ensemble(get_ϕ_final(ekp)`) can be provided by `encoder_kwargs_from(ekp, prior; final_samples_out=g)` └ ┌ Warning: Detected that observation covariances vary for different observations. │ Encoder kwarg `:obs_noise_cov` will be set to the FIRST of these covariances for the purpose of data processing. └ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:142 ┌ Warning: Comparing equality of linear maps with size (3050, 3050) and (50, 50). Was this intended? └ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:430 ┌ Warning: Comparing equality of linear maps with size (3050, 3050) and (50, 50). Was this intended? └ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities.jl:430 [ Info: Initialize encoding of data: "in" with LikelihoodInformed: iters=[1], grad_type=linreg, retain_info=0.85 ┌ Warning: Structure vectors do not contain key `:dt`. │ Continuing, assuming all vectors come from the prior `:dt=>0`. └ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:109 ┌ Info: Constructing a likelihood-informed subspace using, │ iterations:[1], └ α: [0] ┌ Warning: Consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed └ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:125 ┌ Info: Structure vectors either not provided, else do not contain keys `:samples_in, :samples_out`. └ Continuing using input-output pairs as structure vectors [ Info: truncating at 2/10 retaining 97.07119036719806% of the information [ Info: Initialize encoding of data: "out" with LikelihoodInformed: iters=[1], grad_type=linreg, retain_info=0.85 ┌ Warning: Structure vectors do not contain key `:dt`. │ Continuing, assuming all vectors come from the prior `:dt=>0`. └ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:109 ┌ Info: Constructing a likelihood-informed subspace using, │ iterations:[1], └ α: [0] ┌ Warning: Consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed └ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:125 ┌ Info: Structure vectors either not provided, else do not contain keys `:samples_in, :samples_out`. └ Continuing using input-output pairs as structure vectors ┌ Warning: Using LikelihoodInformed on output data with α≠0 or with obs_noise_cov≠I triggers a manifold optimization process that may take some time. If α=0, consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed └ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:300 [ Info: increasing k from 1 to 2 [ Info: increasing k from 2 to 4 [ Info: increasing k from 4 to 8 [ Info: increasing k from 8 to 16 [ Info: truncating at 16/50 retaining 92.49312701395625% of the KL divergence reduction [ Info: Initialize encoding of data: "in" with ElementwiseScaler: ZScoreScaling [ Info: Initialize encoding of data: "out" with ElementwiseScaler: ZScoreScaling [ Info: Initialize encoding of data: "in" with ElementwiseScaler: QuartileScaling [ Info: Initialize encoding of data: "out" with ElementwiseScaler: QuartileScaling [ Info: Initialize encoding of data: "in" with ElementwiseScaler: MinMaxScaling [ Info: Initialize encoding of data: "out" with ElementwiseScaler: MinMaxScaling [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 120, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 120, while the space dimension is 50, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=structure_mat [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=combined ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 120, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=combined ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 120, while the space dimension is 50, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "in" with CanonicalCorrelation: [ Info: Initialize encoding of data: "out" with CanonicalCorrelation: [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95 [ Info: truncating at 6/10 retaining 96.3172690603363% of the variance of the structure matrix [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95 [ Info: truncating at 25/50 retaining 95.43320516785353% of the variance of the structure matrix [ Info: Initialize encoding of data: "in" with CanonicalCorrelation: retain_var=0.95 [ Info: truncating at 9/10 retaining 95.0218809235637% of the variance in the joint space [ Info: Initialize encoding of data: "out" with CanonicalCorrelation: retain_var=0.95 [ Info: truncating at 9/9 retaining 100.0% of the variance in the joint space [ Info: Initialize encoding of data: "in" with LikelihoodInformed: iters=[1], grad_type=linreg, retain_info=0.99 ┌ Info: Constructing a likelihood-informed subspace using, │ iterations:[1], └ α: [0.0] ┌ Warning: Consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed └ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:125 [ Info: truncating at 4/10 retaining 99.76383264279495% of the information [ Info: Initialize encoding of data: "out" with LikelihoodInformed: iters=[1], grad_type=linreg, retain_info=0.99 ┌ Info: Constructing a likelihood-informed subspace using, │ iterations:[1], └ α: [0.0] ┌ Warning: Consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed └ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:125 ┌ Warning: Using LikelihoodInformed on output data with α≠0 or with obs_noise_cov≠I triggers a manifold optimization process that may take some time. If α=0, consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed └ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:300 [ Info: increasing k from 1 to 2 [ Info: increasing k from 2 to 4 [ Info: increasing k from 4 to 8 [ Info: increasing k from 8 to 16 [ Info: increasing k from 16 to 32 [ Info: increasing k from 32 to 49 [ Info: truncating at 49/50 retaining 99.90483752878771% of the KL divergence reduction [ Info: Initialize encoding of data: "in" with LikelihoodInformed: iters=1:2, grad_type=localsl, retain_info=0.99 ┌ Info: Constructing a likelihood-informed subspace using, │ iterations:1:2, └ α: [0.0, 0.5] ┌ Warning: Consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed └ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:125 [ Info: truncating at 10/10 retaining 99.99999999999999% of the information [ Info: Initialize encoding of data: "out" with LikelihoodInformed: iters=1:2, grad_type=localsl, retain_info=0.99 ┌ Info: Constructing a likelihood-informed subspace using, │ iterations:1:2, └ α: [0.0, 0.5] ┌ Warning: Consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed └ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:125 ┌ Warning: Using LikelihoodInformed on output data with α≠0 or with obs_noise_cov≠I triggers a manifold optimization process that may take some time. If α=0, consider using decorrelate_structure_mat to gain obs_noise_cov = I before calling likelihood_informed └ @ CalibrateEmulateSample.Utilities ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Utilities/likelihood_informed.jl:300 [ Info: increasing k from 1 to 2 [ Info: increasing k from 2 to 4 [ Info: increasing k from 4 to 8 [ Info: increasing k from 8 to 16 [ Info: increasing k from 16 to 32 [ Info: increasing k from 32 to 49 [ Info: truncating at 49/50 retaining 99.85137063252363% of the KL divergence reduction [ Info: Initialize encoding of data: "in" with ElementwiseScaler: ZScoreScaling [ Info: Initialize encoding of data: "out" with ElementwiseScaler: ZScoreScaling [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95 [ Info: truncating at 6/10 retaining 96.3172690603363% of the variance of the structure matrix [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95 [ Info: truncating at 25/50 retaining 95.43320516785353% of the variance of the structure matrix [ Info: Initialize encoding of data: "in" with CanonicalCorrelation: [ Info: Initialize encoding of data: "out" with CanonicalCorrelation: [ Info: Initialize encoding of data: "in" with ElementwiseScaler: ZScoreScaling [ Info: Initialize encoding of data: "out" with ElementwiseScaler: ZScoreScaling [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov, retain_var=0.99 ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 120, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: truncating at 8/10 retaining 99.64145742343696% of the variance of the structure matrix [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov, retain_var=0.99 ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 120, while the space dimension is 50, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: truncating at 34/50 retaining 99.08168482305003% of the variance of the structure matrix [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov, retain_var=0.99 ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 120, while the space dimension is 8, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: truncating at 8/8 retaining 100.0% of the variance of the structure matrix [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov, retain_var=0.99 ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 120, while the space dimension is 34, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: truncating at 34/34 retaining 100.00000000000003% of the variance of the structure matrix [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=structure_mat [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat [ Info: Initialize encoding of data: "in" with CanonicalCorrelation: [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=bad_value [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 120, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "bad" with CanonicalCorrelation: [ Info: Initialize encoding of data: "in" with ElementwiseScaler: ZScoreScaling [ Info: Initialize encoding of data: "out" with ElementwiseScaler: ZScoreScaling [ Info: Initialize encoding of data: "in" with ElementwiseScaler: QuartileScaling [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 150, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 150, while the space dimension is 50, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with ElementwiseScaler: MinMaxScaling [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=structure_mat [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat [ Info: Initialize encoding of data: "in" with CanonicalCorrelation: [ Info: [ Info: Testing decorrelating dimension: 10 [ Info: ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 30, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95 [ Info: truncating at 9/10 retaining 96.94724658587292% of the variance of the structure matrix [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=combined, retain_var=0.95 ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: truncating at 9/10 retaining 95.28722326458872% of the variance of the structure matrix [ Info: [ Info: Testing decorrelating dimension: 10 [ Info: ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 30, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95 [ Info: truncating at 9/10 retaining 97.17920752579474% of the variance of the structure matrix [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=combined, retain_var=0.95 ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: truncating at 9/10 retaining 95.84746553532077% of the variance of the structure matrix [ Info: [ Info: Testing decorrelating dimension: 100 [ Info: [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov [ Info: truncating at 49/100, as low-rank data detected [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95 [ Info: truncating at 25/100 retaining 95.70595153762656% of the variance of the structure matrix [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=combined, retain_var=0.95 [ Info: truncating at 45/100 retaining 95.3743783168293% of the variance of the structure matrix [ Info: [ Info: Testing decorrelating dimension: 1000 [ Info: [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov [ Info: truncating at 49/1000, as low-rank data detected [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95 [ Info: truncating at 28/1000 retaining 97.45627884217224% of the variance of the structure matrix [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=combined, retain_var=0.95 [ Info: truncating at 59/1000 retaining 95.37138142016833% of the variance of the structure matrix [ Info: [ Info: Testing decorrelating dimension: 10000 [ Info: [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov [ Info: truncating at 49/10000, as low-rank data detected [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95 [ Info: relative error of total variance 0.012632811063376808 [ Info: truncating at 26/10000 retaining 97.76510841876316% (+/-1.2632811063376808)% of the variance of the structure matrix [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=combined, retain_var=0.95 [ Info: relative error of total variance 0.012739793420345464 [ Info: truncating at 29/10000 retaining 95.33458707311726% (+/-1.2739793420345464)% of the variance of the structure matrix [ Info: [ Info: Testing decorrelating dimension: 100000 [ Info: [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov [ Info: truncating at 49/100000, as low-rank data detected [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95 [ Info: relative error of total variance 0.012485815049894669 [ Info: truncating at 28/100000 retaining 97.94799689276151% (+/-1.2485815049894669)% of the variance of the structure matrix [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 50, while the space dimension is 10, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=combined, retain_var=0.95 [ Info: relative error of total variance 0.010819851394050716 [ Info: truncating at 29/100000 retaining 95.38039057028318% (+/-1.0819851394050715)% of the variance of the structure matrix dimension decorr-sample decorr-structure decorr-combined 10, 0.002837262, 0.001795673, 0.002148048 100, 0.010663285, 0.007463157, 0.014136251 1000, 1.179302566, 0.748050558, 2.081822577 10000, 4.438638467, 17.91189072, 37.931875622 ┌ Error: ∟ timings have exceeded linear scaling └ @ Main ~/.julia/packages/CalibrateEmulateSample/yapkx/test/Utilities/runtests.jl:758 100000, 13.659167186, 132.007740037, 408.863010893 ┌ Error: ∟ timings have exceeded linear scaling └ @ Main ~/.julia/packages/CalibrateEmulateSample/yapkx/test/Utilities/runtests.jl:758 Completed tests for Utilities, 1218 seconds elapsed Starting tests for Show Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: SumKernel{SEArd{Float64}, Noise{Float64}} Type: SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: Noise{Float64}, Params: [0.0] created GP: 1 kernel in GaussianProcess: Type: SumKernel{SEArd{Float64}, Noise{Float64}} Type: SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: Noise{Float64}, Params: [0.0] created GP: 2 [ Info: Initialize encoding of data: "in" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 20, while the space dimension is 3, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=sample_cov ┌ Warning: SVD representation is efficient when estimating high-dimensional covariance with few samples. │ here # samples is 20, while the space dimension is 2, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/1ArOR/src/Observations.jl:203 Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Learning additive white noise kernel in GaussianProcess: Type: SumKernel{SEArd{Float64}, Noise{Float64}} Type: SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: Noise{Float64}, Params: [0.0] created GP: 1 kernel in GaussianProcess: Type: SumKernel{SEArd{Float64}, Noise{Float64}} Type: SEArd{Float64}, Params: [-0.0, -0.0, -0.0, 0.0] Type: Noise{Float64}, Params: [0.0] created GP: 2 Completed tests for Show, 4 seconds elapsed Test Summary: | Pass Fail Error Total Time CalibrateEmulateSample | 663 2 8 673 38m05.3s Emulators | 19 19 1m51.3s Emulators | 15 15 45.3s GaussianProcess | 41 41 1m43.2s RandomFeatures | 136 136 8m01.6s MarkovChainMonteCarlo | 16 2 8 26 3m00.1s 1D-1D GP/RF & RW Metropolis | 1 1 9.6s 2D-2D RF & RW | 3 3 48.5s 1D-1D pCN | 1 1 0.8s ND-1D | 2 2 26.1s Test the encode-decode for posterior samples | 11 1 12 21.9s Autodiff MCMC variants | 2 5 7 8.2s Utilities | 15 15 31.2s Data Preprocessing | 306 306 9m24.0s Decorrelator: Large observational covariance | 0 10m21.2s Show | 115 115 3.5s RNG of the outermost testset: Xoshiro(0x556eb437d6bd3d83, 0x01f228aafdc56f72, 0xdd0365933e229fcf, 0x2649768b4341a729, 0xb85867e7dd863d03) ERROR: LoadError: Some tests did not pass: 663 passed, 2 failed, 8 errored, 0 broken. in expression starting at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/runtests.jl:20 Testing failed after 2299.78s ERROR: LoadError: Package CalibrateEmulateSample errored during testing Stacktrace: [1] pkgerror(msg::String) @ Pkg.Types /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Types.jl:68 [2] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, julia_args::Cmd, test_args::Cmd, test_fn::Nothing, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool) @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Operations.jl:3247 [3] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, test_fn::Nothing, julia_args::Cmd, test_args::Cmd, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool, kwargs::@Kwargs{io::IOContext{IO}}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:587 [4] test(pkgs::Vector{PackageSpec}; io::IOContext{IO}, kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:172 [5] test(pkgs::Vector{String}; kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:160 [6] test(pkg::String; kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:159 [inlined] [7] top-level scope @ /PkgEval.jl/scripts/evaluate.jl:223 [8] include(mod::Module, _path::String) @ Base Base.jl:326 [9] exec_options(opts::Base.JLOptions) @ Base client.jl:355 [10] _start() @ Base client.jl:596 in expression starting at /PkgEval.jl/scripts/evaluate.jl:214 PkgEval failed after 2695.04s: package tests unexpectedly errored