Package evaluation to test CalibrateEmulateSample on Julia 1.14.0-DEV.3055 (7e75a8061a*) started at 2026-08-28T07:44:43.459 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.14` Set-up completed after 14.48s ################################################################################ # 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 v1.24.0 [14f7f29c] + AMD v0.5.3 [621f4979] + AbstractFFTs v1.5.0 [99985d1d] + AbstractGPs v0.5.24 [80f14c24] + AbstractMCMC v5.16.0 [1520ce14] + AbstractTrees v0.4.5 [7d9f7c33] + Accessors v0.1.45 [79e6a3ab] + Adapt v4.7.0 [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 v7.30.0 [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 v0.7.9 [35d6a980] + ColorSchemes v3.31.0 [3da002f7] + ColorTypes v0.12.1 [c3611d14] + ColorVectorSpace v0.11.0 [5ae59095] + Colors v0.13.1 [38540f10] + CommonSolve v0.2.14 [bbf7d656] + CommonSubexpressions v0.3.1 [f70d9fcc] + CommonWorldInvalidations v1.2.0 [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 v0.16.7 [adafc99b] + CpuId v0.3.1 [a8cc5b0e] + Crayons v4.2.0 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.19.6 [e2d170a0] + DataValueInterfaces v1.0.0 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.16.0 [a0c0ee7d] + DifferentiationInterface v0.7.21 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.131 [ffbed154] + DocStringExtensions v0.9.5 [fdbdab4c] + ElasticArrays v1.2.12 [2904ab23] + ElasticPDMats v0.2.4 [aa8a2aa5] + EnsembleKalmanProcesses v2.7.2 [4e289a0a] + EnumX v1.0.7 [c87230d0] + FFMPEG v0.4.5 [b86e33f2] + FFTA v0.3.1 [7a1cc6ca] + FFTW v1.10.0 [442a2c76] + FastGaussQuadrature v1.3.0 [1a297f60] + FillArrays v1.17.0 [6a86dc24] + FiniteDiff v2.33.0 ⌅ [53c48c17] + FixedPointNumbers v0.8.6 [f6369f11] + ForwardDiff v1.4.5 [069b7b12] + FunctionWrappers v1.1.3 [d9f16b24] + Functors v0.5.3 ⌃ [a0844989] + Gamma v1.1.0 [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 v0.3.30 [615f187c] + IfElse v0.1.1 [d25df0c9] + Inflate v0.1.5 [22cec73e] + InitialValues v0.3.1 [18e54dd8] + IntegerMathUtils v0.1.4 [a98d9a8b] + Interpolations v0.16.3 [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 v1.7.1 [5ab0869b] + KernelDensity v0.6.12 ⌅ [ec8451be] + KernelFunctions v0.10.67 [2c470bb0] + Kronecker v0.5.5 [40e66cde] + LDLFactorizations v0.10.2 [b964fa9f] + LaTeXStrings v1.4.1 [10f19ff3] + LayoutPointers v0.1.17 [1d6d02ad] + LeftChildRightSiblingTrees v0.3.0 ⌃ [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 v0.3.19 [e80e1ace] + MLJModelInterface v1.12.1 [1914dd2f] + MacroTools v0.5.16 [af67fdf4] + ManifoldDiff v0.4.5 [1cead3c2] + Manifolds v0.11.29 [3362f125] + ManifoldsBase v2.5.0 ⌅ [0fc0a36d] + Manopt v0.5.39 [d125e4d3] + ManualMemory v0.1.8 [b8f27783] + MathOptInterface v1.53.0 [99c1a7ee] + MatrixEquations v2.6.4 [0b3b1443] + MicroMamba v0.1.15 [e1d29d7a] + Missings v1.2.0 [46d2c3a1] + MuladdMacro v0.2.7 [d8a4904e] + MutableArithmetics v1.8.0 ⌅ [d41bc354] + NLSolversBase v7.10.0 [77ba4419] + NaNMath v1.1.4 [356022a1] + NamedDims v1.2.3 [c020b1a1] + NaturalSort v1.0.0 [4d1e1d77] + Nullables v1.0.0 [6fe1bfb0] + OffsetArrays v1.17.0 ⌅ [429524aa] + Optim v1.13.3 ⌅ [bac558e1] + OrderedCollections v1.8.2 ⌅ [90014a1f] + PDMats v0.11.36 ⌅ [69de0a69] + Parsers v2.8.7 [fa939f87] + Pidfile v1.3.0 [1d0040c9] + PolyesterWeave v0.2.2 [85a6dd25] + PositiveFactorizations v0.2.4 [aea7be01] + PrecompileTools v1.3.4 [21216c6a] + Preferences v1.5.2 [08abe8d2] + PrettyTables v3.4.8 [27ebfcd6] + Primes v0.5.7 [49802e3a] + ProgressBars v1.5.1 [33c8b6b6] + ProgressLogging v0.1.6 [92933f4c] + ProgressMeter v1.11.0 [43287f4e] + PtrArrays v1.4.0 [6099a3de] + PythonCall v0.9.35 [1fd47b50] + QuadGK v2.11.3 [94ee1d12] + Quaternions v0.7.7 [36c3bae2] + RandomFeatures v0.3.5 [b3c3ace0] + RangeArrays v0.3.2 [c84ed2f1] + Ratios v0.4.5 [c1ae055f] + RealDot v0.1.0 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [37e2e3b7] + ReverseDiff v1.17.0 [79098fc4] + Rmath v0.9.0 [f2b01f46] + Roots v3.0.7 [c946c3f1] + SCS v2.6.4 [94e857df] + SIMDTypes v0.1.0 [476501e8] + SLEEFPirates v0.6.46 [431bcebd] + SciMLPublic v1.3.0 [30f210dd] + ScientificTypesBase v3.1.0 [6e75b9c4] + ScikitLearnBase v0.5.0 [6c6a2e73] + Scratch v1.3.0 [efcf1570] + Setfield v1.1.2 [699a6c99] + SimpleTraits v0.9.6 [47aef6b3] + SimpleWeightedGraphs v1.5.1 [a2af1166] + SortingAlgorithms v1.2.3 [276daf66] + SpecialFunctions v2.9.0 [860ef19b] + StableRNGs v1.0.4 [aedffcd0] + Static v1.4.6 [0d7ed370] + StaticArrayInterface v1.10.0 [90137ffa] + StaticArrays v1.9.19 [1e83bf80] + StaticArraysCore v1.4.4 [64bff920] + StatisticalTraits v3.5.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.8.0 [2913bbd2] + StatsBase v0.34.13 ⌅ [4c63d2b9] + StatsFuns v1.5.2 [892a3eda] + StringManipulation v0.5.0 [ec057cc2] + StructUtils v2.8.5 [9449cd9e] + TSVD v0.4.4 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.14.0 [62fd8b95] + TensorCore v0.1.1 [5d786b92] + TerminalLoggers v0.1.8 [8290d209] + ThreadingUtilities v0.5.6 [3bb67fe8] + TranscodingStreams v0.11.3 [bc48ee85] + Tullio v0.3.9 [3a884ed6] + UnPack v1.0.2 [e17b2a0c] + UnsafePointers v1.0.0 [3d5dd08c] + VectorizationBase v0.21.74 [efce3f68] + WoodburyMatrices v1.1.0 [700de1a5] + ZygoteRules v0.2.8 ⌅ [68821587] + Arpack_jll v3.5.2+0 [6e34b625] + Bzip2_jll v1.0.9+0 [83423d85] + Cairo_jll v1.18.7+0 [2e619515] + Expat_jll v2.8.3+0 ⌅ [b22a6f82] + FFMPEG_jll v8.1.2+0 [f5851436] + FFTW_jll v3.3.12+0 [a3f928ae] + Fontconfig_jll v2.17.1+0 [d7e528f0] + FreeType2_jll v2.14.3+1 [559328eb] + FriBidi_jll v1.0.17+0 ⌅ [b0724c58] + GettextRuntime_jll v0.22.4+0 [7746bdde] + Glib_jll v2.88.3+0 [3b182d85] + Graphite2_jll v1.3.16+0 ⌅ [2e76f6c2] + HarfBuzz_jll v8.5.1+0 [1d5cc7b8] + IntelOpenMP_jll v2025.2.0+0 [c1c5ebd0] + LAME_jll v3.100.3+0 [1d63c593] + LLVMOpenMP_jll v22.1.7+0 ⌅ [e9f186c6] + Libffi_jll v3.4.7+0 [94ce4f54] + Libiconv_jll v1.18.0+0 [4b2f31a3] + Libmount_jll v2.42.0+0 [38a345b3] + Libuuid_jll v2.42.0+0 [856f044c] + MKL_jll v2025.2.0+0 [e7412a2a] + Ogg_jll v1.3.6+0 [656ef2d0] + OpenBLAS32_jll v0.3.34+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [91d4177d] + Opus_jll v1.6.1+0 [30392449] + Pixman_jll v0.46.4+0 [f50d1b31] + Rmath_jll v0.5.2+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 v3.14.1+0 [0ac62f75] + libass_jll v0.17.4+0 [8e53e030] + libdrm_jll v2.4.134+0 [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 v0.76.2+0 ⌅ [1270edf5] + x264_jll v10164.0.1+0 [dfaa095f] + x265_jll v4.1.0+0 [0dad84c5] + ArgTools v1.2.0 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.12.0 [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 v1.5.7+0 [deac9b47] + LibCURL_jll v8.21.0+0 [e37daf67] + LibGit2_jll v1.9.7+0 [29816b5a] + LibSSH2_jll v1.11.104+0 [14a3606d] + MozillaCACerts_jll v2026.8.13 [4536629a] + OpenBLAS_jll v0.3.34+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.8+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 v1.70.0+0 [3f19e933] + p7zip_jll v17.8.2+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 5.78s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompiling project... 3.4 s ✓ PDMats → StatsBaseExt 2.1 s ✓ ElasticPDMats 1.8 s ✓ FillArrays → FillArraysPDMatsExt 19.7 s ✓ Manopt 35.6 s ✓ Manifolds 14.3 s ✓ Distributions 6.0 s ✓ Manopt → ManoptLineSearchesExt 7.1 s ✓ Manifolds → ManifoldsRecipesBaseExt 10.0 s ✓ Manifolds → ManifoldsTestExt 7.3 s ✓ Manopt → ManoptManifoldsExt 6.1 s ✓ Distributions → DistributionsTestExt 5.6 s ✓ Distributions → DistributionsChainRulesCoreExt 7.5 s ✓ MCMCDiagnosticTools 9.3 s ✓ GaussianProcesses 7.4 s ✓ AdvancedMH 6.9 s ✓ KernelDensity 7.6 s ✓ AbstractGPs 19.3 s ✓ EnsembleKalmanProcesses 6.5 s ✓ AdvancedMH → AdvancedMHForwardDiffExt 12.2 s ✓ MCMCChains 49.4 s ✓ RandomFeatures 9.7 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/5WGSP/CondaPkg.toml CondaPkg Resolving changes + openssl + python + scikit-learn + scipy CondaPkg Initialising pixi │ /home/pkgeval/.julia/artifacts/8429d049f6c01106c62971e9540e146dc068df25/bin/pixi │ init │ --format pixi └ /tmp/jl_hLnhGk/.CondaPkg ✔ Created /tmp/jl_hLnhGk/.CondaPkg/pixi.toml CondaPkg Wrote /tmp/jl_hLnhGk/.CondaPkg/pixi.toml │ [dependencies] │ openssl = ">=3, <3.6" │ scikit-learn = "=1.5.1" │ scipy = "=1.14.1" │ │ [dependencies.python] │ version = ">=3.10,!=3.14.0,!=3.14.1,<4, =3.11" │ 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/8429d049f6c01106c62971e9540e146dc068df25/bin/pixi │ install └ --manifest-path /tmp/jl_hLnhGk/.CondaPkg/pixi.toml ✔ The default environment has been installed. 110.3 s ✓ CalibrateEmulateSample 23 dependencies successfully precompiled in 369 seconds. 339 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/5WGSP/CondaPkg.toml │ CondaPkg Resolving changes │ + openssl │ + python │ + scikit-learn │ + scipy │ CondaPkg Initialising pixi │ │ /home/pkgeval/.julia/artifacts/8429d049f6c01106c62971e9540e146dc068df25/bin/pixi │ │ init │ │ --format pixi │ └ /tmp/jl_hLnhGk/.CondaPkg │ ✔ Created /tmp/jl_hLnhGk/.CondaPkg/pixi.toml │ CondaPkg Wrote /tmp/jl_hLnhGk/.CondaPkg/pixi.toml │ │ [dependencies] │ │ openssl = ">=3, <3.6" │ │ scikit-learn = "=1.5.1" │ │ scipy = "=1.14.1" │ │ │ │ [dependencies.python] │ │ version = ">=3.10,!=3.14.0,!=3.14.1,<4, =3.11" │ │ 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/8429d049f6c01106c62971e9540e146dc068df25/bin/pixi │ │ install │ └ --manifest-path /tmp/jl_hLnhGk/.CondaPkg/pixi.toml │ ✔ The default environment has been installed. └ Precompilation completed after 380.56s ################################################################################ # Testing # Testing CalibrateEmulateSample Status `/tmp/jl_Pzrckl/Project.toml` [99985d1d] AbstractGPs v0.5.24 [80f14c24] AbstractMCMC v5.16.0 [5b7e9947] AdvancedMH v0.8.10 [95e48a1f] CalibrateEmulateSample v1.1.0 [ae650224] ChunkSplitters v3.2.0 [992eb4ea] CondaPkg v0.2.36 [31c24e10] Distributions v0.25.131 [ffbed154] DocStringExtensions v0.9.5 [aa8a2aa5] EnsembleKalmanProcesses v2.7.2 [f6369f11] ForwardDiff v1.4.5 [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 v0.11.29 ⌅ [0fc0a36d] Manopt v0.5.39 ⌅ [90014a1f] PDMats v0.11.36 [49802e3a] ProgressBars v1.5.1 [6099a3de] PythonCall v0.9.35 [36c3bae2] RandomFeatures v0.3.5 [37e2e3b7] ReverseDiff v1.17.0 [860ef19b] StableRNGs v1.0.4 [10745b16] Statistics v1.11.1 [2913bbd2] StatsBase v0.34.13 [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_Pzrckl/Manifest.toml` [47edcb42] ADTypes v1.24.0 [14f7f29c] AMD v0.5.3 [621f4979] AbstractFFTs v1.5.0 [99985d1d] AbstractGPs v0.5.24 [80f14c24] AbstractMCMC v5.16.0 [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.45 [79e6a3ab] Adapt v4.7.0 [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 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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/5WGSP/CondaPkg.toml CondaPkg Resolving changes + openssl + python + scikit-learn + scipy CondaPkg Initialising pixi │ /home/pkgeval/.julia/artifacts/8429d049f6c01106c62971e9540e146dc068df25/bin/pixi │ init │ --format pixi └ /tmp/jl_Pzrckl/.CondaPkg ✔ Created /tmp/jl_Pzrckl/.CondaPkg/pixi.toml CondaPkg Wrote /tmp/jl_Pzrckl/.CondaPkg/pixi.toml │ [dependencies] │ openssl = ">=3, <3.6" │ scikit-learn = "=1.5.1" │ scipy = "=1.14.1" │ │ [dependencies.python] │ version = ">=3.10,!=3.14.0,!=3.14.1,<4, =3.11" │ 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/8429d049f6c01106c62971e9540e146dc068df25/bin/pixi │ install └ --manifest-path /tmp/jl_Pzrckl/.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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/src/Observations.jl:203 Completed tests for Emulator, 239 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/NajPj/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/NajPj/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/NajPj/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, 80 seconds elapsed Starting tests for RandomFeature ┌ Info: Shrinkage scale: 0.9707373683067573, (0 = none, 1 = revert to scaled Identity) └ shrinkage covariance condition number: 1.1738044784996795 [ Info: NICE-adjusted covariance condition number: 2.6560387034032704 [ 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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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 RF within Emulator: 2D -> 2D: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/RandomFeature/runtests.jl:415 Got exception outside of a @test MethodError: no method matching reducedim1(::Matrix{Float64}, ::Matrix{Float64}) The function `reducedim1` exists, but no method is defined for this combination of argument types. Closest candidates are: reducedim1(::Any) @ Base reducedim.jl:1229 Stacktrace: [1] centralize_sumabs2!(R::Matrix{Float64}, A::Matrix{Float64}, means::Matrix{Float64}) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:282 [2] varm!(R::Matrix{Float64}, A::Matrix{Float64}, m::Matrix{Float64}; corrected::Bool) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:309 [3] _varm(A::Matrix{Float64}, m::Matrix{Float64}, corrected::Bool, region::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:341 [4] varm(A::Matrix{Float64}, m::Matrix{Float64}; corrected::Bool, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:339 [inlined] [5] _var(A::Matrix{Float64}, corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:386 [inlined] [6] var(A::Matrix{Float64}; corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:380 [inlined] [7] _std(A::Matrix{Float64}, corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:469 [8] std(A::Matrix{Float64}; corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:461 [inlined] [9] build_models!(srfi::ScalarRandomFeatureInterface{String, MersenneTwister, SeparableKernel{DiagonalFactor{Float64}, OneDimFactor}}, input_output_pairs::PairedDataContainer{Float64}, input_structure_mats::Dict{Symbol, Union{UniformScaling, LinearMaps.LinearMap, AbstractVecOrMat}}, output_structure_mats::Dict{Symbol, Union{UniformScaling, LinearMaps.LinearMap, AbstractVecOrMat}}) @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MachineLearningTools/ScalarRandomFeature.jl:425 [10] Emulator(machine_learning_tool::ScalarRandomFeatureInterface{String, MersenneTwister, SeparableKernel{DiagonalFactor{Float64}, OneDimFactor}}, input_output_pairs::PairedDataContainer{Float64}; encoder_schedule::Nothing, encoder_kwargs::@NamedTuple{obs_noise_cov::Matrix{Float64}}, obs_noise_cov::Nothing, mlt_kwargs::@Kwargs{}) @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Emulator.jl:251 [11] top-level scope @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/RandomFeature/runtests.jl:16 [12] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [13] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/RandomFeature/runtests.jl:417 [inlined] [14] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [15] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/RandomFeature/runtests.jl:499 [inlined] Completed tests for RandomFeature, 318 seconds elapsed Starting tests for MarkovChainMonteCarlo Using default squared exponential kernel, learning length scale and variance parameters Using default squared exponential kernel: Type: SEArd{Float64}, Params: [-0.0, 0.0] kernel in GaussianProcess: Type: SEArd{Float64}, Params: [-0.0, 0.0] created GP: 1 optimized hyperparameters of GP: 1 Type: SEArd{Float64}, Params: [-0.7344956715967699, 1.6909811297504154] optimised GP: 1 Sum of 2 kernels: Squared Exponential Kernel (metric = Distances.Euclidean(0.0)) - ARD Transform (dims: 1) - σ² = 29.428460770481188 White Kernel - σ² = 2.061153622438558e-9 [ Info: Initialize encoding of data: "out" with ElementwiseScaler: QuartileScaling [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95 [ Info: truncating at 1/1 retaining 100.0% of the variance of the structure matrix Using user-defined kernelType: SEIso{Float64}, Params: [0.0, 0.0] kernel in GaussianProcess: Type: SEIso{Float64}, Params: [0.0, 0.0] created GP: 1 optimized hyperparameters of GP: 1 Type: SEIso{Float64}, Params: [-0.8279716521015293, 2.768606386766634] [ Info: hyperparameter learning for 1 models using 64 training points, 16 validation points and 100 features estimating covariances with 180 iterations... [ Info: Random Features already trained. continuing... ┌ Info: srfi_1d │ per-point - Emulator RMSE = 0.28178190546760523 │ Emulator STD = 0.23475419385186524 └ (These numbers should be similar sized to indicate no overfitting) ┌ Info: gpjl_1d │ per-point - Emulator RMSE = 0.20024482573071362 │ Emulator STD = 0.23974737449832012 └ (These numbers should be similar sized to indicate no overfitting) ┌ Info: agpjl_1d │ per-point - Emulator RMSE = 0.20024481767058971 │ Emulator STD = 0.2397473781137044 └ (These numbers should be similar sized to indicate no overfitting) ┌ Info: gpjl-svd_1d │ per-point - Emulator RMSE = 0.1974712233704437 │ Emulator STD = 0.2400373194495227 └ (These numbers should be similar sized to indicate no overfitting) [ 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 500, while the space dimension is 5, and representation will be inefficient. └ @ EnsembleKalmanProcesses ~/.julia/packages/EnsembleKalmanProcesses/NajPj/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, -0.0, -0.0, 0.0] kernel in GaussianProcess: Type: SEArd{Float64}, Params: [-0.0, -0.0, -0.0, -0.0, -0.0, 0.0] created GP: 1 optimized hyperparameters of GP: 1 Type: SEArd{Float64}, Params: [1.639066704401936, 1.6439292190344692, 1.7664555587688304, 1.2686882519363163, 2.1927427290036876, 3.7271738539647274] ┌ Info: gp_mv │ per-point - Emulator RMSE = 0.09972100944320987 │ Emulator STD = 0.1065925120730421 └ (These numbers should be similar sized to indicate no overfitting) MarkovChainMonteCarlo: Error During Test at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:297 Got exception outside of a @test MethodError: no method matching reducedim1(::Matrix{Float64}, ::Matrix{Float64}) The function `reducedim1` exists, but no method is defined for this combination of argument types. Closest candidates are: reducedim1(::Any) @ Base reducedim.jl:1229 Stacktrace: [1] centralize_sumabs2!(R::Matrix{Float64}, A::Matrix{Float64}, means::Matrix{Float64}) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:282 [2] varm!(R::Matrix{Float64}, A::Matrix{Float64}, m::Matrix{Float64}; corrected::Bool) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:309 [3] _varm(A::Matrix{Float64}, m::Matrix{Float64}, corrected::Bool, region::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:341 [4] varm(A::Matrix{Float64}, m::Matrix{Float64}; corrected::Bool, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:339 [inlined] [5] _var(A::Matrix{Float64}, corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:386 [inlined] [6] var(A::Matrix{Float64}; corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:380 [inlined] [7] _std(A::Matrix{Float64}, corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:469 [8] std(A::Matrix{Float64}; corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:461 [inlined] [9] build_models!(vrfi::VectorRandomFeatureInterface{String, TaskLocalRNG, NonseparableKernel{LowRankFactor{Float64}}}, input_output_pairs::PairedDataContainer{Float64}, input_structure_mats::Dict{Symbol, Union{UniformScaling, LinearMaps.LinearMap, AbstractVecOrMat}}, output_structure_mats::Dict{Symbol, Union{UniformScaling, LinearMaps.LinearMap, AbstractVecOrMat}}) @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/MachineLearningTools/VectorRandomFeature.jl:415 [10] Emulator(machine_learning_tool::VectorRandomFeatureInterface{String, TaskLocalRNG, NonseparableKernel{LowRankFactor{Float64}}}, input_output_pairs::PairedDataContainer{Float64}; encoder_schedule::Vector{Any}, encoder_kwargs::@NamedTuple{obs_noise_cov::Matrix{Float64}}, obs_noise_cov::Nothing, mlt_kwargs::@Kwargs{}) @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/yapkx/src/Emulator.jl:251 [11] test_vrfi(y::Matrix{Float64}, σ2_y::Matrix{Float64}, iopairs::PairedDataContainer{Float64}) @ Main ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:236 [12] top-level scope @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:303 [13] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [14] macro expansion @ ~/.julia/packages/CalibrateEmulateSample/yapkx/test/MarkovChainMonteCarlo/runtests.jl:369 [inlined] Completed tests for MarkovChainMonteCarlo, 43 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/NajPj/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 97.07119036719803% 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 94.26951000238529% 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/NajPj/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/NajPj/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/NajPj/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/NajPj/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 95.4332051678535% 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 95.02188092356373% 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 99.76383264279497% 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 99.23381793757974% 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 99.99999999999997% 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 99.93693790106968% 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 95.4332051678535% 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/NajPj/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/NajPj/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/NajPj/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/NajPj/src/Observations.jl:203 [ Info: truncating at 34/34 retaining 100.0% 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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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/NajPj/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 98.16244265393996% 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/NajPj/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/NajPj/src/Observations.jl:203 [ Info: truncating at 9/10 retaining 96.1183219725501% 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/NajPj/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/NajPj/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/NajPj/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/NajPj/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95 [ Info: truncating at 8/10 retaining 95.09116831654421% 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/NajPj/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/NajPj/src/Observations.jl:203 [ Info: truncating at 9/10 retaining 96.05067421406062% 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/NajPj/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/NajPj/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 95.62472321947124% 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/NajPj/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 95.35099411089617% 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/NajPj/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/NajPj/src/Observations.jl:203 [ Info: Initialize encoding of data: "out" with Decorrelator: decorrelate_with=structure_mat, retain_var=0.95 [ Info: truncating at 27/1000 retaining 95.06621313341303% 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/NajPj/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 95.31177031309116% 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/NajPj/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/NajPj/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 0.011807444531520954 [ Info: truncating at 26/10000 retaining 98.11271993975322% (+/-1.1807444531520952)% 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/NajPj/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 0.013456668689411316 [ Info: truncating at 29/10000 retaining 95.4597062938048% (+/-1.3456668689411315)% 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/NajPj/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/NajPj/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 0.009573483967385523 [ Info: truncating at 27/100000 retaining 95.1183939879456% (+/-0.9573483967385522)% 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/NajPj/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 0.013906698628024678 [ Info: truncating at 28/100000 retaining 95.38820375649945% (+/-1.3906698628024676)% of the variance of the structure matrix dimension decorr-sample decorr-structure decorr-combined 10, 0.001568536, 0.001342788, 0.001653545 100, 0.008476734, 0.005980396, 0.010782333 1000, 1.066790378, 0.918605392, 1.596232832 10000, 3.748984985, 11.686223245, 29.445717832 ┌ Error: ∟ timings have exceeded linear scaling └ @ Main ~/.julia/packages/CalibrateEmulateSample/yapkx/test/Utilities/runtests.jl:758 100000, 7.347120525, 77.265639512, 275.490628485 ┌ Error: ∟ timings have exceeded linear scaling └ @ Main ~/.julia/packages/CalibrateEmulateSample/yapkx/test/Utilities/runtests.jl:758 Completed tests for Utilities, 875 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/NajPj/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/NajPj/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, 6 seconds elapsed Test Summary: | Pass Error Total Time CalibrateEmulateSample | 637 2 639 26m00.7s Emulators | 19 19 1m13.1s Emulators | 15 15 29.0s GaussianProcess | 41 41 1m19.6s RandomFeatures | 126 1 127 5m17.2s hyperparameter prior interface | 59 59 30.3s ScalarRandomFeatureInterface | 19 19 4.3s VectorRandomFeatureInterface | 23 23 3.4s RF within Emulator: 1D -> 1D | 25 25 4m30.4s RF within Emulator: 2D -> 2D | 1 1 8.7s MarkovChainMonteCarlo | 1 1 42.8s Utilities | 15 15 28.8s Data Preprocessing | 306 306 7m12.8s Decorrelator: Large observational covariance | 0 6m51.5s Show | 115 115 5.0s RNG of the outermost testset: Xoshiro(0xb048d480e14ad714, 0xd85738962c341ec0, 0xdbc34d5c56204f5b, 0x5ef19be81dc96691, 0x1cba4469e50a8e12) ERROR: LoadError: Some tests did not pass: 637 passed, 0 failed, 2 errored, 0 broken. in expression starting at /home/pkgeval/.julia/packages/CalibrateEmulateSample/yapkx/test/runtests.jl:20 Testing failed after 1552.13s 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:3283 [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 in expression starting at /PkgEval.jl/scripts/evaluate.jl:214 PkgEval failed after 2039.4s: package tests unexpectedly errored