Package evaluation of CalibrateEmulateSample on Julia 1.11.4 (a71dd056e0*) started at 2025-04-09T01:48:01.202 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 8.3s ################################################################################ # Installation # Installing CalibrateEmulateSample... Resolving package versions... Installed Conda ────────────────── v1.10.2 Installed PyCall ───────────────── v1.96.4 Installed CalibrateEmulateSample ─ v0.6.1 Updating `~/.julia/environments/v1.11/Project.toml` [95e48a1f] + CalibrateEmulateSample v0.6.1 Updating `~/.julia/environments/v1.11/Manifest.toml` [47edcb42] + ADTypes v1.14.0 [14f7f29c] + AMD v0.5.3 [621f4979] + AbstractFFTs v1.5.0 ⌅ [80f14c24] + AbstractMCMC v4.4.2 [1520ce14] + AbstractTrees v0.4.5 [79e6a3ab] + Adapt v4.3.0 ⌃ [5b7e9947] + AdvancedMH v0.7.5 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [7d9fca2a] + Arpack v0.5.4 [4fba245c] + ArrayInterface v7.18.0 [13072b0f] + AxisAlgorithms v1.1.0 [39de3d68] + AxisArrays v0.4.7 ⌅ [198e06fe] + BangBang v0.3.40 [9718e550] + Baselet v0.1.1 [6e4b80f9] + BenchmarkTools v1.6.0 [62783981] + BitTwiddlingConvenienceFunctions v0.1.6 [2a0fbf3d] + CPUSummary v0.2.6 [95e48a1f] + CalibrateEmulateSample v0.6.1 [d360d2e6] + ChainRulesCore v1.25.1 [fb6a15b2] + CloseOpenIntervals v0.1.13 [523fee87] + CodecBzip2 v0.8.5 [944b1d66] + CodecZlib v0.7.8 [bbf7d656] + CommonSubexpressions v0.3.1 [f70d9fcc] + CommonWorldInvalidations v1.0.0 [34da2185] + Compat v4.16.0 [a33af91c] + CompositionsBase v0.1.2 [8f4d0f93] + Conda v1.10.2 [88cd18e8] + ConsoleProgressMonitor v0.1.2 [187b0558] + ConstructionBase v1.5.8 [f65535da] + Convex v0.16.4 [adafc99b] + CpuId v0.3.1 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [a93c6f00] + DataFrames v1.7.0 [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.15.1 [a0c0ee7d] + DifferentiationInterface v0.6.50 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.118 [ffbed154] + DocStringExtensions v0.9.4 [fdbdab4c] + ElasticArrays v1.2.12 [2904ab23] + ElasticPDMats v0.2.3 [aa8a2aa5] + EnsembleKalmanProcesses v2.3.1 [4e289a0a] + EnumX v1.0.5 [c87230d0] + FFMPEG v0.4.2 [7a1cc6ca] + FFTW v1.8.1 ⌅ [442a2c76] + FastGaussQuadrature v0.4.9 [1a297f60] + FillArrays v1.13.0 [6a86dc24] + FiniteDiff v2.27.0 [59287772] + Formatting v0.4.3 ⌅ [f6369f11] + ForwardDiff v0.10.38 [891a1506] + GaussianProcesses v0.12.5 ⌃ [e4b2fa32] + GaussianRandomFields v2.1.6 [3e5b6fbb] + HostCPUFeatures v0.1.17 [615f187c] + IfElse v0.1.1 [22cec73e] + InitialValues v0.3.1 [842dd82b] + InlineStrings v1.4.3 [a98d9a8b] + Interpolations v0.15.1 [8197267c] + IntervalSets v0.7.10 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 ⌅ [92d709cd] + IrrationalConstants v0.1.1 [c8e1da08] + IterTools v1.10.0 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.7.0 [682c06a0] + JSON v0.21.4 [0f8b85d8] + JSON3 v1.14.2 [5ab0869b] + KernelDensity v0.6.9 [40e66cde] + LDLFactorizations v0.10.1 [b964fa9f] + LaTeXStrings v1.4.0 [10f19ff3] + LayoutPointers v0.1.17 [1d6d02ad] + LeftChildRightSiblingTrees v0.2.0 [d3d80556] + LineSearches v7.3.0 [6fdf6af0] + LogDensityProblems v2.1.2 [2ab3a3ac] + LogExpFunctions v0.3.29 [e6f89c97] + LoggingExtras v1.1.0 [bdcacae8] + LoopVectorization v0.12.172 ⌃ [c7f686f2] + MCMCChains v5.7.1 ⌅ [be115224] + MCMCDiagnosticTools v0.2.1 [e80e1ace] + MLJModelInterface v1.11.0 [1914dd2f] + MacroTools v0.5.15 [d125e4d3] + ManualMemory v0.1.8 [b8f27783] + MathOptInterface v1.38.1 ⌅ [128add7d] + MicroCollections v0.1.4 [e1d29d7a] + Missings v1.2.0 [d8a4904e] + MutableArithmetics v1.6.4 [d41bc354] + NLSolversBase v7.9.1 [77ba4419] + NaNMath v1.1.3 [c020b1a1] + NaturalSort v1.0.0 [6fe1bfb0] + OffsetArrays v1.16.0 [429524aa] + Optim v1.12.0 [bac558e1] + OrderedCollections v1.8.0 [90014a1f] + PDMats v0.11.33 [d96e819e] + Parameters v0.12.3 [69de0a69] + Parsers v2.8.1 [1d0040c9] + PolyesterWeave v0.2.2 [2dfb63ee] + PooledArrays v1.4.3 [85a6dd25] + PositiveFactorizations v0.2.4 ⌅ [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.4.3 [08abe8d2] + PrettyTables v2.4.0 [49802e3a] + ProgressBars v1.5.1 [33c8b6b6] + ProgressLogging v0.1.4 [92933f4c] + ProgressMeter v1.10.4 [43287f4e] + PtrArrays v1.3.0 [438e738f] + PyCall v1.96.4 [1fd47b50] + QuadGK v2.11.2 [36c3bae2] + RandomFeatures v0.3.4 [b3c3ace0] + RangeArrays v0.3.2 [c84ed2f1] + Ratios v0.4.5 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 ⌅ [79098fc4] + Rmath v0.7.1 [c946c3f1] + SCS v2.1.0 [94e857df] + SIMDTypes v0.1.0 [476501e8] + SLEEFPirates v0.6.43 [30f210dd] + ScientificTypesBase v3.0.0 [3646fa90] + ScikitLearn v0.7.0 [6e75b9c4] + ScikitLearnBase v0.5.0 [91c51154] + SentinelArrays v1.4.8 [efcf1570] + Setfield v1.1.2 [a2af1166] + SortingAlgorithms v1.2.1 [276daf66] + SpecialFunctions v2.5.0 [171d559e] + SplittablesBase v0.1.15 [860ef19b] + StableRNGs v1.0.2 [aedffcd0] + Static v1.2.0 [0d7ed370] + StaticArrayInterface v1.8.0 [90137ffa] + StaticArrays v1.9.13 [1e83bf80] + StaticArraysCore v1.4.3 [64bff920] + StatisticalTraits v3.4.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.0 ⌅ [2913bbd2] + StatsBase v0.33.21 ⌅ [4c63d2b9] + StatsFuns v0.9.18 [892a3eda] + StringManipulation v0.4.1 [856f2bd8] + StructTypes v1.11.0 [9449cd9e] + TSVD v0.4.4 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.0 [5d786b92] + TerminalLoggers v0.1.7 [8290d209] + ThreadingUtilities v0.5.2 [3bb67fe8] + TranscodingStreams v0.11.3 ⌃ [28d57a85] + Transducers v0.4.80 [bc48ee85] + Tullio v0.3.8 [3a884ed6] + UnPack v1.0.2 [3d5dd08c] + VectorizationBase v0.21.71 [81def892] + VersionParsing v1.3.0 [efce3f68] + WoodburyMatrices v1.0.0 ⌅ [68821587] + Arpack_jll v3.5.1+1 [6e34b625] + Bzip2_jll v1.0.9+0 [83423d85] + Cairo_jll v1.18.4+0 [2e619515] + Expat_jll v2.6.5+0 ⌅ [b22a6f82] + FFMPEG_jll v4.4.4+1 [f5851436] + FFTW_jll v3.3.11+0 [a3f928ae] + Fontconfig_jll v2.16.0+0 [d7e528f0] + FreeType2_jll v2.13.4+0 [559328eb] + FriBidi_jll v1.0.17+0 [78b55507] + Gettext_jll v0.21.0+0 ⌃ [7746bdde] + Glib_jll v2.82.4+0 [3b182d85] + Graphite2_jll v1.3.15+0 [2e76f6c2] + HarfBuzz_jll v8.5.0+0 [1d5cc7b8] + IntelOpenMP_jll v2025.0.4+0 [c1c5ebd0] + LAME_jll v3.100.2+0 [1d63c593] + LLVMOpenMP_jll v18.1.7+0 [dd4b983a] + LZO_jll v2.10.3+0 ⌅ [e9f186c6] + Libffi_jll v3.2.2+2 [d4300ac3] + Libgcrypt_jll v1.11.1+0 [7add5ba3] + Libgpg_error_jll v1.51.1+0 [94ce4f54] + Libiconv_jll v1.18.0+0 [4b2f31a3] + Libmount_jll v2.41.0+0 [38a345b3] + Libuuid_jll v2.41.0+0 [856f044c] + MKL_jll v2025.0.1+1 [e7412a2a] + Ogg_jll v1.3.5+1 [656ef2d0] + OpenBLAS32_jll v0.3.29+0 [458c3c95] + OpenSSL_jll v3.0.16+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [91d4177d] + Opus_jll v1.3.3+0 [30392449] + Pixman_jll v0.44.2+0 ⌅ [f50d1b31] + Rmath_jll v0.4.3+0 [f4f2fc5b] + SCS_jll v3.2.7+0 [02c8fc9c] + XML2_jll v2.13.6+1 [aed1982a] + XSLT_jll v1.1.43+0 [4f6342f7] + Xorg_libX11_jll v1.8.6+3 [0c0b7dd1] + Xorg_libXau_jll v1.0.12+0 [a3789734] + Xorg_libXdmcp_jll v1.1.5+0 [1082639a] + Xorg_libXext_jll v1.3.6+3 [ea2f1a96] + Xorg_libXrender_jll v0.9.11+1 [14d82f49] + Xorg_libpthread_stubs_jll v0.1.2+0 [c7cfdc94] + Xorg_libxcb_jll v1.17.0+3 [c5fb5394] + Xorg_xtrans_jll v1.6.0+0 [a4ae2306] + libaom_jll v3.11.0+0 [0ac62f75] + libass_jll v0.15.2+0 [f638f0a6] + libfdk_aac_jll v2.0.3+0 [b53b4c65] + libpng_jll v1.6.47+0 [f27f6e37] + libvorbis_jll v1.3.7+2 [1317d2d5] + oneTBB_jll v2022.0.0+0 ⌅ [1270edf5] + x264_jll v2021.5.5+0 ⌅ [dfaa095f] + x265_jll v3.5.0+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 v1.6.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [4af54fe1] + LazyArtifacts v1.11.0 [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.11.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.2.0 [44cfe95a] + Pkg v1.11.0 [de0858da] + Printf v1.11.0 [9abbd945] + Profile v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization v1.11.0 [1a1011a3] + SharedArrays v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.11.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] + LibCURL_jll v8.6.0+0 [e37daf67] + LibGit2_jll v1.7.2+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.6+0 [14a3606d] + MozillaCACerts_jll v2023.12.12 [4536629a] + OpenBLAS_jll v0.3.27+1 [05823500] + OpenLibm_jll v0.8.5+0 [efcefdf7] + PCRE2_jll v10.42.0+1 [bea87d4a] + SuiteSparse_jll v7.7.0+0 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.59.0+0 [3f19e933] + p7zip_jll v17.4.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Building Conda ─────────────────→ `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/b19db3927f0db4151cb86d073689f2428e524576/build.log` Building PyCall ────────────────→ `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/9816a3826b0ebf49ab4926e2b18842ad8b5c8f04/build.log` Building CalibrateEmulateSample → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/b128c35dc0bc7c14a4595c0d2bf18dfeae33daa2/build.log` Installation completed after 112.46s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 1251.96s ################################################################################ # Testing # Testing CalibrateEmulateSample Status `/tmp/jl_mQTmNF/Project.toml` ⌅ [80f14c24] AbstractMCMC v4.4.2 ⌃ [5b7e9947] AdvancedMH v0.7.5 [95e48a1f] CalibrateEmulateSample v0.6.1 [8f4d0f93] Conda v1.10.2 [31c24e10] Distributions v0.25.118 [ffbed154] DocStringExtensions v0.9.4 [aa8a2aa5] EnsembleKalmanProcesses v2.3.1 [891a1506] GaussianProcesses v0.12.5 ⌃ [c7f686f2] MCMCChains v5.7.1 [49802e3a] ProgressBars v1.5.1 [438e738f] PyCall v1.96.4 [36c3bae2] RandomFeatures v0.3.4 [3646fa90] ScikitLearn v0.7.0 [860ef19b] StableRNGs v1.0.2 [10745b16] Statistics v1.11.1 ⌅ [2913bbd2] StatsBase v0.33.21 [37e2e46d] LinearAlgebra v1.11.0 [44cfe95a] Pkg v1.11.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_mQTmNF/Manifest.toml` [47edcb42] ADTypes v1.14.0 [14f7f29c] AMD v0.5.3 [621f4979] AbstractFFTs v1.5.0 ⌅ [80f14c24] AbstractMCMC v4.4.2 [1520ce14] AbstractTrees v0.4.5 [79e6a3ab] Adapt v4.3.0 ⌃ [5b7e9947] AdvancedMH v0.7.5 [66dad0bd] AliasTables v1.1.3 [dce04be8] ArgCheck v2.5.0 [7d9fca2a] Arpack v0.5.4 [4fba245c] ArrayInterface v7.18.0 [13072b0f] AxisAlgorithms v1.1.0 [39de3d68] AxisArrays v0.4.7 ⌅ [198e06fe] BangBang v0.3.40 [9718e550] Baselet v0.1.1 [6e4b80f9] BenchmarkTools v1.6.0 [62783981] BitTwiddlingConvenienceFunctions v0.1.6 [2a0fbf3d] CPUSummary v0.2.6 [95e48a1f] CalibrateEmulateSample v0.6.1 [d360d2e6] ChainRulesCore v1.25.1 [fb6a15b2] CloseOpenIntervals v0.1.13 [523fee87] CodecBzip2 v0.8.5 [944b1d66] CodecZlib v0.7.8 [bbf7d656] CommonSubexpressions v0.3.1 [f70d9fcc] CommonWorldInvalidations v1.0.0 [34da2185] Compat v4.16.0 [a33af91c] CompositionsBase v0.1.2 [8f4d0f93] Conda v1.10.2 [88cd18e8] ConsoleProgressMonitor v0.1.2 [187b0558] ConstructionBase v1.5.8 [f65535da] Convex v0.16.4 [adafc99b] CpuId v0.3.1 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.7.0 [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [244e2a9f] DefineSingletons v0.1.2 [163ba53b] DiffResults v1.1.0 [b552c78f] DiffRules v1.15.1 [a0c0ee7d] DifferentiationInterface v0.6.50 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.118 [ffbed154] DocStringExtensions v0.9.4 [fdbdab4c] ElasticArrays v1.2.12 [2904ab23] ElasticPDMats v0.2.3 [aa8a2aa5] EnsembleKalmanProcesses v2.3.1 [4e289a0a] EnumX v1.0.5 [c87230d0] FFMPEG v0.4.2 [7a1cc6ca] FFTW v1.8.1 ⌅ [442a2c76] FastGaussQuadrature v0.4.9 [1a297f60] FillArrays v1.13.0 [6a86dc24] FiniteDiff v2.27.0 [59287772] Formatting v0.4.3 ⌅ [f6369f11] ForwardDiff v0.10.38 [891a1506] GaussianProcesses v0.12.5 ⌃ [e4b2fa32] GaussianRandomFields v2.1.6 [3e5b6fbb] HostCPUFeatures v0.1.17 [615f187c] IfElse v0.1.1 [22cec73e] InitialValues v0.3.1 [842dd82b] InlineStrings v1.4.3 [a98d9a8b] Interpolations v0.15.1 [8197267c] IntervalSets v0.7.10 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 ⌅ [92d709cd] 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OrderedCollections v1.8.0 [90014a1f] PDMats v0.11.33 [d96e819e] Parameters v0.12.3 [69de0a69] Parsers v2.8.1 [1d0040c9] PolyesterWeave v0.2.2 [2dfb63ee] PooledArrays v1.4.3 [85a6dd25] PositiveFactorizations v0.2.4 ⌅ [aea7be01] PrecompileTools v1.2.1 [21216c6a] Preferences v1.4.3 [08abe8d2] PrettyTables v2.4.0 [49802e3a] ProgressBars v1.5.1 [33c8b6b6] ProgressLogging v0.1.4 [92933f4c] ProgressMeter v1.10.4 [43287f4e] PtrArrays v1.3.0 [438e738f] PyCall v1.96.4 [1fd47b50] QuadGK v2.11.2 [36c3bae2] RandomFeatures v0.3.4 [b3c3ace0] RangeArrays v0.3.2 [c84ed2f1] Ratios v0.4.5 [3cdcf5f2] RecipesBase v1.3.4 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 ⌅ [79098fc4] Rmath v0.7.1 [c946c3f1] SCS v2.1.0 [94e857df] SIMDTypes v0.1.0 [476501e8] SLEEFPirates v0.6.43 [30f210dd] ScientificTypesBase v3.0.0 [3646fa90] ScikitLearn v0.7.0 [6e75b9c4] ScikitLearnBase v0.5.0 [91c51154] SentinelArrays v1.4.8 [efcf1570] Setfield v1.1.2 [a2af1166] SortingAlgorithms v1.2.1 [276daf66] SpecialFunctions 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v1.2.0 [44cfe95a] Pkg v1.11.0 [de0858da] Printf v1.11.0 [9abbd945] Profile v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization v1.11.0 [1a1011a3] SharedArrays v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.11.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] LibCURL_jll v8.6.0+0 [e37daf67] LibGit2_jll v1.7.2+0 [29816b5a] LibSSH2_jll v1.11.0+1 [c8ffd9c3] MbedTLS_jll v2.28.6+0 [14a3606d] MozillaCACerts_jll v2023.12.12 [4536629a] OpenBLAS_jll v0.3.27+1 [05823500] OpenLibm_jll v0.8.5+0 [efcefdf7] PCRE2_jll v10.42.0+1 [bea87d4a] SuiteSparse_jll v7.7.0+0 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.59.0+0 [3f19e933] p7zip_jll v17.4.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... Starting tests for Emulator [ Info: fit successful SVD truncated at k: 3/6 [ Info: reducing input dimension from 10 to rank(input_cov) during low rank in normalization 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 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 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 SVD truncated at k: 2/6 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 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] 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] 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] 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] 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] 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] 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] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 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] 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] 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] 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] 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] 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] 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] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 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] 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] 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] 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] 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] 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] 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] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 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] 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] 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] 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] 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] 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] 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] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 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] 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] 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] 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] 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] 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] 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] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 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] 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] 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] 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] 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] 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] 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] Type: GaussianProcesses.Noise{Float64}, Params: [0.0] created GP: 6 Completed tests for Emulator, 222 seconds elapsed Starting tests for GaussianProcess WARNING: redefinition of constant Main.pykernels. This may fail, cause incorrect answers, or produce other errors. ┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov. └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/Emulator.jl:120 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: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov. └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/Emulator.jl:120 ┌ Warning: GaussianProcess already built. skipping... └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/GaussianProcess.jl:117 optimized hyperparameters of GP: 1 Type: SumKernel{SEIso{Float64}, Noise{Float64}} Type: SEIso{Float64}, Params: [0.4671112501723779, -0.11637219097092133] Type: Noise{Float64}, Params: [-2.7795647959619263] ┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov. └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/Emulator.jl:120 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: [0.467111250159053, -0.11637219100329507] Type: Noise{Float64}, Params: [-2.912614529741828] ┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov. └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/Emulator.jl:120 Using user-defined kernelPyObject 1**2 * RBF(length_scale=1) Learning additive white noise [ Info: Training kernel 1, [ Info: PyObject 1**2 * RBF(length_scale=1) + WhiteKernel(noise_level=1) ┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov. └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/Emulator.jl:120 ┌ Warning: GaussianProcess already built. skipping... └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/GaussianProcess.jl:236 SKlearn, already trained. continuing... 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 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: SumKernel{SEArd{Float64}, Noise{Float64}} Type: SEArd{Float64}, Params: [-0.034010033457988934, 2.6947372695174536, 1.9374032687140703] Type: Noise{Float64}, Params: [-0.19545576083347674] optimized hyperparameters of GP: 2 Type: SumKernel{SEArd{Float64}, Noise{Float64}} Type: SEArd{Float64}, Params: [2.040064043166128, -0.263116528583071, 2.0697093362932244] Type: Noise{Float64}, Params: [-0.08245764941529067] optimized hyperparameters of GP: 1 Type: SEArd{Float64}, Params: [-0.070755506795137, 2.7805790822912098, 1.879857339393294] optimized hyperparameters of GP: 2 Type: SEArd{Float64}, Params: [2.0918473623519653, -0.15767169342096962, 2.145456577252389] Completed tests for GaussianProcess, 65 seconds elapsed Starting tests for RandomFeature ┌ Info: Shrinkage scale: 1.0, (0 = none, 1 = revert to scaled Identity) └ shrinkage covariance condition number: 1.0 [ Info: NICE-adjusted covariance condition number: 5.665820537358531 [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 40, "cov_sample_multiplier" => 10.0, "n_features_opt" => 200, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 70, "cov_sample_multiplier" => 10.0, "n_features_opt" => 200, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 90, "cov_sample_multiplier" => 10.0, "n_features_opt" => 200, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10, "tikhonov" => 0) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 100, "cov_sample_multiplier" => 10.0, "n_features_opt" => 200, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10, "tikhonov" => 0) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 20, "cov_sample_multiplier" => 10.0, "n_features_opt" => 100, "train_fraction" => 0.8, "n_cross_val_sets" => 0, "n_iteration" => 10) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 30, "cov_sample_multiplier" => 10.0, "n_features_opt" => 100, "train_fraction" => 0.8, "n_cross_val_sets" => 0, "n_iteration" => 10, "tikhonov" => 0) [ Info: hyperparameter learning for 1 models using 50 training points, 50 validation points and 100 features estimate cov with 520 iterations... 0.2%┣ ┫ 1/520 [01:17 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "bad_option", "n_ensemble" => 20, "cov_sample_multiplier" => 10.0, "n_features_opt" => 100, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10) ┌ Warning: The covariance of the observational noise (a.k.a obs_noise_cov) is useful for data processing. Large approximation errors can occur without it. If possible, please provide it using the keyword obs_noise_cov. └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/Emulator.jl:120 [ Info: hyperparameter learning for 1 models using 40 training points, 10 validation points and 100 features ┌ Warning: ScalarRandomFeatureInterface already built. skipping... └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/ScalarRandomFeature.jl:325 ┌ Warning: VectorRandomFeatureInterface already built. skipping... └ @ CalibrateEmulateSample.Emulators ~/.julia/packages/CalibrateEmulateSample/wGWPe/src/VectorRandomFeature.jl:373 [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 30, "cov_sample_multiplier" => 10.0, "n_features_opt" => 150, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 40, "cov_sample_multiplier" => 10.0, "n_features_opt" => 150, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 60, "cov_sample_multiplier" => 10.0, "n_features_opt" => 150, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10, "tikhonov" => 0) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 70, "cov_sample_multiplier" => 10.0, "n_features_opt" => 150, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10, "tikhonov" => 0) [ Info: hyperparameter optimization with EKI configured with Dict{Any, Any}("inflation" => 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "ensemble", "n_ensemble" => 100, "cov_sample_multiplier" => 10.0, "n_features_opt" => 150, "train_fraction" => 0.8, "n_cross_val_sets" => 2, "n_iteration" => 10, "tikhonov" => 0) [ Info: hyperparameter learning for 2 models using 80 training points, 20 validation points and 150 features estimate cov with 220 iterations... 0.5%┣▏ ┫ 1/220 [00:00 0.0001, "localization" => EnsembleKalmanProcesses.Localizers.NoLocalization(), "accelerator" => NesterovAccelerator{Float64}(Float64[], 1.0), "scheduler" => DataMisfitController{Float64, String}(Int64[], 1000.0, "stop"), "cov_correction" => "shrinkage", "verbose" => false, "multithread" => "tullio", "n_ensemble" => 70, "cov_sample_multiplier" => 10.0, "n_features_opt" => 150, "train_fraction" => 0.7, "n_cross_val_sets" => 2, "n_iteration" => 10, "tikhonov" => 0) [ Info: hyperparameter learning using 70 training points, 30 validation points and 150 features estimate cov with 620 iterations... 0.0%┣ ┫ 0/620 [00:00<00:00, -0s/it]  0.2%┣ ┫ 1/620 [00:25