Package evaluation of ConformalPrediction on Julia 1.11.4 (a71dd056e0*) started at 2025-04-08T22:45:53.980 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 8.47s ################################################################################ # Installation # Installing ConformalPrediction... Resolving package versions... Updating `~/.julia/environments/v1.11/Project.toml` [98bfc277] + ConformalPrediction v0.1.13 Updating `~/.julia/environments/v1.11/Manifest.toml` [47edcb42] + ADTypes v1.14.0 [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 v7.18.0 [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 v1.72.3 [d360d2e6] + ChainRulesCore v1.25.1 ⌅ [3da002f7] + ColorTypes v0.11.5 [bbf7d656] + CommonSubexpressions v0.3.1 [34da2185] + Compat v4.16.0 [a33af91c] + CompositionsBase v0.1.2 [ed09eef8] + ComputationalResources v0.3.2 [98bfc277] + ConformalPrediction v0.1.13 [187b0558] + ConstructionBase v1.5.8 [6add18c4] + ContextVariablesX v0.1.3 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [8bb1440f] + DelimitedFiles v1.9.1 [b429d917] + DensityInterface v0.4.0 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.15.1 [a0c0ee7d] + DifferentiationInterface v0.6.50 [31c24e10] + Distributions v0.25.118 [ffbed154] + DocStringExtensions v0.9.4 [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 v11.2.2 [46192b85] + GPUArraysCore v0.2.0 [076d061b] + HashArrayMappedTries v0.2.0 [34004b35] + HypergeometricFunctions v0.3.28 [7869d1d1] + IRTools v0.4.14 [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 v0.5.12 [692b3bcd] + JLLWrappers v1.7.0 [b14d175d] + JuliaVariables v0.2.4 [63c18a36] + KernelAbstractions v0.9.34 [929cbde3] + LLVM v9.2.0 [b964fa9f] + LaTeXStrings v1.4.0 [92ad9a40] + LearnAPI v1.0.1 [d3d80556] + LineSearches v7.3.0 [7a12625a] + LinearMaps v3.11.4 [2ab3a3ac] + LogExpFunctions v0.3.29 [c2834f40] + MLCore v1.0.0 ⌃ [7e8f7934] + MLDataDevices v1.5.3 [a7f614a8] + MLJBase v1.8.1 [50ed68f4] + MLJEnsembles v0.4.3 ⌅ [094fc8d1] + MLJFlux v0.5.1 [6ee0df7b] + MLJLinearModels v0.10.0 [e80e1ace] + MLJModelInterface v1.11.0 [d8e11817] + MLStyle v0.4.17 [f1d291b0] + MLUtils v0.4.8 [1914dd2f] + MacroTools v0.5.15 [dbeba491] + Metalhead v0.9.5 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [d41bc354] + NLSolversBase v7.9.1 [872c559c] + NNlib v0.9.30 [77ba4419] + NaNMath v1.1.3 [71a1bf82] + NameResolution v0.1.5 [0b1bfda6] + OneHotArrays v0.2.7 [429524aa] + Optim v1.12.0 ⌅ [3bd65402] + Optimisers v0.3.4 [bac558e1] + OrderedCollections v1.8.0 [90014a1f] + PDMats v0.11.33 [d96e819e] + Parameters v0.12.3 [570af359] + PartialFunctions v1.2.0 [85a6dd25] + PositiveFactorizations v0.2.4 ⌅ [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.4.3 [8162dcfd] + PrettyPrint v0.2.0 [08abe8d2] + PrettyTables v2.4.0 [33c8b6b6] + ProgressLogging v0.1.4 [92933f4c] + ProgressMeter v1.10.4 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [c1ae055f] + RealDot v0.1.0 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [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 v1.3.0 [efcf1570] + Setfield v1.1.2 [605ecd9f] + ShowCases v0.1.0 [699a6c99] + SimpleTraits v0.9.4 [a2af1166] + SortingAlgorithms v1.2.1 [dc90abb0] + SparseInverseSubset v0.1.2 [276daf66] + SpecialFunctions v2.5.0 [171d559e] + SplittablesBase v0.1.15 [90137ffa] + StaticArrays v1.9.13 [1e83bf80] + StaticArraysCore v1.4.3 [c062fc1d] + StatisticalMeasuresBase v0.1.2 [64bff920] + StatisticalTraits v3.4.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.0 [2913bbd2] + StatsBase v0.34.4 [4c63d2b9] + StatsFuns v1.4.0 [892a3eda] + StringManipulation v0.4.1 [09ab397b] + StructArrays v0.7.1 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.0 [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 v0.6.76 [700de1a5] + ZygoteRules v0.2.7 [dad2f222] + LLVMExtra_jll v0.0.35+0 [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 v1.6.0 [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 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization 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 [bea87d4a] + SuiteSparse_jll v7.7.0+0 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.59.0+0 [3f19e933] + p7zip_jll v17.4.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Installation completed after 5.56s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... ┌ Warning: Could not use exact versions of packages in manifest, re-resolving └ @ TestEnv ~/.julia/packages/TestEnv/tgnBf/src/julia-1.11/activate_set.jl:63 Precompiling package dependencies... Precompilation completed after 1143.57s ################################################################################ # Testing # Testing ConformalPrediction ┌ Warning: Could not use exact versions of packages in manifest, re-resolving └ @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:1920 Status `/tmp/jl_v4fQ15/Project.toml` [4c88cf16] Aqua v0.8.11 [5224ae11] CompatHelperLocal v0.1.27 [98bfc277] ConformalPrediction v0.1.13 [7806a523] DecisionTree v0.12.4 [e30172f5] Documenter v1.10.1 ⌅ [f6006082] EvoTrees v0.16.9 ⌅ [7acf609c] LightGBM v0.7.2 [add582a8] MLJ v0.20.7 [c6f25543] MLJDecisionTreeInterface v0.4.2 ⌅ [094fc8d1] MLJFlux v0.5.1 [6ee0df7b] MLJLinearModels v0.10.0 [e80e1ace] MLJModelInterface v1.11.0 [636a865e] NearestNeighborModels v0.2.3 [91a5bcdd] Plots v1.40.11 [bd7198b4] TaijaPlotting v1.3.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_v4fQ15/Manifest.toml` [47edcb42] ADTypes v1.14.0 [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 v0.8.11 [dce04be8] ArgCheck v2.5.0 [7d9fca2a] Arpack v0.5.4 [4fba245c] ArrayInterface v7.18.0 [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 v1.72.3 [d360d2e6] ChainRulesCore v1.25.1 [944b1d66] CodecZlib v0.7.8 [35d6a980] ColorSchemes v3.29.0 ⌅ [3da002f7] ColorTypes v0.11.5 ⌃ [c3611d14] ColorVectorSpace v0.10.0 [5ae59095] Colors v0.13.0 [861a8166] Combinatorics v1.0.2 [bbf7d656] CommonSubexpressions v0.3.1 [34da2185] Compat v4.16.0 [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 v1.5.8 [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 [e2d170a0] DataValueInterfaces v1.0.0 [7806a523] DecisionTree v0.12.4 [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 v0.6.50 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.118 [ffbed154] DocStringExtensions v0.9.4 [e30172f5] Documenter v1.10.1 [792122b4] EarlyStopping v0.3.0 [4e289a0a] EnumX v1.0.5 ⌅ [f6006082] EvoTrees v0.16.9 [460bff9d] ExceptionUnwrapping v0.1.11 [411431e0] Extents v0.1.5 [c87230d0] FFMPEG v0.4.2 [cc61a311] FLoops v0.2.2 [b9860ae5] FLoopsBase v0.1.1 [33837fe5] FeatureSelection v0.2.2 [5789e2e9] FileIO v1.17.0 [48062228] FilePathsBase v0.9.24 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Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... ┌ 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/tbj1p/src/utilities/utilities.jl:660 [ 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 40.6s WARNING: using deprecated binding ColorTypes.RGB1 in Colors. , use XRGB instead. WARNING: using deprecated binding Colors.RGB1 in PlotUtils. , use ColorTypes.XRGB{T} where T<:Union{AbstractFloat, FixedPointNumbers.FixedPoint{T, f} where f where T<:Integer} instead. WARNING: PlotUtils.RGB1 is deprecated, use ColorTypes.XRGB{T} where T<:Union{AbstractFloat, FixedPointNumbers.FixedPoint{T, f} where f where T<:Integer} instead. likely near /home/pkgeval/.julia/packages/ConformalPrediction/bz1ka/test/aqua.jl:3 WARNING: using deprecated binding ColorTypes.RGB4 in Colors. , use RGBX instead. WARNING: using deprecated binding Colors.RGB4 in PlotUtils. , use ColorTypes.RGBX{T} where T<:Union{AbstractFloat, FixedPointNumbers.FixedPoint{T, f} where f where T<:Integer} instead. WARNING: PlotUtils.RGB4 is deprecated, use ColorTypes.RGBX{T} where T<:Union{AbstractFloat, FixedPointNumbers.FixedPoint{T, f} where f where T<:Integer} instead. likely near /home/pkgeval/.julia/packages/ConformalPrediction/bz1ka/test/aqua.jl:3 [ 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 EvoTrees... 4099.6 ms ✓ NetworkLayout 12707.1 ms ✓ EvoTrees 2 dependencies successfully precompiled in 20 seconds. 85 already precompiled. Precompiling GeometryBasicsExt... 16810.6 ms ✓ Plots → UnitfulExt 17948.6 ms ✓ Plots → GeometryBasicsExt 2 dependencies successfully precompiled in 37 seconds. 193 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, …), …), …). ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ 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, …), …), …). ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ 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, …), …), …). ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ 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, …), …), …). ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ 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, …), …), …). ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ 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, …), …), …). ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ 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, …), …), …). ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}} │ optim_options: Optim.Options{Float64, Nothing} └ lbfgs_options: @NamedTuple{} NamedTuple() ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 [ Info: Training machine(SimpleInductiveClassifier(model = LogisticClassifier(lambda = 2.220446049250313e-16, …), …), …). ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ 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, …), …), …). ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Info: Solver: MLJLinearModels.LBFGS{Optim.Options{Float64, Nothing}, @NamedTuple{}} │ optim_options: Optim.Options{Float64, Nothing} └ lbfgs_options: @NamedTuple{} NamedTuple() ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 ┌ Warning: f_tol is deprecated. Use f_abstol or f_reltol instead. The provided value (0.0001) will be used as f_reltol. └ @ Optim ~/.julia/packages/Optim/8dE7C/src/types.jl:120 [ 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)) └ , …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. ┌ 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, 3973932, 3972930, 532)) └ , …), …). ┌ Info: Training machine(CVMinMaxRegressor(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, 7173318, 7172316, 789)) └ , …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. ┌ Info: Training machine(CVMinMaxRegressor(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, 7216404, 7215402, 705)) └ , …), …). ┌ Info: Training machine(JackknifeRegressor(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, 7257486, 7256484, 224)) └ , …), …). [ Info: Multivariate input for regression with no input variable (`input_var`) specified: defaulting to first variable. ┌ Info: Training machine(JackknifeRegressor(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, 10624206, 10623204, 452)) └ , …), …). ┌ Info: Training machine(JackknifeMinMaxRegressor(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, 13830606, 13829604, 598)) └ , …), …). ====================================================================================== Information request received. A stacktrace will print followed by a 1.0 second profile ====================================================================================== cmd: /opt/julia/bin/julia 306 running 1 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_process_events at /source/src/jl_uv.c:395 process_events at ./libuv.jl:125 [inlined] wait at ./task.jl:1023 #wait#731 at ./condition.jl:130 wait at ./condition.jl:125 [inlined] _wait at ./task.jl:328 threading_run at ./threadingconstructs.jl:168 macro expansion at ./threadingconstructs.jl:190 [inlined] split_set_threads! at /home/pkgeval/.julia/packages/EvoTrees/73o4j/src/fit-utils.jl:178 grow_tree! at /home/pkgeval/.julia/packages/EvoTrees/73o4j/src/fit.jl:114 grow_evotree! at /home/pkgeval/.julia/packages/EvoTrees/73o4j/src/fit.jl:18 grow_evotree! at /home/pkgeval/.julia/packages/EvoTrees/73o4j/src/fit.jl:9 unknown function (ip: 0x7ca70fff722d) _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 fit at /home/pkgeval/.julia/packages/EvoTrees/73o4j/src/MLJ.jl:10 fit at /home/pkgeval/.julia/packages/EvoTrees/73o4j/src/MLJ.jl:3 [inlined] fit at /home/pkgeval/.julia/packages/ConformalPrediction/bz1ka/src/conformal_models/transductive_regression.jl:286 unknown function (ip: 0x7ca70f5a9ab1) _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 jl_apply at /source/src/julia.h:2157 [inlined] do_apply at /source/src/builtins.c:831 #fit_only!#57 at /home/pkgeval/.julia/packages/MLJBase/7nGJF/src/machines.jl:692 fit_only! at /home/pkgeval/.julia/packages/MLJBase/7nGJF/src/machines.jl:617 [inlined] #fit!#63 at /home/pkgeval/.julia/packages/MLJBase/7nGJF/src/machines.jl:789 [inlined] fit! at /home/pkgeval/.julia/packages/MLJBase/7nGJF/src/machines.jl:786 unknown function (ip: 0x7ca70f5a7486) _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 macro expansion at /home/pkgeval/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:43 [inlined] macro expansion at /source/usr/share/julia/stdlib/v1.11/Test/src/Test.jl:1704 [inlined] macro expansion at /home/pkgeval/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:39 [inlined] macro expansion at /source/usr/share/julia/stdlib/v1.11/Test/src/Test.jl:1704 [inlined] macro expansion at /home/pkgeval/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:30 [inlined] macro expansion at /source/usr/share/julia/stdlib/v1.11/Test/src/Test.jl:1704 [inlined] macro expansion at /home/pkgeval/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:26 [inlined] macro expansion at /source/usr/share/julia/stdlib/v1.11/Test/src/Test.jl:1704 [inlined] top-level scope at /home/pkgeval/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:23 _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_invoke at /source/src/gf.c:2955 jl_toplevel_eval_flex at /source/src/toplevel.c:934 jl_toplevel_eval_flex at /source/src/toplevel.c:886 ijl_toplevel_eval_in at /source/src/toplevel.c:994 eval at ./boot.jl:430 [inlined] include_string at ./loading.jl:2734 _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 _include at ./loading.jl:2794 include at ./sysimg.jl:38 unknown function (ip: 0x7ca730f00082) _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 jl_apply at /source/src/julia.h:2157 [inlined] do_call at /source/src/interpreter.c:126 eval_value at /source/src/interpreter.c:223 eval_stmt_value at /source/src/interpreter.c:174 [inlined] eval_body at /source/src/interpreter.c:670 eval_body at /source/src/interpreter.c:539 eval_body at /source/src/interpreter.c:539 jl_interpret_toplevel_thunk at /source/src/interpreter.c:824 jl_toplevel_eval_flex at /source/src/toplevel.c:943 jl_toplevel_eval_flex at /source/src/toplevel.c:886 ijl_toplevel_eval_in at /source/src/toplevel.c:994 eval at ./boot.jl:430 [inlined] include_string at ./loading.jl:2734 _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 _include at ./loading.jl:2794 include at ./sysimg.jl:38 unknown function (ip: 0x7ca730f00082) _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 jl_apply at /source/src/julia.h:2157 [inlined] do_call at /source/src/interpreter.c:126 eval_value at /source/src/interpreter.c:223 eval_stmt_value at /source/src/interpreter.c:174 [inlined] eval_body at /source/src/interpreter.c:670 jl_interpret_toplevel_thunk at /source/src/interpreter.c:824 jl_toplevel_eval_flex at /source/src/toplevel.c:943 jl_toplevel_eval_flex at /source/src/toplevel.c:886 ijl_toplevel_eval_in at /source/src/toplevel.c:994 eval at ./boot.jl:430 [inlined] exec_options at ./client.jl:296 _start at ./client.jl:531 jfptr__start_73523.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 jl_apply at /source/src/julia.h:2157 [inlined] true_main at /source/src/jlapi.c:900 jl_repl_entrypoint at /source/src/jlapi.c:1059 main at /source/cli/loader_exe.c:58 unknown function (ip: 0x7ca7321f9249) __libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) unknown function (ip: 0x4010b8) unknown function (ip: (nil)) ============================================================== 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/v1.11/Profile/src/Profile.jl:1240 Overhead ╎ [+additional indent] Count File:Line; Function ========================================================= Thread 1 Task 0x00007ca724c00010 Total snapshots: 1. Utilization: 100% ╎1 @Base/client.jl:531; _start() ╎ 1 @Base/client.jl:296; exec_options(opts::Base.JLOptions) ╎ 1 @Base/boot.jl:430; eval ╎ 1 @Base/sysimg.jl:38; include(fname::String) ╎ 1 @Base/loading.jl:2794; _include(mapexpr::Function, mod::Module, _path::… ╎ 1 @Base/loading.jl:2734; include_string(mapexpr::typeof(identity), mod::… ╎ ╎ 1 @Base/boot.jl:430; eval ╎ ╎ 1 @Base/sysimg.jl:38; include(fname::String) ╎ ╎ 1 @Base/loading.jl:2794; _include(mapexpr::Function, mod::Module, _pa… ╎ ╎ 1 @Base/loading.jl:2734; include_string(mapexpr::typeof(identity), m… ╎ ╎ 1 @Base/boot.jl:430; eval ╎ ╎ ╎ 1 …a/test/regression.jl:23; top-level scope ╎ ╎ ╎ 1 @Test/src/Test.jl:1704; macro expansion ╎ ╎ ╎ 1 …/test/regression.jl:26; macro expansion ╎ ╎ ╎ 1 @Test/src/Test.jl:1704; macro expansion ╎ ╎ ╎ 1 …test/regression.jl:30; macro expansion ╎ ╎ ╎ ╎ 1 @Test/src/Test.jl:1704; macro expansion ╎ ╎ ╎ ╎ 1 …est/regression.jl:39; macro expansion ╎ ╎ ╎ ╎ 1 @Test/src/Test.jl:1704; macro expansion ╎ ╎ ╎ ╎ 1 …est/regression.jl:43; macro expansion ╎ ╎ ╎ ╎ 1 …/src/machines.jl:786; kwcall(::@NamedTuple{rows::Vecto… ╎ ╎ ╎ ╎ ╎ 1 …src/machines.jl:789; #fit!#63 ╎ ╎ ╎ ╎ ╎ 1 …src/machines.jl:617; fit_only! ╎ ╎ ╎ ╎ ╎ 1 …src/machines.jl:692; fit_only!(mach::Machine{Confor… ╎ ╎ ╎ ╎ ╎ 1 …_regression.jl:286; fit(conf_model::ConformalPredi… ╎ ╎ ╎ ╎ ╎ 1 …rees/src/MLJ.jl:3; fit ╎ ╎ ╎ ╎ ╎ ╎ 1 …ees/src/MLJ.jl:10; fit(model::EvoTrees.EvoTreeRe… ╎ ╎ ╎ ╎ ╎ ╎ 1 …ees/src/fit.jl:9; grow_evotree!(m::EvoTrees.Evo… ╎ ╎ ╎ ╎ ╎ ╎ 1 …es/src/fit.jl:18; grow_evotree!(m::EvoTrees.Ev… ╎ ╎ ╎ ╎ ╎ ╎ 1 …s/src/fit.jl:114; grow_tree!(tree::EvoTrees.T… ╎ ╎ ╎ ╎ ╎ ╎ 1 …fit-utils.jl:198; split_set_threads!(out::Ve… ╎ ╎ ╎ ╎ ╎ ╎ ╎ 1 …nstructs.jl:190; macro expansion ╎ ╎ ╎ ╎ ╎ ╎ ╎ 1 …nstructs.jl:154; threading_run(fun::EvoTre… ╎ ╎ ╎ ╎ ╎ ╎ ╎ 1 …ase/task.jl:5; Task ╎ ╎ ╎ ╎ ╎ ╎ ╎ 1 …ase/task.jl:5; Task ╎ ╎ ╎ ╎ ╎ ╎ ╎ 1 …ase/boot.jl:502; _Task ====================================================================================== 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:522 poptask at ./task.jl:1012 wait at ./task.jl:1021 #wait#731 at ./condition.jl:130 wait at ./condition.jl:125 [inlined] wait at ./process.jl:694 wait at ./process.jl:687 unknown function (ip: 0x73e904970f42) _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 subprocess_handler at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:2142 #131 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:2082 withenv at ./env.jl:265 #118 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:1931 with_temp_env at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:1789 #116 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:1898 #mktempdir#28 at ./file.jl:819 unknown function (ip: 0x73e90495cdfd) _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 mktempdir at ./file.jl:815 mktempdir at ./file.jl:815 [inlined] #sandbox#115 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:1845 [inlined] sandbox at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:1837 unknown function (ip: 0x73e9049513c6) _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 #test#128 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:2063 test at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:2007 [inlined] #test#146 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:481 test at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:460 unknown function (ip: 0x73e9049510cd) _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 #test#77 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:159 unknown function (ip: 0x73e904950abd) _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 test at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:148 #test#75 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:147 [inlined] test at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:147 [inlined] #test#74 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:146 [inlined] test at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:146 unknown function (ip: 0x73e90494cf66) _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 jl_apply at /source/src/julia.h:2157 [inlined] do_call at /source/src/interpreter.c:126 eval_value at /source/src/interpreter.c:223 eval_stmt_value at /source/src/interpreter.c:174 [inlined] eval_body at /source/src/interpreter.c:670 eval_body at /source/src/interpreter.c:539 eval_body at /source/src/interpreter.c:539 jl_interpret_toplevel_thunk at /source/src/interpreter.c:824 jl_toplevel_eval_flex at /source/src/toplevel.c:943 jl_toplevel_eval_flex at /source/src/toplevel.c:886 ijl_toplevel_eval_in at /source/src/toplevel.c:994 eval at ./boot.jl:430 [inlined] include_string at ./loading.jl:2734 _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 _include at ./loading.jl:2794 include at ./Base.jl:557 jfptr_include_46977.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 exec_options at ./client.jl:323 _start at ./client.jl:531 jfptr__start_73523.1 at /opt/julia/lib/julia/sys.so (unknown line) _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 jl_apply at /source/src/julia.h:2157 [inlined] true_main at /source/src/jlapi.c:900 jl_repl_entrypoint at /source/src/jlapi.c:1059 main at /source/cli/loader_exe.c:58 unknown function (ip: 0x73e905c44249) __libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line) unknown function (ip: 0x4010b8) unknown function (ip: (nil)) ============================================================== 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/v1.11/Profile/src/Profile.jl:1240 Overhead ╎ [+additional indent] Count File:Line; Function ========================================================= Thread 1 Task 0x000073e8f8600010 Total snapshots: 22. Utilization: 0% ╎22 @Base/client.jl:531; _start() ╎ 22 @Base/client.jl:323; exec_options(opts::Base.JLOptions) ╎ 22 @Base/Base.jl:557; include(mod::Module, _path::String) ╎ 22 @Base/loading.jl:2794; _include(mapexpr::Function, mod::Module, _path:… ╎ 22 @Base/loading.jl:2734; include_string(mapexpr::typeof(identity), mod:… ╎ 22 @Base/boot.jl:430; eval ╎ ╎ 22 @Pkg/src/API.jl:146; kwcall(::@NamedTuple{julia_args::Cmd}, ::typeo… ╎ ╎ 22 @Pkg/src/API.jl:146; #test#74 ╎ ╎ 22 @Pkg/src/API.jl:147; test ╎ ╎ 22 @Pkg/src/API.jl:147; #test#75 ╎ ╎ 22 @Pkg/src/API.jl:148; kwcall(::@NamedTuple{julia_args::Cmd}, ::t… ╎ ╎ ╎ 22 @Pkg/src/API.jl:159; test(pkgs::Vector{Pkg.Types.PackageSpec};… ╎ ╎ ╎ 22 @Pkg/src/API.jl:460; kwcall(::@NamedTuple{julia_args::Cmd, io… ╎ ╎ ╎ 22 @Pkg/src/API.jl:481; test(ctx::Pkg.Types.Context, pkgs::Vect… ╎ ╎ ╎ 22 …src/Operations.jl:2007; test ╎ ╎ ╎ 22 …src/Operations.jl:2063; test(ctx::Pkg.Types.Context, pkgs… ╎ ╎ ╎ ╎ 22 …rc/Operations.jl:1837; kwcall(::@NamedTuple{preferences:… ╎ ╎ ╎ ╎ 22 …rc/Operations.jl:1845; #sandbox#115 ╎ ╎ ╎ ╎ 22 @Base/file.jl:815; mktempdir ╎ ╎ ╎ ╎ 22 @Base/file.jl:815; mktempdir(fn::Function, parent::Str… ╎ ╎ ╎ ╎ 22 @Base/file.jl:819; mktempdir(fn::Pkg.Operations.var"#… ╎ ╎ ╎ ╎ ╎ 22 …/Operations.jl:1898; (::Pkg.Operations.var"#116#121… ╎ ╎ ╎ ╎ ╎ 22 …/Operations.jl:1789; with_temp_env(fn::Pkg.Operati… ╎ ╎ ╎ ╎ ╎ 22 …Operations.jl:1931; (::Pkg.Operations.var"#118#12… ╎ ╎ ╎ ╎ ╎ 22 @Base/env.jl:265; withenv(::Pkg.Operations.var"#1… ╎ ╎ ╎ ╎ ╎ 22 …Operations.jl:2082; (::Pkg.Operations.var"#131#… ╎ ╎ ╎ ╎ ╎ ╎ 22 …perations.jl:2142; subprocess_handler(cmd::Cmd… ╎ ╎ ╎ ╎ ╎ ╎ 22 …e/process.jl:687; wait(x::Base.Process) ╎ ╎ ╎ ╎ ╎ ╎ 22 …e/process.jl:694; wait(x::Base.Process, sync… ╎ ╎ ╎ ╎ ╎ ╎ 22 …ondition.jl:125; wait ╎ ╎ ╎ ╎ ╎ ╎ 22 …ondition.jl:130; wait(c::Base.GenericCondi… ╎ ╎ ╎ ╎ ╎ ╎ ╎ 22 …ase/task.jl:1021; wait() 21╎ ╎ ╎ ╎ ╎ ╎ ╎ 22 …ase/task.jl:1012; poptask(W::Base.Intrus… [306] signal 15: Terminated in expression starting at /home/pkgeval/.julia/packages/ConformalPrediction/bz1ka/test/regression.jl:22 setindex! at ./array.jl:994 [inlined] setindex! at ./multidimensional.jl:704 [inlined] macro expansion at ./broadcast.jl:973 [inlined] macro expansion at ./simdloop.jl:77 [inlined] copyto! at ./broadcast.jl:972 [inlined] copyto! at ./broadcast.jl:925 [inlined] materialize! at ./broadcast.jl:883 [inlined] materialize! at ./broadcast.jl:880 [inlined] update_gains! at /home/pkgeval/.julia/packages/EvoTrees/73o4j/src/fit-utils.jl:319 macro expansion at /home/pkgeval/.julia/packages/EvoTrees/73o4j/src/fit.jl:95 [inlined] #881#threadsfor_fun#157 at ./threadingconstructs.jl:253 #881#threadsfor_fun at ./threadingconstructs.jl:220 [inlined] #1 at ./threadingconstructs.jl:154 unknown function (ip: 0x7ca70f50a75f) _jl_invoke at /source/src/gf.c:2948 [inlined] ijl_apply_generic at /source/src/gf.c:3125 jl_apply at /source/src/julia.h:2157 [inlined] start_task at /source/src/task.c:1202 unknown function (ip: (nil)) Allocations: 668928544 (Pool: 668921012; Big: 7532); GC: 290 PkgEval terminated after 2724.44s: test duration exceeded the time limit