Package evaluation of ConformalPrediction on Julia 1.13.0-DEV.888 (0e1aa6c7eb*) started at 2025-07-23T03:25:11.454 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 9.79s ################################################################################ # Installation # Installing ConformalPrediction... Resolving package versions... Updating `~/.julia/environments/v1.13/Project.toml` [98bfc277] + ConformalPrediction v0.1.13 Updating `~/.julia/environments/v1.13/Manifest.toml` [47edcb42] + ADTypes v1.15.0 [621f4979] + AbstractFFTs v1.5.0 [7d9f7c33] + Accessors v0.1.42 [79e6a3ab] + Adapt v4.3.0 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [4fba245c] + ArrayInterface v7.19.0 [a9b6321e] + Atomix v1.1.1 [fbb218c0] + BSON v0.3.9 [198e06fe] + BangBang v0.4.4 [9718e550] + Baselet v0.1.1 [fa961155] + CEnum v0.5.0 [324d7699] + CategoricalArrays v0.10.8 [af321ab8] + CategoricalDistributions v0.1.15 [082447d4] + ChainRules v1.72.5 [d360d2e6] + ChainRulesCore v1.25.2 ⌅ [3da002f7] + ColorTypes v0.11.5 [bbf7d656] + CommonSubexpressions v0.3.1 [34da2185] + Compat v4.17.0 [a33af91c] + CompositionsBase v0.1.2 [ed09eef8] + ComputationalResources v0.3.2 [98bfc277] + ConformalPrediction v0.1.13 [187b0558] + ConstructionBase v1.6.0 [6add18c4] + ContextVariablesX v0.1.3 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [8bb1440f] + DelimitedFiles v1.9.1 [b429d917] + DensityInterface v0.4.0 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.15.1 [a0c0ee7d] + DifferentiationInterface v0.7.3 [31c24e10] + Distributions v0.25.120 [ffbed154] + DocStringExtensions v0.9.5 [4e289a0a] + EnumX v1.0.5 [cc61a311] + FLoops v0.2.2 [b9860ae5] + FLoopsBase v0.1.1 [5789e2e9] + FileIO v1.17.0 [1a297f60] + FillArrays v1.13.0 [6a86dc24] + FiniteDiff v2.27.0 [53c48c17] + FixedPointNumbers v0.8.5 ⌅ [587475ba] + Flux v0.14.25 [f6369f11] + ForwardDiff v1.0.1 ⌅ [d9f16b24] + Functors v0.4.12 [0c68f7d7] + GPUArrays v11.2.3 [46192b85] + GPUArraysCore v0.2.0 [076d061b] + HashArrayMappedTries v0.2.0 [34004b35] + HypergeometricFunctions v0.3.28 [7869d1d1] + IRTools v0.4.15 ⌅ [4846b161] + InferOpt v0.6.1 [22cec73e] + InitialValues v0.3.1 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.4 [42fd0dbc] + IterativeSolvers v0.9.4 [82899510] + IteratorInterfaceExtensions v1.0.0 [033835bb] + JLD2 v0.5.15 [692b3bcd] + JLLWrappers v1.7.0 [b14d175d] + JuliaVariables v0.2.4 [63c18a36] + KernelAbstractions v0.9.37 [929cbde3] + LLVM v9.4.2 [b964fa9f] + LaTeXStrings v1.4.0 [92ad9a40] + LearnAPI v1.0.1 [d3d80556] + LineSearches v7.4.0 [7a12625a] + LinearMaps v3.11.4 [2ab3a3ac] + LogExpFunctions v0.3.29 [c2834f40] + MLCore v1.0.0 ⌃ [7e8f7934] + MLDataDevices v1.5.3 [a7f614a8] + MLJBase v1.8.2 [50ed68f4] + MLJEnsembles v0.4.3 ⌅ [094fc8d1] + MLJFlux v0.5.1 [6ee0df7b] + MLJLinearModels v0.10.1 [e80e1ace] + MLJModelInterface v1.12.0 [d8e11817] + MLStyle v0.4.17 [f1d291b0] + MLUtils v0.4.8 [1914dd2f] + MacroTools v0.5.16 [dbeba491] + Metalhead v0.9.5 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [d41bc354] + NLSolversBase v7.10.0 [872c559c] + NNlib v0.9.30 [77ba4419] + NaNMath v1.1.3 [71a1bf82] + NameResolution v0.1.5 [0b1bfda6] + OneHotArrays v0.2.10 [429524aa] + Optim v1.13.2 ⌅ [3bd65402] + Optimisers v0.3.4 [bac558e1] + OrderedCollections v1.8.1 [90014a1f] + PDMats v0.11.35 [d96e819e] + Parameters v0.12.3 [570af359] + PartialFunctions v1.2.1 [85a6dd25] + PositiveFactorizations v0.2.4 [aea7be01] + PrecompileTools v1.3.2 [21216c6a] + Preferences v1.4.3 [8162dcfd] + PrettyPrint v0.2.0 [08abe8d2] + PrettyTables v2.4.0 [33c8b6b6] + ProgressLogging v0.1.5 [92933f4c] + ProgressMeter v1.10.4 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [c1ae055f] + RealDot v0.1.0 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [42d2dcc6] + Referenceables v0.1.3 [97f35ef4] + RequiredInterfaces v0.1.7 [ae029012] + Requires v1.3.1 [79098fc4] + Rmath v0.8.0 [321657f4] + ScientificTypes v3.1.0 [30f210dd] + ScientificTypesBase v3.0.0 [7e506255] + ScopedValues v1.4.0 [efcf1570] + Setfield v1.1.2 [605ecd9f] + ShowCases v0.1.0 [699a6c99] + SimpleTraits v0.9.4 [a2af1166] + SortingAlgorithms v1.2.1 [dc90abb0] + SparseInverseSubset v0.1.2 [276daf66] + SpecialFunctions v2.5.1 [171d559e] + SplittablesBase v0.1.15 [90137ffa] + StaticArrays v1.9.14 [1e83bf80] + StaticArraysCore v1.4.3 [c062fc1d] + StatisticalMeasuresBase v0.1.2 [64bff920] + StatisticalTraits v3.5.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.1 [2913bbd2] + StatsBase v0.34.5 [4c63d2b9] + StatsFuns v1.5.0 [892a3eda] + StringManipulation v0.4.1 [09ab397b] + StructArrays v0.7.1 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [ac1d9e8a] + ThreadsX v0.1.12 [3bb67fe8] + TranscodingStreams v0.11.3 [28d57a85] + Transducers v0.4.84 [3a884ed6] + UnPack v1.0.2 [013be700] + UnsafeAtomics v0.3.0 ⌅ [e88e6eb3] + Zygote v0.6.77 [700de1a5] + ZygoteRules v0.2.7 [dad2f222] + LLVMExtra_jll v0.0.37+2 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [0dad84c5] + ArgTools v1.1.2 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [f43a241f] + Downloads v1.7.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [4af54fe1] + LazyArtifacts v1.11.0 [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.13.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.13.0 [de0858da] + Printf v1.11.0 [3fa0cd96] + REPL v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings 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 v1.3.0+1 [deac9b47] + LibCURL_jll v8.14.1+1 [e37daf67] + LibGit2_jll v1.9.1+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.7.15 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.5+0 [458c3c95] + OpenSSL_jll v3.5.1+0 [efcefdf7] + PCRE2_jll v10.45.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.1+2 [8e850b90] + libblastrampoline_jll v5.13.1+0 [8e850ede] + nghttp2_jll v1.65.0+0 [3f19e933] + p7zip_jll v17.5.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Installation completed after 6.52s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 1331.98s ################################################################################ # Testing # Testing ConformalPrediction Test Could not use exact versions of packages in manifest, re-resolving. Note: if you do not check your manifest file into source control, then you can probably ignore this message. However, if you do check your manifest file into source control, then you probably want to pass the `allow_reresolve = false` kwarg when calling the `Pkg.test` function. Updating `/tmp/jl_FpL3HN/Project.toml` [4c88cf16] ↑ Aqua v0.8.4 ⇒ v0.8.13 [5224ae11] ↑ CompatHelperLocal v0.1.26 ⇒ v0.1.27 [98bfc277] + ConformalPrediction v0.1.13 [e30172f5] ↑ Documenter v1.3.0 ⇒ v1.14.1 ⌅ [f6006082] ↑ EvoTrees v0.16.6 ⇒ v0.16.9 ⌅ [7acf609c] ↑ LightGBM v0.6.1 ⇒ v0.7.2 [add582a8] ↑ MLJ v0.19.5 ⇒ v0.20.9 [c6f25543] ↑ MLJDecisionTreeInterface v0.4.1 ⇒ v0.4.2 ⌅ [094fc8d1] ↑ MLJFlux v0.4.0 ⇒ v0.5.1 [6ee0df7b] ↑ MLJLinearModels v0.10.0 ⇒ v0.10.1 [e80e1ace] ↑ MLJModelInterface v1.9.5 ⇒ v1.12.0 [91a5bcdd] ↑ Plots v1.40.2 ⇒ v1.40.17 [bd7198b4] ↑ TaijaPlotting v1.0.7 ⇒ v1.3.0 [8dfed614] ~ Test ⇒ v1.11.0 Updating `/tmp/jl_FpL3HN/Manifest.toml` [47edcb42] + ADTypes v1.15.0 [da404889] ↑ ARFFFiles v1.4.1 ⇒ v1.5.0 [7d9f7c33] + Accessors v0.1.42 [79e6a3ab] ↑ Adapt v4.0.3 ⇒ v4.3.0 [66dad0bd] + AliasTables v1.1.3 [4c88cf16] ↑ Aqua v0.8.4 ⇒ v0.8.13 [dce04be8] ↑ ArgCheck v2.3.0 ⇒ v2.5.0 [ec485272] - ArnoldiMethod v0.2.0 [4fba245c] ↑ ArrayInterface v7.9.0 ⇒ v7.19.0 [a9b6321e] ↑ Atomix v0.1.0 ⇒ v1.1.1 [a963bdd2] - AtomsBase v0.3.5 [ab4f0b2a] - BFloat16s v0.4.2 [198e06fe] ↑ BangBang v0.3.40 ⇒ v0.4.4 [6e4b80f9] - BenchmarkTools v1.5.0 [d1d4a3ce] ↑ BitFlags v0.1.8 ⇒ v0.1.9 [e1450e63] - BufferedStreams v1.2.1 [336ed68f] - CSV v0.10.13 [052768ef] - CUDA v5.2.0 [1af6417a] - CUDA_Runtime_Discovery v0.2.3 [49dc2e85] - Calculus v0.5.1 [af321ab8] ↑ CategoricalDistributions v0.1.14 ⇒ v0.1.15 [082447d4] ↑ ChainRules v1.63.0 ⇒ v1.72.5 [d360d2e6] ↑ ChainRulesCore v1.23.0 ⇒ v1.25.2 [46823bd8] - Chemfiles v0.10.41 [523fee87] - CodecBzip2 v0.8.2 [944b1d66] ↑ CodecZlib v0.7.4 ⇒ v0.7.8 [35d6a980] ↑ ColorSchemes v3.24.0 ⇒ v3.30.0 ⌅ [3da002f7] ↑ ColorTypes v0.11.4 ⇒ v0.11.5 [5ae59095] ↑ Colors v0.12.10 ⇒ v0.13.1 [861a8166] ↑ Combinatorics v1.0.2 ⇒ v1.0.3 [bbf7d656] ↑ CommonSubexpressions v0.3.0 ⇒ v0.3.1 [34da2185] ↑ Compat v4.14.0 ⇒ v4.17.0 [5224ae11] ↑ CompatHelperLocal v0.1.26 ⇒ v0.1.27 [f0e56b4a] ↑ ConcurrentUtilities v2.4.1 ⇒ v2.5.0 [98bfc277] ↑ ConformalPrediction v0.1.6 ⇒ v0.1.13 [187b0558] ↑ ConstructionBase v1.5.5 ⇒ v1.6.0 [d38c429a] ↑ Contour v0.6.2 ⇒ v0.6.3 ⌃ [2f13d31b] ↑ CounterfactualExplanations v0.1.31 ⇒ v1.1.6 [124859b0] - DataDeps v0.7.13 [a93c6f00] ↑ DataFrames v1.6.1 ⇒ v1.7.0 [864edb3b] ↑ DataStructures v0.18.18 ⇒ v0.18.22 [b429d917] + DensityInterface v0.4.0 [a0c0ee7d] + DifferentiationInterface v0.7.3 [b4f34e82] ↑ Distances v0.10.11 ⇒ v0.10.12 [31c24e10] ↑ Distributions v0.25.107 ⇒ v0.25.120 [ffbed154] ↑ DocStringExtensions v0.9.3 ⇒ v0.9.5 [e30172f5] ↑ Documenter v1.3.0 ⇒ v1.14.1 [fa6b7ba4] - DualNumbers v0.6.8 [4e289a0a] + EnumX v1.0.5 ⌅ [f6006082] ↑ EvoTrees v0.16.6 ⇒ v0.16.9 [460bff9d] ↑ ExceptionUnwrapping v0.1.10 ⇒ v0.1.11 [e2ba6199] - ExprTools v0.1.10 [411431e0] ↑ Extents v0.1.2 ⇒ v0.1.6 [c87230d0] ↑ FFMPEG v0.4.1 ⇒ v0.4.2 [cc61a311] ↑ FLoops v0.2.1 ⇒ v0.2.2 [33837fe5] + FeatureSelection v0.2.3 [5789e2e9] ↑ FileIO v1.16.3 ⇒ v1.17.0 [48062228] ↑ FilePathsBase v0.9.21 ⇒ v0.9.24 [1a297f60] ↑ FillArrays v1.9.3 ⇒ v1.13.0 [6a86dc24] ↑ FiniteDiff v2.23.0 ⇒ v2.27.0 [53c48c17] ↑ FixedPointNumbers v0.8.4 ⇒ v0.8.5 ⌅ [587475ba] ↑ Flux v0.14.14 ⇒ v0.14.25 [1fa38f19] ↑ Format v1.3.6 ⇒ v1.3.7 [f6369f11] ↑ ForwardDiff v0.10.36 ⇒ v1.0.1 ⌅ [d9f16b24] ↑ Functors v0.4.8 ⇒ v0.4.12 [0c68f7d7] ↑ GPUArrays v10.0.2 ⇒ v11.2.3 [46192b85] ↑ GPUArraysCore v0.1.6 ⇒ v0.2.0 [61eb1bfa] - GPUCompiler v0.25.0 [28b8d3ca] ↑ GR v0.73.3 ⇒ v0.73.17 [92fee26a] - GZip v0.6.2 [cf35fbd7] - GeoInterface v1.3.3 [5c1252a2] ↑ GeometryBasics v0.4.10 ⇒ v0.5.10 [d7ba0133] ↑ Git v1.3.1 ⇒ v1.4.0 [c27321d9] - Glob v1.3.1 [86223c79] - Graphs v1.9.0 [f67ccb44] - HDF5 v0.17.1 [cd3eb016] ↑ HTTP v1.10.4 ⇒ v1.10.17 [076d061b] + HashArrayMappedTries v0.2.0 [34004b35] ↑ HypergeometricFunctions v0.3.23 ⇒ v0.3.28 [b5f81e59] ↑ IOCapture v0.2.4 ⇒ v0.2.5 [7869d1d1] ↑ IRTools v0.4.12 ⇒ v0.4.15 [c817782e] - ImageBase v0.1.7 [a09fc81d] - ImageCore v0.10.2 [4e3cecfd] - ImageShow v0.3.8 ⌅ [4846b161] + InferOpt v0.6.1 [d25df0c9] - Inflate v0.1.4 [842dd82b] ↑ InlineStrings v1.4.0 ⇒ v1.4.4 [7d512f48] - InternedStrings v0.7.0 [3587e190] + InverseFunctions v0.1.17 [41ab1584] ↑ InvertedIndices v1.3.0 ⇒ v1.3.1 [92d709cd] ↑ IrrationalConstants v0.2.2 ⇒ v0.2.4 [033835bb] ↑ JLD2 v0.4.46 ⇒ v0.5.15 [1019f520] ↑ JLFzf v0.1.7 ⇒ v0.1.11 [692b3bcd] ↑ JLLWrappers v1.5.0 ⇒ v1.7.0 [0f8b85d8] - JSON3 v1.14.0 [63c18a36] ↑ KernelAbstractions v0.9.18 ⇒ v0.9.37 [929cbde3] ↑ LLVM v6.6.1 ⇒ v9.4.2 [8b046642] - LLVMLoopInfo v1.0.0 [b964fa9f] ↑ LaTeXStrings v1.3.1 ⇒ v1.4.0 [c52c1a26] ↑ LaplaceRedux v0.1.4 ⇒ v1.2.0 [23fbe1c1] ↑ Latexify v0.16.2 ⇒ v0.16.8 [0e77f7df] ↑ LazilyInitializedFields v1.2.2 ⇒ v1.3.0 [8cdb02fc] - LazyModules v0.3.1 [92ad9a40] + LearnAPI v1.0.1 ⌅ [7acf609c] ↑ LightGBM v0.6.1 ⇒ v0.7.2 [d3d80556] ↑ LineSearches v7.2.0 ⇒ v7.4.0 [7a12625a] ↑ LinearMaps v3.11.2 ⇒ v3.11.4 [2ab3a3ac] ↑ LogExpFunctions v0.3.27 ⇒ v0.3.29 [e6f89c97] ↑ LoggingExtras v1.0.3 ⇒ v1.1.0 [30fc2ffe] - LossFunctions v0.11.1 [23992714] - MAT v0.10.6 [c2834f40] + MLCore v1.0.0 ⌃ [7e8f7934] + MLDataDevices v1.5.3 [eb30cadb] - MLDatasets v0.7.14 ⌅ [64a0f543] ↑ MLFlowClient v0.4.6 ⇒ v0.5.1 [add582a8] ↑ MLJ v0.19.5 ⇒ v0.20.9 [45f359ea] + MLJBalancing v0.1.5 [a7f614a8] ↑ MLJBase v0.21.14 ⇒ v1.8.2 [c6f25543] ↑ MLJDecisionTreeInterface v0.4.1 ⇒ v0.4.2 [50ed68f4] ↑ MLJEnsembles v0.3.3 ⇒ v0.4.3 [7b7b8358] ↑ MLJFlow v0.1.1 ⇒ v0.5.0 ⌅ [094fc8d1] ↑ MLJFlux v0.4.0 ⇒ v0.5.1 [614be32b] ↑ MLJIteration v0.5.1 ⇒ v0.6.3 [6ee0df7b] ↑ MLJLinearModels v0.10.0 ⇒ v0.10.1 [e80e1ace] ↑ MLJModelInterface v1.9.5 ⇒ v1.12.0 [d491faf4] ↑ MLJModels v0.16.16 ⇒ v0.17.9 [03970b2e] ↑ MLJTuning v0.7.4 ⇒ v0.8.8 [f1d291b0] ↑ MLUtils v0.4.4 ⇒ v0.4.8 [3da0fdf6] - MPIPreferences v0.1.10 [1914dd2f] ↑ MacroTools v0.5.13 ⇒ v0.5.16 [06eb3307] - ManifoldLearning v0.9.0 [dbb5928d] - MappedArrays v0.4.2 [b8f27783] - MathOptInterface v1.27.0 [dbeba491] ↑ Metalhead v0.9.3 ⇒ v0.9.5 [128add7d] ↑ MicroCollections v0.1.4 ⇒ v0.2.0 [e1d29d7a] ↑ Missings v1.1.0 ⇒ v1.2.0 [e94cdb99] - MosaicViews v0.3.4 [6f286f6a] ↑ MultivariateStats v0.9.0 ⇒ v0.10.3 [d8a4904e] - MutableArithmetics v1.4.1 [d41bc354] ↑ NLSolversBase v7.8.3 ⇒ v7.10.0 [872c559c] ↑ NNlib v0.9.12 ⇒ v0.9.30 [15e1cf62] - NPZ v0.4.3 [5da4648a] - NVTX v0.3.4 [77ba4419] ↑ NaNMath v1.0.2 ⇒ v1.1.3 [b8a86587] ↑ NearestNeighbors v0.4.16 ⇒ v0.4.22 [46757867] ↑ NetworkLayout v0.4.6 ⇒ v0.4.10 [6fe1bfb0] - OffsetArrays v1.13.0 [0b1bfda6] ↑ OneHotArrays v0.2.5 ⇒ v0.2.10 [8b6db2d4] ↑ OpenML v0.3.1 ⇒ v0.3.2 [4d8831e6] ↑ OpenSSL v1.4.2 ⇒ v1.5.0 [429524aa] ↑ Optim v1.9.2 ⇒ v1.13.2 ⌅ [3bd65402] ↑ Optimisers v0.3.2 ⇒ v0.3.4 [bac558e1] ↑ OrderedCollections v1.6.3 ⇒ v1.8.1 [90014a1f] ↑ PDMats v0.11.31 ⇒ v0.11.35 [5432bcbf] - PaddedViews v0.5.12 [69de0a69] ↑ Parsers v2.8.1 ⇒ v2.8.3 [570af359] ↑ PartialFunctions v1.2.0 ⇒ v1.2.1 [7b2266bf] - PeriodicTable v1.2.1 [fbb45041] - Pickle v0.3.3 [b98c9c47] - Pipe v1.3.0 [ccf2f8ad] ↑ PlotThemes v3.1.0 ⇒ v3.3.0 [995b91a9] ↑ PlotUtils v1.4.1 ⇒ v1.4.3 [91a5bcdd] ↑ Plots v1.40.2 ⇒ v1.40.17 [aea7be01] ↑ PrecompileTools v1.2.1 ⇒ v1.3.2 [08abe8d2] ↑ PrettyTables v2.3.1 ⇒ v2.4.0 [33c8b6b6] ↑ ProgressLogging v0.1.4 ⇒ v0.1.5 [92933f4c] ↑ ProgressMeter v1.10.0 ⇒ v1.10.4 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] ↑ QuadGK v2.9.4 ⇒ v2.11.2 [74087812] - Random123 v1.7.0 [e6cf234a] - RandomNumbers v1.5.3 [42d2dcc6] + Referenceables v0.1.3 [97f35ef4] + RequiredInterfaces v0.1.7 [ae029012] ↑ Requires v1.3.0 ⇒ v1.3.1 [79098fc4] ↑ Rmath v0.7.1 ⇒ v0.8.0 [321657f4] ↑ ScientificTypes v3.0.2 ⇒ v3.1.0 [7e506255] + ScopedValues v1.4.0 [6c6a2e73] ↑ Scratch v1.2.1 ⇒ v1.3.0 [91c51154] ↑ SentinelArrays v1.4.1 ⇒ v1.4.8 [efcf1570] ↑ Setfield v1.1.1 ⇒ v1.1.2 [777ac1f9] ↑ SimpleBufferStream v1.1.0 ⇒ v1.2.0 [276daf66] ↑ SpecialFunctions v2.3.1 ⇒ v2.5.1 [860ef19b] ↑ StableRNGs v1.0.1 ⇒ v1.0.3 [cae243ae] - StackViews v0.1.1 [90137ffa] ↑ StaticArrays v1.9.3 ⇒ v1.9.14 [1e83bf80] ↑ StaticArraysCore v1.4.2 ⇒ v1.4.3 [a19d573c] + StatisticalMeasures v0.2.1 [c062fc1d] + StatisticalMeasuresBase v0.1.2 [64bff920] ↑ StatisticalTraits v3.2.0 ⇒ v3.5.0 [10745b16] ↑ Statistics v1.10.0 ⇒ v1.11.1 [82ae8749] ↑ StatsAPI v1.7.0 ⇒ v1.7.1 [2913bbd2] ↑ StatsBase v0.33.21 ⇒ v0.34.5 [4c63d2b9] ↑ StatsFuns v1.3.1 ⇒ v1.5.0 [5e0ebb24] - Strided v1.2.3 [69024149] - StringEncodings v0.3.7 [892a3eda] ↑ StringManipulation v0.3.4 ⇒ v0.4.1 [09ab397b] ↑ StructArrays v0.6.18 ⇒ v0.7.1 [856f2bd8] - StructTypes v1.10.0 [bd369af6] ↑ Tables v1.11.1 ⇒ v1.12.1 [10284c91] + TaijaBase v1.2.3 [bd7198b4] ↑ TaijaPlotting v1.0.7 ⇒ v1.3.0 [ac1d9e8a] + ThreadsX v0.1.12 [a759f4b9] - TimerOutputs v0.5.23 [3bb67fe8] ↑ TranscodingStreams v0.10.5 ⇒ v0.11.3 [28d57a85] ↑ Transducers v0.4.80 ⇒ v0.4.84 [592b5752] + Trapz v2.0.3 [bc48ee85] ↑ Tullio v0.3.7 ⇒ v0.3.8 [9d95972d] - TupleTools v1.5.0 [5c2747f8] ↑ URIs v1.5.1 ⇒ v1.6.1 [1986cc42] ↑ Unitful v1.19.0 ⇒ v1.23.1 [a7773ee8] - UnitfulAtomic v1.0.0 [45397f5d] ↑ UnitfulLatexify v1.6.3 ⇒ v1.7.0 [013be700] ↑ UnsafeAtomics v0.2.1 ⇒ v0.3.0 [d80eeb9a] - UnsafeAtomicsLLVM v0.1.3 [ea10d353] - WeakRefStrings v1.4.2 [76eceee3] - WorkerUtilities v1.6.1 [a5390f91] - ZipFile v0.10.1 ⌅ [e88e6eb3] ↑ Zygote v0.6.69 ⇒ v0.6.77 [700de1a5] ↑ ZygoteRules v0.2.5 ⇒ v0.2.7 [02a925ec] - cuDNN v1.3.0 [6e34b625] ↑ Bzip2_jll v1.0.8+1 ⇒ v1.0.9+0 [4ee394cb] - CUDA_Driver_jll v0.7.0+1 [76a88914] - CUDA_Runtime_jll v0.11.1+0 [62b44479] - CUDNN_jll v8.9.4+0 [83423d85] ↑ Cairo_jll v1.18.0+1 ⇒ v1.18.5+0 [78a364fa] - Chemfiles_jll v0.10.4+0 [ee1fde0b] + Dbus_jll v1.16.2+0 [2702e6a9] ↑ EpollShim_jll v0.0.20230411+0 ⇒ v0.0.20230411+1 [2e619515] ↑ Expat_jll v2.5.0+0 ⇒ v2.6.5+0 [a3f928ae] ↑ Fontconfig_jll v2.13.93+0 ⇒ v2.16.0+0 [d7e528f0] ↑ FreeType2_jll v2.13.1+0 ⇒ v2.13.4+0 [559328eb] ↑ FriBidi_jll v1.0.10+0 ⇒ v1.0.17+0 [0656b61e] ↑ GLFW_jll v3.3.9+0 ⇒ v3.4.0+2 [d2c73de3] ↑ GR_jll v0.73.3+0 ⇒ v0.73.17+0 [b0724c58] + GettextRuntime_jll v0.22.4+0 [78b55507] - Gettext_jll v0.21.0+0 [f8c6e375] ↑ Git_jll v2.44.0+1 ⇒ v2.50.1+0 [7746bdde] ↑ Glib_jll v2.80.0+0 ⇒ v2.84.3+0 [3b182d85] ↑ Graphite2_jll v1.3.14+0 ⇒ v1.3.15+0 [0234f1f7] - HDF5_jll v1.14.2+1 [2e76f6c2] ↑ HarfBuzz_jll v2.8.1+1 ⇒ v8.5.1+0 [e33a78d0] - Hwloc_jll v2.10.0+0 [aacddb02] ↑ JpegTurbo_jll v3.0.2+0 ⇒ v3.1.1+0 [9c1d0b0a] - JuliaNVTXCallbacks_jll v0.2.1+0 [c1c5ebd0] ↑ LAME_jll v3.100.1+0 ⇒ v3.100.3+0 [88015f11] ↑ LERC_jll v3.0.0+1 ⇒ v4.0.1+0 [dad2f222] ↑ LLVMExtra_jll v0.0.29+0 ⇒ v0.0.37+2 [1d63c593] ↑ LLVMOpenMP_jll v15.0.7+0 ⇒ v18.1.8+0 [dd4b983a] ↑ LZO_jll v2.10.1+0 ⇒ v2.10.3+0 [e9f186c6] ↑ Libffi_jll v3.2.2+1 ⇒ v3.4.7+0 [d4300ac3] - Libgcrypt_jll v1.8.7+0 [7e76a0d4] ↑ Libglvnd_jll v1.6.0+0 ⇒ v1.7.1+1 [7add5ba3] - Libgpg_error_jll v1.42.0+0 [94ce4f54] ↑ Libiconv_jll v1.17.0+0 ⇒ v1.18.0+0 [4b2f31a3] ↑ Libmount_jll v2.39.3+0 ⇒ v2.41.0+0 [89763e89] ↑ Libtiff_jll v4.5.1+1 ⇒ v4.7.1+0 [38a345b3] ↑ Libuuid_jll v2.39.3+1 ⇒ v2.41.0+0 ⌅ [0e4427ef] + LightGBM_jll v3.3.5+1 [7cb0a576] - MPICH_jll v4.2.0+0 [f1f71cc9] - MPItrampoline_jll v5.3.2+0 [c8ffd9c3] ↑ MbedTLS_jll v2.28.2+1 ⇒ v2.28.6+2 [9237b28f] - MicrosoftMPI_jll v10.1.4+2 [e98f9f5b] - NVTX_jll v3.1.0+2 [e7412a2a] ↑ Ogg_jll v1.3.5+1 ⇒ v1.3.6+0 [fe0851c0] - OpenMPI_jll v5.0.2+0 [9bd350c2] + OpenSSH_jll v10.0.1+0 [efe28fd5] ↑ OpenSpecFun_jll v0.5.5+0 ⇒ v0.5.6+0 [91d4177d] ↑ Opus_jll v1.3.2+0 ⇒ v1.5.2+0 [32165bc3] - PMIx_jll v4.2.7+0 [36c8627f] + Pango_jll v1.56.3+0 ⌅ [30392449] ↑ Pixman_jll v0.42.2+0 ⇒ v0.44.2+0 [c0090381] ↑ Qt6Base_jll v6.5.3+1 ⇒ v6.8.2+1 [629bc702] + Qt6Declarative_jll v6.8.2+1 [ce943373] + Qt6ShaderTools_jll v6.8.2+1 [e99dba38] + Qt6Wayland_jll v6.8.2+1 [f50d1b31] ↑ Rmath_jll v0.4.0+0 ⇒ v0.5.1+0 [a2964d1f] ↑ Wayland_jll v1.21.0+1 ⇒ v1.24.0+0 [2381bf8a] - Wayland_protocols_jll v1.31.0+0 [02c8fc9c] - XML2_jll v2.12.5+0 [aed1982a] - XSLT_jll v1.1.34+0 [ffd25f8a] ↑ XZ_jll v5.6.1+0 ⇒ v5.8.1+0 [f67eecfb] ↑ Xorg_libICE_jll v1.0.10+1 ⇒ v1.1.2+0 [c834827a] ↑ Xorg_libSM_jll v1.2.3+0 ⇒ v1.2.6+0 [4f6342f7] ↑ Xorg_libX11_jll v1.8.6+0 ⇒ v1.8.12+0 [0c0b7dd1] ↑ Xorg_libXau_jll v1.0.11+0 ⇒ v1.0.13+0 [935fb764] ↑ Xorg_libXcursor_jll v1.2.0+4 ⇒ v1.2.4+0 [a3789734] ↑ Xorg_libXdmcp_jll v1.1.4+0 ⇒ v1.1.6+0 [1082639a] ↑ Xorg_libXext_jll v1.3.4+4 ⇒ v1.3.7+0 [d091e8ba] ↑ Xorg_libXfixes_jll v5.0.3+4 ⇒ v6.0.1+0 [a51aa0fd] ↑ Xorg_libXi_jll v1.7.10+4 ⇒ v1.8.3+0 [d1454406] ↑ Xorg_libXinerama_jll v1.1.4+4 ⇒ v1.1.6+0 [ec84b674] ↑ Xorg_libXrandr_jll v1.5.2+4 ⇒ v1.5.5+0 [ea2f1a96] ↑ Xorg_libXrender_jll v0.9.10+4 ⇒ v0.9.12+0 [14d82f49] - Xorg_libpthread_stubs_jll v0.1.1+0 [c7cfdc94] ↑ Xorg_libxcb_jll v1.15.0+0 ⇒ v1.17.1+0 [cc61e674] ↑ Xorg_libxkbfile_jll v1.1.2+0 ⇒ v1.1.3+0 [e920d4aa] ↑ Xorg_xcb_util_cursor_jll v0.1.4+0 ⇒ v0.1.5+0 [12413925] ↑ Xorg_xcb_util_image_jll v0.4.0+1 ⇒ v0.4.1+0 [2def613f] ↑ Xorg_xcb_util_jll v0.4.0+1 ⇒ v0.4.1+0 [975044d2] ↑ Xorg_xcb_util_keysyms_jll v0.4.0+1 ⇒ v0.4.1+0 [0d47668e] ↑ Xorg_xcb_util_renderutil_jll v0.3.9+1 ⇒ v0.3.10+0 [c22f9ab0] ↑ Xorg_xcb_util_wm_jll v0.4.1+1 ⇒ v0.4.2+0 [35661453] ↑ Xorg_xkbcomp_jll v1.4.6+0 ⇒ v1.4.7+0 [33bec58e] ↑ Xorg_xkeyboard_config_jll v2.39.0+0 ⇒ v2.44.0+0 [c5fb5394] ↑ Xorg_xtrans_jll v1.5.0+0 ⇒ v1.6.0+0 [35ca27e7] ↑ eudev_jll v3.2.9+0 ⇒ v3.2.14+0 [214eeab7] ↑ fzf_jll v0.43.0+0 ⇒ v0.61.1+0 [1a1c6b14] - gperf_jll v3.1.1+0 [477f73a3] - libaec_jll v1.1.2+0 [a4ae2306] ↑ libaom_jll v3.4.0+0 ⇒ v3.12.1+0 ⌅ [0ac62f75] ↑ libass_jll v0.15.1+0 ⇒ v0.15.2+0 [1183f4f0] + libdecor_jll v0.2.2+0 [2db6ffa8] ↑ libevdev_jll v1.11.0+0 ⇒ v1.13.4+0 [1080aeaf] - libevent_jll v2.1.13+1 [f638f0a6] ↑ libfdk_aac_jll v2.0.2+0 ⇒ v2.0.4+0 [36db933b] ↑ libinput_jll v1.18.0+0 ⇒ v1.28.1+0 [b53b4c65] ↑ libpng_jll v1.6.43+1 ⇒ v1.6.50+0 [f27f6e37] ↑ libvorbis_jll v1.3.7+1 ⇒ v1.3.8+0 [009596ad] ↑ mtdev_jll v1.1.6+0 ⇒ v1.1.7+0 [eb928a42] - prrte_jll v3.0.2+0 [d8fb68d0] ↑ xkbcommon_jll v1.4.1+1 ⇒ v1.9.2+0 [0dad84c5] ↑ ArgTools v1.1.1 ⇒ 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 ⇒ v1.7.0 [7b1f6079] ~ FileWatching ⇒ v1.11.0 [9fa8497b] ~ Future ⇒ v1.11.0 [b77e0a4c] ~ InteractiveUtils ⇒ v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [4af54fe1] ~ LazyArtifacts ⇒ v1.11.0 [76f85450] ~ LibGit2 ⇒ v1.11.0 [8f399da3] ~ Libdl ⇒ v1.11.0 [37e2e46d] ~ LinearAlgebra ⇒ v1.13.0 [56ddb016] ~ Logging ⇒ v1.11.0 [d6f4376e] ~ Markdown ⇒ v1.11.0 [a63ad114] ~ Mmap ⇒ v1.11.0 [ca575930] ↑ NetworkOptions v1.2.0 ⇒ v1.3.0 [44cfe95a] ↑ Pkg v1.10.0 ⇒ v1.13.0 [de0858da] ~ Printf ⇒ v1.11.0 [9abbd945] - Profile [3fa0cd96] ~ REPL ⇒ v1.11.0 [9a3f8284] ~ Random ⇒ v1.11.0 [9e88b42a] ~ Serialization ⇒ v1.11.0 [1a1011a3] - SharedArrays [6462fe0b] ~ Sockets ⇒ v1.11.0 [2f01184e] ↑ SparseArrays v1.10.0 ⇒ v1.13.0 [f489334b] + StyledStrings v1.11.0 [8dfed614] ~ Test ⇒ v1.11.0 [cf7118a7] ~ UUIDs ⇒ v1.11.0 [4ec0a83e] ~ Unicode ⇒ v1.11.0 [e66e0078] ↑ CompilerSupportLibraries_jll v1.1.0+0 ⇒ v1.3.0+1 [deac9b47] ↑ LibCURL_jll v8.4.0+0 ⇒ v8.14.1+1 [e37daf67] ↑ LibGit2_jll v1.6.4+0 ⇒ v1.9.1+0 [29816b5a] ↑ LibSSH2_jll v1.11.0+1 ⇒ v1.11.3+1 [14a3606d] ↑ MozillaCACerts_jll v2023.1.10 ⇒ v2025.7.15 [4536629a] ↑ OpenBLAS_jll v0.3.23+4 ⇒ v0.3.29+0 [05823500] ↑ OpenLibm_jll v0.8.1+2 ⇒ v0.8.5+0 [458c3c95] ↑ OpenSSL_jll v3.0.13+0 ⇒ v3.5.1+0 [efcefdf7] ↑ PCRE2_jll v10.42.0+1 ⇒ v10.45.0+0 [bea87d4a] ↑ SuiteSparse_jll v7.2.1+1 ⇒ v7.10.1+0 [83775a58] ↑ Zlib_jll v1.2.13+1 ⇒ v1.3.1+2 [3161d3a3] ↑ Zstd_jll v1.5.5+0 ⇒ v1.5.7+1 [8e850b90] ↑ libblastrampoline_jll v5.8.0+1 ⇒ v5.13.1+0 [8e850ede] ↑ nghttp2_jll v1.52.0+1 ⇒ v1.65.0+0 [3f19e933] ↑ p7zip_jll v17.4.0+2 ⇒ v17.5.0+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` Test Successfully re-resolved Status `/tmp/jl_FpL3HN/Project.toml` [4c88cf16] Aqua v0.8.13 [5224ae11] CompatHelperLocal v0.1.27 [98bfc277] ConformalPrediction v0.1.13 [7806a523] DecisionTree v0.12.4 [e30172f5] Documenter v1.14.1 ⌅ [f6006082] EvoTrees v0.16.9 ⌅ [7acf609c] LightGBM v0.7.2 [add582a8] MLJ v0.20.9 [c6f25543] MLJDecisionTreeInterface v0.4.2 ⌅ [094fc8d1] MLJFlux v0.5.1 [6ee0df7b] MLJLinearModels v0.10.1 [e80e1ace] MLJModelInterface v1.12.0 [636a865e] NearestNeighborModels v0.2.3 [91a5bcdd] Plots v1.40.17 [bd7198b4] TaijaPlotting v1.3.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_FpL3HN/Manifest.toml` [47edcb42] ADTypes v1.15.0 [a4c015fc] ANSIColoredPrinters v0.0.1 [da404889] ARFFFiles v1.5.0 [621f4979] AbstractFFTs v1.5.0 [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.42 [79e6a3ab] Adapt v4.3.0 [66dad0bd] AliasTables v1.1.3 [4c88cf16] Aqua v0.8.13 [dce04be8] ArgCheck v2.5.0 [7d9fca2a] Arpack v0.5.4 [4fba245c] ArrayInterface v7.19.0 [a9b6321e] Atomix v1.1.1 [fbb218c0] BSON v0.3.9 [198e06fe] BangBang v0.4.4 [9718e550] Baselet v0.1.1 [d1d4a3ce] BitFlags v0.1.9 [fa961155] CEnum v0.5.0 [324d7699] CategoricalArrays v0.10.8 [af321ab8] CategoricalDistributions v0.1.15 [082447d4] ChainRules v1.72.5 [d360d2e6] ChainRulesCore v1.25.2 [944b1d66] CodecZlib v0.7.8 [35d6a980] ColorSchemes v3.30.0 ⌅ [3da002f7] ColorTypes v0.11.5 ⌃ [c3611d14] ColorVectorSpace v0.10.0 [5ae59095] Colors v0.13.1 [861a8166] Combinatorics v1.0.3 [bbf7d656] CommonSubexpressions v0.3.1 [34da2185] Compat v4.17.0 [5224ae11] CompatHelperLocal v0.1.27 [a33af91c] CompositionsBase v0.1.2 [ed09eef8] ComputationalResources v0.3.2 [f0e56b4a] ConcurrentUtilities v2.5.0 [98bfc277] ConformalPrediction v0.1.13 [187b0558] ConstructionBase v1.6.0 [6add18c4] ContextVariablesX v0.1.3 [d38c429a] Contour v0.6.3 ⌃ [2f13d31b] CounterfactualExplanations v1.1.6 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.7.0 [864edb3b] DataStructures v0.18.22 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[8e850ede] nghttp2_jll v1.65.0+0 [3f19e933] p7zip_jll v17.5.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... Precompiling packages... 46912.2 ms ✓ Documenter 1 dependency successfully precompiled in 48 seconds. 51 already precompiled. Precompiling packages... 8505.5 ms ✓ Latexify → DataFramesExt 2556.7 ms ✓ Unitful → ForwardDiffExt 40835.2 ms ✓ CounterfactualExplanations 19738.6 ms ✓ Plots → UnitfulExt 37663.2 ms ✓ CounterfactualExplanations → LaplaceReduxExt 35956.5 ms ✓ CounterfactualExplanations → DecisionTreeExt 67009.4 ms ✓ TaijaPlotting 7 dependencies successfully precompiled in 222 seconds. 405 already precompiled. ┌ Warning: Unable to determine HTML(edit_link = ...) from remote HEAD branch, defaulting to "master". │ Calling `git remote` failed with an exception. Set JULIA_DEBUG=Documenter to see the error. │ Unless this is due to a configuration error, the relevant variable should be set explicitly. └ @ Documenter ~/.julia/packages/Documenter/eoWm2/src/utilities/utilities.jl:665 [ Info: SetupBuildDirectory: setting up build directory. [ Info: Doctest: running doctests. [ Info: Skipped ExpandTemplates step (doctest only). [ Info: Skipped CrossReferences step (doctest only). [ Info: Skipped CheckDocument step (doctest only). [ Info: Skipped Populate step (doctest only). [ Info: Skipped RenderDocument step (doctest only). Test Summary: | Pass Total Time Doctests: ConformalPrediction | 1 1 53.9s [ Info: For silent loading, specify `verbosity=0`. import NearestNeighborModels ✔ [ Info: Training machine(AdaptiveInductiveClassifier(model = KNNClassifier(K = 5, …), …), …). [ Info: Training machine(AdaptiveInductiveClassifier(model = KNNClassifier(K = 5, …), …), …). [ Info: Training machine(AdaptiveInductiveClassifier(model = KNNClassifier(K = 5, …), …), …). [ Info: Training machine(NaiveClassifier(model = KNNClassifier(K = 5, …), …), …). [ Info: Training machine(NaiveClassifier(model = KNNClassifier(K = 5, …), …), …). [ Info: Training machine(NaiveClassifier(model = KNNClassifier(K = 5, …), …), …). [ Info: Training machine(SimpleInductiveClassifier(model = KNNClassifier(K = 5, …), …), …). [ Info: Training machine(SimpleInductiveClassifier(model = KNNClassifier(K = 5, …), …), …). [ Info: Training machine(SimpleInductiveClassifier(model = KNNClassifier(K = 5, …), …), …). [ Info: For silent loading, specify `verbosity=0`. import EvoTreesPrecompiling packages... 16577.3 ms ✓ EvoTrees 1 dependency successfully precompiled in 20 seconds. 88 already precompiled. Precompiling packages... 21582.9 ms ✓ Plots → GeometryBasicsExt 1 dependency successfully precompiled in 24 seconds. 186 already precompiled. ✔ ┌ Info: Training machine(AdaptiveInductiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - tree_type: binary │ - rng: Random.MersenneTwister(123) └ , …), …). ┌ Info: Training machine(AdaptiveInductiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 8016, 7014, 286)) └ , …), …). ┌ Info: Training machine(AdaptiveInductiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 15030, 14028, 172)) └ , …), …). ┌ Info: Training machine(NaiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 22044, 21042, 158)) └ , …), …). ┌ Info: Training machine(NaiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 36072, 35070, 30)) └ , …), …). ┌ Info: Training machine(NaiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 49098, 48096, 504)) └ , …), …). ┌ Info: Training machine(SimpleInductiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 63126, 62124, 76)) └ , …), …). ┌ Info: Training machine(SimpleInductiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 160320, 159318, 779)) └ , …), …). ┌ Info: Training machine(SimpleInductiveClassifier(model = EvoTrees.EvoTreeClassifier{EvoTrees.MLogLoss} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 167334, 166332, 665)) └ , …), …). [ Info: For silent loading, specify `verbosity=0`. import MLJDecisionTreeInterface ✔ [ Info: Training machine(AdaptiveInductiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …). [ Info: Training machine(AdaptiveInductiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …). [ Info: Training machine(AdaptiveInductiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …). [ Info: Training machine(NaiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …). [ Info: Training machine(NaiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …). [ Info: Training machine(NaiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …). [ Info: Training machine(SimpleInductiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …). [ Info: Training machine(SimpleInductiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …). [ Info: Training machine(SimpleInductiveClassifier(model = RandomForestClassifier(max_depth = -1, …), …), …). [ Info: For silent loading, specify `verbosity=0`. import MLJLinearModels ✔ [ Info: Training machine(AdaptiveInductiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …). ┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}} │ optim_options: Optim.Options{Float64, Nothing} └ lbfgs_options: @NamedTuple{} NamedTuple() [ Info: Training machine(AdaptiveInductiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …). ┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}} │ optim_options: Optim.Options{Float64, Nothing} └ lbfgs_options: @NamedTuple{} NamedTuple() [ Info: Training machine(AdaptiveInductiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …). ┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}} │ optim_options: Optim.Options{Float64, Nothing} └ lbfgs_options: @NamedTuple{} NamedTuple() [ Info: Training machine(NaiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …). ┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}} │ optim_options: Optim.Options{Float64, Nothing} └ lbfgs_options: @NamedTuple{} NamedTuple() [ Info: Training machine(NaiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …). ┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}} │ optim_options: Optim.Options{Float64, Nothing} └ lbfgs_options: @NamedTuple{} NamedTuple() [ Info: Training machine(NaiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …). ┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}} │ optim_options: Optim.Options{Float64, Nothing} └ lbfgs_options: @NamedTuple{} NamedTuple() [ Info: Training machine(SimpleInductiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …). ┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}} │ optim_options: Optim.Options{Float64, Nothing} └ lbfgs_options: @NamedTuple{} NamedTuple() [ Info: Training machine(SimpleInductiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …). ┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}} │ optim_options: Optim.Options{Float64, Nothing} └ lbfgs_options: @NamedTuple{} NamedTuple() [ Info: Training machine(SimpleInductiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …). ┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}} │ optim_options: Optim.Options{Float64, Nothing} └ lbfgs_options: @NamedTuple{} NamedTuple() [ Info: For silent loading, specify `verbosity=0`. import MLJDecisionTreeInterface ✔ [ Info: Training machine(AdaptiveInductiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …). [ Info: Training machine(AdaptiveInductiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …). [ Info: Training machine(AdaptiveInductiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …). [ Info: Training machine(NaiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …). [ Info: Training machine(NaiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …). [ Info: Training machine(NaiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …). [ Info: Training machine(SimpleInductiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …). [ Info: Training machine(SimpleInductiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …). [ Info: Training machine(SimpleInductiveClassifier(model = DecisionTreeClassifier(max_depth = -1, …), …), …). [ Info: For silent loading, specify `verbosity=0`. import MLJLinearModels ✔ ┌ Warning: This test is skipped as the method is not suitable for Quantile Regression └ @ Main ~/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:80 [ Info: Training machine(CVPlusRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(CVPlusRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). [ Info: Training machine(NaiveRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). ┌ Info: Solver: MLJLinearModels.Analytical │ iterative: Bool false └ max_inner: Int64 200 [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(NaiveRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). ┌ Info: Solver: MLJLinearModels.Analytical │ iterative: Bool false └ max_inner: Int64 200 [ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). [ Info: Training machine(JackknifePlusAbRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(JackknifePlusAbRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). [ Info: Training machine(JackknifePlusRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(JackknifePlusRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). [ Info: Training machine(CVMinMaxRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(CVMinMaxRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). [ Info: Training machine(JackknifeRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). ┌ Info: Solver: MLJLinearModels.Analytical │ iterative: Bool false └ max_inner: Int64 200 [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(JackknifeRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). ┌ Info: Solver: MLJLinearModels.Analytical │ iterative: Bool false └ max_inner: Int64 200 [ Info: Training machine(JackknifeMinMaxRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(JackknifeMinMaxRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). [ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = RidgeRegressor(lambda = 1.0, …), …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = RidgeRegressor(lambda = 1.0, …), …), …). [ Info: Training machine(SimpleInductiveRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). ┌ Info: Solver: MLJLinearModels.Analytical │ iterative: Bool false └ max_inner: Int64 200 [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(SimpleInductiveRegressor(model = RidgeRegressor(lambda = 1.0, …), …), …). ┌ Info: Solver: MLJLinearModels.Analytical │ iterative: Bool false └ max_inner: Int64 200 [ Info: For silent loading, specify `verbosity=0`. import MLJLinearModels ✔ ┌ Warning: This test is skipped as the method is not suitable for Quantile Regression └ @ Main ~/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:80 [ Info: Training machine(CVPlusRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(CVPlusRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). [ Info: Training machine(NaiveRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). ┌ Info: Solver: MLJLinearModels.ProxGrad │ accel: Bool true │ max_iter: Int64 1000 │ tol: Float64 0.0001 │ max_inner: Int64 100 │ beta: Float64 0.8 └ gram: Bool false [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(NaiveRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). ┌ Info: Solver: MLJLinearModels.ProxGrad │ accel: Bool true │ max_iter: Int64 1000 │ tol: Float64 0.0001 │ max_inner: Int64 100 │ beta: Float64 0.8 └ gram: Bool false [ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). [ Info: Training machine(JackknifePlusAbRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(JackknifePlusAbRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). [ Info: Training machine(JackknifePlusRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(JackknifePlusRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). [ Info: Training machine(CVMinMaxRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(CVMinMaxRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). [ Info: Training machine(JackknifeRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). ┌ Info: Solver: MLJLinearModels.ProxGrad │ accel: Bool true │ max_iter: Int64 1000 │ tol: Float64 0.0001 │ max_inner: Int64 100 │ beta: Float64 0.8 └ gram: Bool false [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(JackknifeRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). ┌ Info: Solver: MLJLinearModels.ProxGrad │ accel: Bool true │ max_iter: Int64 1000 │ tol: Float64 0.0001 │ max_inner: Int64 100 │ beta: Float64 0.8 └ gram: Bool false [ Info: Training machine(JackknifeMinMaxRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(JackknifeMinMaxRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). [ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = LassoRegressor(lambda = 1.0, …), …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(TimeSeriesRegressorEnsembleBatch(model = LassoRegressor(lambda = 1.0, …), …), …). [ Info: Training machine(SimpleInductiveRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). ┌ Info: Solver: MLJLinearModels.ProxGrad │ accel: Bool true │ max_iter: Int64 1000 │ tol: Float64 0.0001 │ max_inner: Int64 100 │ beta: Float64 0.8 └ gram: Bool false [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. [ Info: Training machine(SimpleInductiveRegressor(model = LassoRegressor(lambda = 1.0, …), …), …). ┌ Info: Solver: MLJLinearModels.ProxGrad │ accel: Bool true │ max_iter: Int64 1000 │ tol: Float64 0.0001 │ max_inner: Int64 100 │ beta: Float64 0.8 └ gram: Bool false [ Info: For silent loading, specify `verbosity=0`. import EvoTrees ✔ ┌ Warning: This test is skipped as the method is not suitable for Quantile Regression └ @ Main ~/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:80 ┌ Info: Training machine(CVPlusRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - monotone_constraints: Dict{Int64, Int64}() │ - tree_type: binary │ - rng: Random.MersenneTwister(123) └ , …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. ┌ Info: Training machine(CVPlusRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - monotone_constraints: Dict{Int64, Int64}() │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 43086, 42084, 916)) └ , …), …). ┌ Info: Training machine(NaiveRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - monotone_constraints: Dict{Int64, Int64}() │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 84168, 83166, 435)) └ , …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. ┌ Info: Training machine(NaiveRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - monotone_constraints: Dict{Int64, Int64}() │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 91182, 90180, 721)) └ , …), …). ┌ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - monotone_constraints: Dict{Int64, Int64}() │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 98196, 97194, 607)) └ , …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. ┌ Info: Training machine(JackknifePlusAbMinMaxRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - monotone_constraints: Dict{Int64, Int64}() │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 233466, 232464, 341)) └ , …), …). ┌ Info: Training machine(JackknifePlusAbRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - monotone_constraints: Dict{Int64, Int64}() │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 356712, 355710, 101)) └ , …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. ┌ Info: Training machine(JackknifePlusAbRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - monotone_constraints: Dict{Int64, Int64}() │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 490980, 489978, 836)) └ , …), …). ┌ Info: Training machine(JackknifePlusRegressor(model = EvoTrees.EvoTreeRegressor{EvoTrees.MSE} │ - nrounds: 100 │ - L2: 0.0 │ - lambda: 0.0 │ - gamma: 0.0 │ - eta: 0.1 │ - max_depth: 6 │ - min_weight: 1.0 │ - rowsample: 1.0 │ - colsample: 1.0 │ - nbins: 64 │ - alpha: 0.5 │ - monotone_constraints: Dict{Int64, Int64}() │ - tree_type: binary │ - rng: Random.MersenneTwister(123, (0, 614226, 613224, 593)) └ , …), …). ====================================================================================== Information request received. A stacktrace will print followed by a 1.0 second profile ====================================================================================== cmd: /opt/julia/bin/julia 285 running 1 of 1 signal (10): User defined signal 1 _setindex! at ./array.jl:993 [inlined] setindex! at ./array.jl:988 [inlined] update_gains! at /home/pkgeval/.julia/packages/EvoTrees/73o4j/src/fit-utils.jl:335 macro expansion at /home/pkgeval/.julia/packages/EvoTrees/73o4j/src/fit.jl:95 [inlined] #grow_tree!##2 at ./threadingconstructs.jl:276 #grow_tree!##0 at ./threadingconstructs.jl:243 [inlined] #threading_run##0 at ./threadingconstructs.jl:177 unknown function (ip: 0x74fbda978533) at (unknown file) _jl_invoke at /source/src/gf.c:3807 [inlined] ijl_apply_generic at /source/src/gf.c:4004 jl_apply at /source/src/julia.h:2345 [inlined] start_task at /source/src/task.c:1249 unknown function (ip: (nil)) at (unknown file) ============================================================== Profile collected. A report will print at the next yield point ============================================================== ====================================================================================== Information request received. A stacktrace will print followed by a 1.0 second profile ====================================================================================== cmd: /opt/julia/bin/julia 1 running 0 of 1 signal (10): User defined signal 1 epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404 uv_run at /workspace/srcdir/libuv/src/unix/core.c:430 ijl_task_get_next at /source/src/scheduler.c:457 wait at ./task.jl:1192 wait_forever at ./task.jl:1129 jfptr_wait_forever_47924.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:3807 [inlined] ijl_apply_generic at /source/src/gf.c:4004 jl_apply at /source/src/julia.h:2345 [inlined] start_task at /source/src/task.c:1249 unknown function (ip: (nil)) at (unknown file) ============================================================== Profile collected. A report will print at the next yield point ============================================================== ┌ Warning: There were no samples collected in one or more groups. │ This may be due to idle threads, or you may need to run your │ program longer (perhaps by running it multiple times), │ or adjust the delay between samples with `Profile.init()`. └ @ Profile /opt/julia/share/julia/stdlib/v1.13/Profile/src/Profile.jl:1362 Overhead ╎ [+additional indent] Count File:Line Function ========================================================= Thread 1 (default) Task 0x00007279fbe01870 Total snapshots: 452. Utilization: 0% ╎452 @Base/task.jl:1129 wait_forever() 451╎ 452 @Base/task.jl:1192 wait() [1] signal 15: Terminated in expression starting at /PkgEval.jl/scripts/evaluate.jl:210 epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404 uv_run at /workspace/srcdir/libuv/src/unix/core.c:430 ijl_task_get_next at /source/src/scheduler.c:457 wait at ./task.jl:1192 wait_forever at ./task.jl:1129 jfptr_wait_forever_47924.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:3807 [inlined] ijl_apply_generic at /source/src/gf.c:4004 jl_apply at /source/src/julia.h:2345 [inlined] start_task at /source/src/task.c:1249 unknown function (ip: (nil)) at (unknown file) Allocations: 32243916 (Pool: 32243254; Big: 662); GC: 31 [285] signal 15: Terminated in expression starting at /home/pkgeval/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:22 append_module_names at /source/src/module.c:1982 ijl_module_names at /source/src/module.c:2012 #unsorted_names#4 at ./runtime_internals.jl:116 [inlined] unsorted_names at ./runtime_internals.jl:116 [inlined] make_typealias at ./show.jl:634 show_typealias at ./show.jl:815 _show_type at ./show.jl:980 show at ./show.jl:975 [inlined] #sprint#442 at ./strings/io.jl:111 sprint at ./strings/io.jl:106 [inlined] #print_type_bicolor#506 at ./show.jl:2763 [inlined] print_type_bicolor at ./show.jl:2762 unknown function (ip: 0x74fbd67261ad) at (unknown file) _jl_invoke at /source/src/gf.c:3807 [inlined] ijl_apply_generic at /source/src/gf.c:4004 #show_tuple_as_call#500 at ./show.jl:2630 show_tuple_as_call at ./show.jl:2597 [inlined] show_spec_sig at ./stacktraces.jl:322 show_spec_linfo at ./stacktraces.jl:282 #sprint#442 at ./strings/io.jl:113 [inlined] sprint at ./strings/io.jl:106 [inlined] tree_format at /source/usr/share/julia/stdlib/v1.13/Profile/src/Profile.jl:1043 print_tree at /source/usr/share/julia/stdlib/v1.13/Profile/src/Profile.jl:1255 tree at /source/usr/share/julia/stdlib/v1.13/Profile/src/Profile.jl:1309 print_group at /source/usr/share/julia/stdlib/v1.13/Profile/src/Profile.jl:389 #print#5 at /source/usr/share/julia/stdlib/v1.13/Profile/src/Profile.jl:331 print at /source/usr/share/julia/stdlib/v1.13/Profile/src/Profile.jl:271 [inlined] print at /source/usr/share/julia/stdlib/v1.13/Profile/src/Profile.jl:271 [inlined] print at /source/usr/share/julia/stdlib/v1.13/Profile/src/Profile.jl:271 _peek_report at /source/usr/share/julia/stdlib/v1.13/Profile/src/Profile.jl:94 unknown function (ip: 0x74fbd671680f) at (unknown file) _jl_invoke at /source/src/gf.c:3807 [inlined] ijl_apply_generic at /source/src/gf.c:4004 jl_apply at /source/src/julia.h:2345 [inlined] jl_f_invokelatest at /source/src/builtins.c:877 profile_printing_listener at ./Base.jl:337 #start_profile_listener##0 at ./Base.jl:355 jfptr_YY.start_profile_listenerYY.YY.0_16685.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:3807 [inlined] ijl_apply_generic at /source/src/gf.c:4004 jl_apply at /source/src/julia.h:2345 [inlined] start_task at /source/src/task.c:1249 unknown function (ip: (nil)) at (unknown file) Allocations: 540057276 (Pool: 540053141; Big: 4135); GC: 193 PkgEval terminated after 2723.53s: test duration exceeded the time limit