Package evaluation to test MLJTuning on Julia 1.14.0-DEV.30 (073666df8b*) started at 2025-11-04T22:27:35.795 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 9.53s ################################################################################ # Installation # Installing MLJTuning... Resolving package versions... Updating `~/.julia/environments/v1.14/Project.toml` [03970b2e] + MLJTuning v0.8.8 Updating `~/.julia/environments/v1.14/Manifest.toml` [7d9f7c33] + Accessors v0.1.42 [79e6a3ab] + Adapt v4.4.0 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [a9b6321e] + Atomix v1.1.2 [198e06fe] + BangBang v0.4.6 [9718e550] + Baselet v0.1.1 [324d7699] + CategoricalArrays v1.0.2 [af321ab8] + CategoricalDistributions v0.2.0 [d360d2e6] + ChainRulesCore v1.26.0 [3da002f7] + ColorTypes v0.12.1 [34da2185] + Compat v4.18.1 [a33af91c] + CompositionsBase v0.1.2 [ed09eef8] + ComputationalResources v0.3.2 [187b0558] + ConstructionBase v1.6.0 [6add18c4] + ContextVariablesX v0.1.3 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.19.2 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [8bb1440f] + DelimitedFiles v1.9.1 [31c24e10] + Distributions v0.25.122 [ffbed154] + DocStringExtensions v0.9.5 [cc61a311] + FLoops v0.2.2 [b9860ae5] + FLoopsBase v0.1.1 [1a297f60] + FillArrays v1.14.0 [53c48c17] + FixedPointNumbers v0.8.5 [46192b85] + GPUArraysCore v0.2.0 [076d061b] + HashArrayMappedTries v0.2.0 [34004b35] + HypergeometricFunctions v0.3.28 [22cec73e] + InitialValues v0.3.1 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.6 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.7.1 [b14d175d] + JuliaVariables v0.2.4 [63c18a36] + KernelAbstractions v0.9.39 [b964fa9f] + LaTeXStrings v1.4.0 [a5e1c1ea] + LatinHypercubeSampling v1.9.0 [92ad9a40] + LearnAPI v2.0.1 [2ab3a3ac] + LogExpFunctions v0.3.29 [c2834f40] + MLCore v1.0.0 [a7f614a8] + MLJBase v1.10.0 [e80e1ace] + MLJModelInterface v1.12.0 [03970b2e] + MLJTuning v0.8.8 [d8e11817] + MLStyle v0.4.17 [f1d291b0] + MLUtils v0.4.8 [1914dd2f] + MacroTools v0.5.16 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [872c559c] + NNlib v0.9.31 [71a1bf82] + NameResolution v0.1.5 [bac558e1] + OrderedCollections v1.8.1 [90014a1f] + PDMats v0.11.36 [d96e819e] + Parameters v0.12.3 [aea7be01] + PrecompileTools v1.3.3 [21216c6a] + Preferences v1.5.0 [8162dcfd] + PrettyPrint v0.2.0 ⌅ [08abe8d2] + PrettyTables v2.4.0 [92933f4c] + ProgressMeter v1.11.0 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [79098fc4] + Rmath v0.9.0 [321657f4] + ScientificTypes v3.1.1 [30f210dd] + ScientificTypesBase v3.0.0 [7e506255] + ScopedValues v1.5.0 [efcf1570] + Setfield v1.1.2 [605ecd9f] + ShowCases v0.1.0 [699a6c99] + SimpleTraits v0.9.5 [a2af1166] + SortingAlgorithms v1.2.2 [276daf66] + SpecialFunctions v2.6.1 [171d559e] + SplittablesBase v0.1.15 [860ef19b] + StableRNGs v1.0.3 [90137ffa] + StaticArrays v1.9.15 [1e83bf80] + StaticArraysCore v1.4.4 [c062fc1d] + StatisticalMeasuresBase v0.1.3 [64bff920] + StatisticalTraits v3.5.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.1 [2913bbd2] + StatsBase v0.34.7 [4c63d2b9] + StatsFuns v1.5.2 [892a3eda] + StringManipulation v0.4.1 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [28d57a85] + Transducers v0.4.85 [3a884ed6] + UnPack v1.0.2 [013be700] + UnsafeAtomics v0.3.0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [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 [de0858da] + Printf v1.11.0 [3fa0cd96] + REPL v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v1.0.0 [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 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.7+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [8e850b90] + libblastrampoline_jll v5.15.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Installation completed after 4.94s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... ┌ Error: Failed to use TestEnv.jl; test dependencies will not be precompiled │ exception = │ UndefVarError: `project_rel_path` not defined in `TestEnv` │ Suggestion: this global was defined as `Pkg.Operations.project_rel_path` but not assigned a value. │ Stacktrace: │ [1] get_test_dir(ctx::Pkg.Types.Context, pkgspec::PackageSpec) │ @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/common.jl:75 │ [2] test_dir_has_project_file │ @ ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/common.jl:52 [inlined] │ [3] maybe_gen_project_override! │ @ ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/common.jl:83 [inlined] │ [4] activate(pkg::String; allow_reresolve::Bool) │ @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/activate_set.jl:12 │ [5] activate(pkg::String) │ @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/activate_set.jl:9 │ [6] top-level scope │ @ /PkgEval.jl/scripts/precompile.jl:24 │ [7] include(mod::Module, _path::String) │ @ Base ./Base.jl:309 │ [8] exec_options(opts::Base.JLOptions) │ @ Base ./client.jl:344 │ [9] _start() │ @ Base ./client.jl:577 └ @ Main /PkgEval.jl/scripts/precompile.jl:26 Precompiling package dependencies... Precompiling packages... 3780.5 ms ✓ LatinHypercubeSampling 3621.8 ms ✓ NNlib → NNlibSpecialFunctionsExt 19140.9 ms ✓ MLUtils 7717.1 ms ✓ ScientificTypes 14720.5 ms ✓ StatisticalMeasuresBase 10558.0 ms ✓ CategoricalDistributions 17426.0 ms ✓ MLJBase 12415.5 ms ✓ MLJTuning 8 dependencies successfully precompiled in 131 seconds. 143 already precompiled. Precompilation completed after 94.77s ################################################################################ # Testing # Testing MLJTuning Status `/tmp/jl_raBtFZ/Project.toml` [324d7699] CategoricalArrays v1.0.2 [ed09eef8] ComputationalResources v0.3.2 [7806a523] DecisionTree v0.12.4 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.122 [a5e1c1ea] LatinHypercubeSampling v1.9.0 [a7f614a8] MLJBase v1.10.0 [e80e1ace] MLJModelInterface v1.12.0 [03970b2e] MLJTuning v0.8.8 [6f286f6a] MultivariateStats v0.10.3 [b8a86587] NearestNeighbors v0.4.22 [92933f4c] ProgressMeter v1.11.0 [3cdcf5f2] RecipesBase v1.3.4 [321657f4] ScientificTypes v3.1.1 [860ef19b] StableRNGs v1.0.3 [a19d573c] StatisticalMeasures v0.3.3 [c062fc1d] StatisticalMeasuresBase v0.1.3 [10745b16] Statistics v1.11.1 [2913bbd2] StatsBase v0.34.7 [bd369af6] Tables v1.12.1 [8ba89e20] Distributed v1.11.0 [37e2e46d] LinearAlgebra v1.13.0 [9a3f8284] Random v1.11.0 [9e88b42a] Serialization v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_raBtFZ/Manifest.toml` [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.42 [79e6a3ab] Adapt v4.4.0 [66dad0bd] AliasTables v1.1.3 [dce04be8] ArgCheck v2.5.0 [7d9fca2a] Arpack v0.5.4 [a9b6321e] Atomix v1.1.2 [198e06fe] BangBang v0.4.6 [9718e550] Baselet v0.1.1 [324d7699] CategoricalArrays v1.0.2 [af321ab8] CategoricalDistributions v0.2.0 [d360d2e6] ChainRulesCore v1.26.0 [3da002f7] ColorTypes v0.12.1 [34da2185] Compat v4.18.1 [a33af91c] CompositionsBase v0.1.2 [ed09eef8] ComputationalResources v0.3.2 [187b0558] ConstructionBase v1.6.0 [6add18c4] ContextVariablesX v0.1.3 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [864edb3b] DataStructures v0.19.2 [e2d170a0] DataValueInterfaces v1.0.0 [7806a523] DecisionTree v0.12.4 [244e2a9f] DefineSingletons v0.1.2 [8bb1440f] DelimitedFiles v1.9.1 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.122 [ffbed154] DocStringExtensions v0.9.5 [cc61a311] FLoops v0.2.2 [b9860ae5] FLoopsBase v0.1.1 [1a297f60] FillArrays v1.14.0 [53c48c17] FixedPointNumbers v0.8.5 [46192b85] GPUArraysCore v0.2.0 [076d061b] HashArrayMappedTries v0.2.0 [34004b35] HypergeometricFunctions v0.3.28 [22cec73e] InitialValues v0.3.1 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.6 [82899510] IteratorInterfaceExtensions v1.0.0 [692b3bcd] JLLWrappers v1.7.1 [b14d175d] JuliaVariables v0.2.4 [63c18a36] KernelAbstractions v0.9.39 [b964fa9f] LaTeXStrings v1.4.0 [a5e1c1ea] LatinHypercubeSampling v1.9.0 [92ad9a40] LearnAPI v2.0.1 [2ab3a3ac] LogExpFunctions v0.3.29 [c2834f40] MLCore v1.0.0 [a7f614a8] MLJBase v1.10.0 [e80e1ace] MLJModelInterface v1.12.0 [03970b2e] MLJTuning v0.8.8 [d8e11817] MLStyle v0.4.17 [f1d291b0] MLUtils v0.4.8 [1914dd2f] MacroTools v0.5.16 [128add7d] MicroCollections v0.2.0 [e1d29d7a] Missings v1.2.0 [6f286f6a] MultivariateStats v0.10.3 [872c559c] NNlib v0.9.31 [71a1bf82] NameResolution v0.1.5 [b8a86587] NearestNeighbors v0.4.22 [bac558e1] OrderedCollections v1.8.1 [90014a1f] PDMats v0.11.36 [d96e819e] Parameters v0.12.3 [aea7be01] PrecompileTools v1.3.3 [21216c6a] Preferences v1.5.0 [8162dcfd] PrettyPrint v0.2.0 ⌅ [08abe8d2] PrettyTables v2.4.0 [92933f4c] ProgressMeter v1.11.0 [43287f4e] PtrArrays v1.3.0 [1fd47b50] QuadGK v2.11.2 [3cdcf5f2] RecipesBase v1.3.4 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 [79098fc4] Rmath v0.9.0 [321657f4] ScientificTypes v3.1.1 [30f210dd] ScientificTypesBase v3.0.0 [6e75b9c4] ScikitLearnBase v0.5.0 [7e506255] ScopedValues v1.5.0 [efcf1570] Setfield v1.1.2 [605ecd9f] ShowCases v0.1.0 [699a6c99] SimpleTraits v0.9.5 [a2af1166] SortingAlgorithms v1.2.2 [276daf66] SpecialFunctions v2.6.1 [171d559e] SplittablesBase v0.1.15 [860ef19b] StableRNGs v1.0.3 [90137ffa] StaticArrays v1.9.15 [1e83bf80] StaticArraysCore v1.4.4 [a19d573c] StatisticalMeasures v0.3.3 [c062fc1d] StatisticalMeasuresBase v0.1.3 [64bff920] StatisticalTraits v3.5.0 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.7.1 [2913bbd2] StatsBase v0.34.7 [4c63d2b9] StatsFuns v1.5.2 [892a3eda] StringManipulation v0.4.1 [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.1 [28d57a85] Transducers v0.4.85 [3a884ed6] UnPack v1.0.2 [013be700] UnsafeAtomics v0.3.0 ⌅ [68821587] Arpack_jll v3.5.1+1 [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 [b27032c2] LibCURL v1.0.0 [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 v1.3.0 [44cfe95a] Pkg v1.13.0 [de0858da] Printf v1.11.0 [3fa0cd96] REPL v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v1.0.0 [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 v8.16.0+0 [e37daf67] LibGit2_jll v1.9.1+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2025.9.9 [4536629a] OpenBLAS_jll v0.3.29+0 [05823500] OpenLibm_jll v0.8.7+0 [458c3c95] OpenSSL_jll v3.5.4+0 [efcefdf7] PCRE2_jll v10.47.0+0 [bea87d4a] SuiteSparse_jll v7.10.1+0 [83775a58] Zlib_jll v1.3.1+2 [3161d3a3] Zstd_jll v1.5.7+1 [8e850b90] libblastrampoline_jll v5.15.0+0 [8e850ede] nghttp2_jll v1.67.1+0 [3f19e933] p7zip_jll v17.6.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... [ Info: nworkers: 2 [ Info: nthreads: 1 Loading some models for testing... Test Summary: | Pass Total Time utilities | 4 4 3.9s Test Summary: | Pass Total Time selection heuristics | 7 7 3.0s Testing progressmeter basic fit with CPU1{Nothing}(nothing) and CPU1 resampling [ Info: Attempting to evaluate 12 models. measurement: 1.903643502106285 measurement: 1.8313682528233075 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:20measurement: 1.725824054837585 measurement: 1.5876920544899495 measurement: 1.461278306396784 measurement: 1.3224538242874866 measurement: 1.2736828159099107 measurement: 1.1333245517941333 measurement: 1.050032852142519 measurement: 0.9515984846885978 measurement: 0.9657853181057472 measurement: 0.979226007963803 Evaluating over 12 metamodels: 100%[=========================] Time: 0:00:04 [ Info: Training machine(KNNRegressor(K = 4, …), …). Testing progressmeter basic fit with CPUProcesses{Nothing}(nothing) and CPU1 resampling [ Info: Attempting to evaluate 12 models. Evaluating over 12 metamodels: 17%[====> ] ETA: 0:06:59 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:04:13 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:02:49 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:01:58 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:01:25 Evaluating over 12 metamodels: 58%[==============> ] ETA: 0:01:01 Evaluating over 12 metamodels: 67%[================> ] ETA: 0:00:43 Evaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:28 Evaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:17 Evaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:08 Evaluating over 12 metamodels: 100%[=========================] Time: 0:01:25 Testing progressmeter basic fit with CPUThreads{Nothing}(nothing) and CPU1 resampling [ Info: Attempting to evaluate 12 models. Evaluating over 12 metamodels: 0%[> ] ETA: N/A Evaluating over 12 metamodels: 8%[==> ] ETA: 0:00:00 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:00 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:00 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:00 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:00 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:00 Evaluating over 12 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 100%[=========================] Time: 0:00:00 Testing progressmeter basic fit with CPU1{Nothing}(nothing) and CPUThreads resampling [ Info: Attempting to evaluate 12 models. Evaluating over 12 metamodels: 0%[> ] ETA: N/A Evaluating over 12 metamodels: 8%[==> ] ETA: 0:00:00 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:00 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:00 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:00 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:00 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:00 Evaluating over 12 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 100%[=========================] Time: 0:00:00 Testing progressmeter basic fit with CPUProcesses{Nothing}(nothing) and CPUThreads resampling ┌ Info: The combination acceleration=CPUThreads{Nothing}(nothing) and acceleration_resampling=CPUProcesses{Nothing}(nothing) isn't supported. └ Resetting to `acceleration = CPUProcesses()` and `acceleration_resampling = CPUThreads()`. [ Info: Attempting to evaluate 12 models. Evaluating over 12 metamodels: 0%[> ] ETA: N/A Evaluating over 12 metamodels: 8%[==> ] ETA: 0:00:25 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:11 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:08 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:04 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:00:03 Evaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:02 Evaluating over 12 metamodels: 67%[================> ] ETA: 0:00:01 Evaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:01 Evaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:01 Evaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 100%[=========================] Time: 0:00:02 Testing progressmeter basic fit with CPUThreads{Nothing}(nothing) and CPUThreads resampling [ Info: Attempting to evaluate 12 models. Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:04 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:02 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:01 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:01 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:00:01 Evaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:01 Evaluating over 12 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 100%[=========================] Time: 0:00:00 Testing progressmeter basic fit with CPU1{Nothing}(nothing) and CPUProcesses resampling [ Info: Attempting to evaluate 12 models. Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:00 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:00 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:00 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:00 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:00 Evaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 100%[=========================] Time: 0:00:00 Testing progressmeter basic fit with CPUProcesses{Nothing}(nothing) and CPUProcesses resampling [ Info: The combination acceleration=CPUProcesses{Nothing}(nothing) and acceleration_resampling=CPUProcesses{Nothing}(nothing) is not generally optimal. You may want to consider setting `acceleration = CPUProcesses()` and `acceleration_resampling = CPUThreads()`. [ Info: Attempting to evaluate 12 models. Evaluating over 12 metamodels: 0%[> ] ETA: N/A Evaluating over 12 metamodels: 8%[==> ] ETA: 0:01:24 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:38 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:24 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:16 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:11 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:00:08 Evaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:06 Evaluating over 12 metamodels: 67%[================> ] ETA: 0:00:04 Evaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:03 Evaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:02 Evaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:01 Evaluating over 12 metamodels: 100%[=========================] Time: 0:00:07 Testing progressmeter basic fit with CPUThreads{Nothing}(nothing) and CPUProcesses resampling [ Info: Attempting to evaluate 12 models. Evaluating over 12 metamodels: 0%[> ] ETA: N/A Evaluating over 12 metamodels: 8%[==> ] ETA: 0:00:00 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:00 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:00 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:00 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 100%[=========================] Time: 0:00:00 Evaluating over 2 metamodels: 0%[> ] ETA: N/A Evaluating over 2 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 2 metamodels: 100%[=========================] Time: 0:00:00 Evaluating over 2 metamodels: 0%[> ] ETA: N/A Evaluating over 2 metamodels: 50%[============> ] ETA: 0:00:01 Evaluating over 2 metamodels: 100%[=========================] Time: 0:00:01 Evaluating over 2 metamodels: 0%[> ] ETA: N/A Evaluating over 2 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 2 metamodels: 100%[=========================] Time: 0:00:00 ┌ Error: Problem fitting the machine machine(Resampler(model = DeterministicConstantRegressor(), …), …). └ @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:695 [ Info: Running type checks... [ Info: Type checks okay. ┌ Error: Problem fitting the machine machine(DeterministicTunedModel(model = DeterministicConstantRegressor(), …), …). └ @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:695 [ Info: Running type checks... [ Info: Type checks okay. Resampling reproducibility: Error During Test at /home/pkgeval/.julia/packages/MLJTuning/vMe8s/test/test_utilities.jl:19 Got exception outside of a @test UndefVarError: `ltm52` not defined in `Random` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty @ ./Base_compiler.jl:50 [inlined] [2] _shuffle!(r::StableRNGs.LehmerRNG, a::Vector{Int64}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:154 [3] shuffle!(r::StableRNGs.LehmerRNG, a::Vector{Int64}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:144 [4] partition(X::UnitRange{Int64}, fractions::Float64; shuffle::Bool, rng::StableRNGs.LehmerRNG, stratify::Nothing, multi::Bool) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/data/data.jl:181 [5] kwcall(::@NamedTuple{shuffle::Bool, rng::StableRNGs.LehmerRNG}, ::typeof(partition), X::UnitRange{Int64}, fractions::Float64) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/data/data.jl:149 [6] train_test_pairs(holdout::Holdout, rows::UnitRange{Int64}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:239 [7] train_test_pairs @ ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:129 [inlined] [8] train_test_pairs @ ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:127 [inlined] [9] #evaluate!##22 @ ./none:-1 [inlined] [10] iterate @ ./generator.jl:48 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, MLJBase.var"#evaluate!##22#evaluate!##23"{Holdout, UnitRange{Int64}, Tuple{Tables.MatrixTable{Matrix{Float64}}, Vector{Float64}}}}) @ Base ./array.jl:828 [12] evaluate!(::Machine{DeterministicConstantRegressor, DeterministicConstantRegressor, true}, ::Holdout, ::Nothing, ::Nothing, ::Nothing, ::Int64, ::Int64, ::Vector{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.FussyMeasure{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.Multimeasure{StatisticalMeasuresBase.SupportsMissingsMeasure{StatisticalMeasures.LPLossOnScalars{Int64}}, Nothing, StatisticalMeasuresBase.Mean, typeof(identity)}}, Nothing}}}, ::Vector{typeof(predict)}, ::CPU1{Nothing}, ::Bool, ::Bool, ::Nothing, ::Holdout, ::Bool) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:1607 [13] fit(::Resampler{Holdout, Nothing}, ::Int64, ::Tables.MatrixTable{Matrix{Float64}}, ::Vector{Float64}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:1773 [14] fit_only!(mach::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}; rows::Nothing, verbosity::Int64, force::Bool, composite::Nothing) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:693 [15] fit_only! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:617 [inlined] [16] #fit!#66 @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:790 [inlined] [17] fit! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:787 [inlined] [18] event!(metamodel::DeterministicConstantRegressor, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, verbosity::Int64, tuning::Explicit, history::Nothing, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:469 [19] (::MLJTuning.var"#assemble_events!##0#assemble_events!##1"{Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, Int64, Explicit, Nothing, MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, ProgressMeter.Progress})(m::DeterministicConstantRegressor) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:508 [20] iterate @ ./generator.jl:48 [inlined] [21] _collect(c::Vector{Any}, itr::Base.Generator{Vector{Any}, MLJTuning.var"#assemble_events!##0#assemble_events!##1"{Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, Int64, Explicit, Nothing, MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, ProgressMeter.Progress}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base ./array.jl:848 [22] collect_similar @ ./array.jl:763 [inlined] [23] map @ ./abstractarray.jl:3390 [inlined] [24] assemble_events!(metamodels::Vector{Any}, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, verbosity::Int64, tuning::Explicit, history::Nothing, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, acceleration::CPU1{Nothing}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:507 [25] build!(history::Nothing, n::Int64, tuning::Explicit, model::DeterministicConstantRegressor, model_buffer::Channel{Any}, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Tuple{DeterministicConstantRegressor, Int64}}, verbosity::Int64, acceleration::CPU1{Nothing}, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:702 [26] fit(::MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, ::Int64, ::Tables.MatrixTable{Matrix{Float64}}, ::Vector{Float64}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:786 [27] fit_only!(mach::Machine{MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, false}; rows::Nothing, verbosity::Int64, force::Bool, composite::Nothing) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:693 [28] fit_only! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:617 [inlined] [29] #fit!#66 @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:790 [inlined] [30] kwcall(::@NamedTuple{verbosity::Int64}, ::typeof(fit!), mach::Machine{MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, false}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:787 [31] top-level scope @ ~/.julia/packages/MLJTuning/vMe8s/test/test_utilities.jl:19 [32] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/tuned_models.jl:344 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [34] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/tuned_models.jl:355 [inlined] [35] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [36] eval_test_function(func::Any, args::Any, kwargs::Any, quoted_func::Union{Expr, Symbol}, source::LineNumberNode, negate::Bool) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:394 [37] top-level scope @ ~/.julia/packages/MLJTuning/vMe8s/test/runtests.jl:32 [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [39] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/runtests.jl:32 [inlined] [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:750 [inlined] [41] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [42] top-level scope @ none:6 [43] eval(m::Module, e::Any) @ Core ./boot.jl:489 [44] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [45] _start() @ Base ./client.jl:577 ┌ Error: Problem fitting the machine machine(Resampler(model = DeterministicConstantRegressor(), …), …). └ @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:695 [ Info: Running type checks... [ Info: Type checks okay. ┌ Error: Problem fitting the machine machine(DeterministicTunedModel(model = DeterministicConstantRegressor(), …), …). └ @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:695 [ Info: Running type checks... [ Info: Type checks okay. Resampling reproducibility (accelerated with typename(CPUProcesses)): Error During Test at /home/pkgeval/.julia/packages/MLJTuning/vMe8s/test/test_utilities.jl:36 Got exception outside of a @test UndefVarError: `ltm52` not defined in `Random` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty @ ./Base_compiler.jl:50 [inlined] [2] _shuffle!(r::StableRNGs.LehmerRNG, a::Vector{Int64}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:154 [3] shuffle!(r::StableRNGs.LehmerRNG, a::Vector{Int64}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:144 [4] partition(X::UnitRange{Int64}, fractions::Float64; shuffle::Bool, rng::StableRNGs.LehmerRNG, stratify::Nothing, multi::Bool) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/data/data.jl:181 [5] kwcall(::@NamedTuple{shuffle::Bool, rng::StableRNGs.LehmerRNG}, ::typeof(partition), X::UnitRange{Int64}, fractions::Float64) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/data/data.jl:149 [6] train_test_pairs(holdout::Holdout, rows::UnitRange{Int64}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:239 [7] train_test_pairs @ ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:129 [inlined] [8] train_test_pairs @ ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:127 [inlined] [9] #evaluate!##22 @ ./none:-1 [inlined] [10] iterate @ ./generator.jl:48 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, MLJBase.var"#evaluate!##22#evaluate!##23"{Holdout, UnitRange{Int64}, Tuple{Tables.MatrixTable{Matrix{Float64}}, Vector{Float64}}}}) @ Base ./array.jl:828 [12] evaluate!(::Machine{DeterministicConstantRegressor, DeterministicConstantRegressor, true}, ::Holdout, ::Nothing, ::Nothing, ::Nothing, ::Int64, ::Int64, ::Vector{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.FussyMeasure{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.Multimeasure{StatisticalMeasuresBase.SupportsMissingsMeasure{StatisticalMeasures.LPLossOnScalars{Int64}}, Nothing, StatisticalMeasuresBase.Mean, typeof(identity)}}, Nothing}}}, ::Vector{typeof(predict)}, ::CPUProcesses{Nothing}, ::Bool, ::Bool, ::Nothing, ::Holdout, ::Bool) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:1607 [13] fit(::Resampler{Holdout, Nothing}, ::Int64, ::Tables.MatrixTable{Matrix{Float64}}, ::Vector{Float64}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:1773 [14] fit_only!(mach::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}; rows::Nothing, verbosity::Int64, force::Bool, composite::Nothing) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:693 [15] fit_only! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:617 [inlined] [16] #fit!#66 @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:790 [inlined] [17] fit! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:787 [inlined] [18] event!(metamodel::DeterministicConstantRegressor, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, verbosity::Int64, tuning::Explicit, history::Nothing, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:469 [19] (::MLJTuning.var"#assemble_events!##0#assemble_events!##1"{Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, Int64, Explicit, Nothing, MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, ProgressMeter.Progress})(m::DeterministicConstantRegressor) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:508 [20] iterate @ ./generator.jl:48 [inlined] [21] _collect(c::Vector{Any}, itr::Base.Generator{Vector{Any}, MLJTuning.var"#assemble_events!##0#assemble_events!##1"{Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, Int64, Explicit, Nothing, MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, ProgressMeter.Progress}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base ./array.jl:848 [22] collect_similar @ ./array.jl:763 [inlined] [23] map @ ./abstractarray.jl:3390 [inlined] [24] assemble_events!(metamodels::Vector{Any}, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, verbosity::Int64, tuning::Explicit, history::Nothing, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, acceleration::CPU1{Nothing}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:507 [25] build!(history::Nothing, n::Int64, tuning::Explicit, model::DeterministicConstantRegressor, model_buffer::Channel{Any}, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Tuple{DeterministicConstantRegressor, Int64}}, verbosity::Int64, acceleration::CPU1{Nothing}, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:702 [26] fit(::MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, ::Int64, ::Tables.MatrixTable{Matrix{Float64}}, ::Vector{Float64}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:786 [27] fit_only!(mach::Machine{MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, false}; rows::Nothing, verbosity::Int64, force::Bool, composite::Nothing) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:693 [28] fit_only! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:617 [inlined] [29] #fit!#66 @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:790 [inlined] [30] kwcall(::@NamedTuple{verbosity::Int64}, ::typeof(fit!), mach::Machine{MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, false}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:787 [31] top-level scope @ ~/.julia/packages/MLJTuning/vMe8s/test/test_utilities.jl:36 [32] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/tuned_models.jl:344 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [34] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/tuned_models.jl:355 [inlined] [35] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [36] eval_test_function(func::Any, args::Any, kwargs::Any, quoted_func::Union{Expr, Symbol}, source::LineNumberNode, negate::Bool) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:394 [37] top-level scope @ ~/.julia/packages/MLJTuning/vMe8s/test/runtests.jl:32 [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [39] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/runtests.jl:32 [inlined] [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:750 [inlined] [41] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [42] top-level scope @ none:6 [43] eval(m::Module, e::Any) @ Core ./boot.jl:489 [44] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [45] _start() @ Base ./client.jl:577 ┌ Error: Problem fitting the machine machine(Resampler(model = DeterministicConstantRegressor(), …), …). └ @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:695 [ Info: Running type checks... [ Info: Type checks okay. ┌ Error: Problem fitting the machine machine(DeterministicTunedModel(model = DeterministicConstantRegressor(), …), …). └ @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:695 [ Info: Running type checks... [ Info: Type checks okay. Resampling reproducibility (accelerated with typename(CPUThreads)): Error During Test at /home/pkgeval/.julia/packages/MLJTuning/vMe8s/test/test_utilities.jl:36 Got exception outside of a @test UndefVarError: `ltm52` not defined in `Random` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty @ ./Base_compiler.jl:50 [inlined] [2] _shuffle!(r::StableRNGs.LehmerRNG, a::Vector{Int64}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:154 [3] shuffle!(r::StableRNGs.LehmerRNG, a::Vector{Int64}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:144 [4] partition(X::UnitRange{Int64}, fractions::Float64; shuffle::Bool, rng::StableRNGs.LehmerRNG, stratify::Nothing, multi::Bool) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/data/data.jl:181 [5] kwcall(::@NamedTuple{shuffle::Bool, rng::StableRNGs.LehmerRNG}, ::typeof(partition), X::UnitRange{Int64}, fractions::Float64) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/data/data.jl:149 [6] train_test_pairs(holdout::Holdout, rows::UnitRange{Int64}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:239 [7] train_test_pairs @ ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:129 [inlined] [8] train_test_pairs @ ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:127 [inlined] [9] #evaluate!##22 @ ./none:-1 [inlined] [10] iterate @ ./generator.jl:48 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, MLJBase.var"#evaluate!##22#evaluate!##23"{Holdout, UnitRange{Int64}, Tuple{Tables.MatrixTable{Matrix{Float64}}, Vector{Float64}}}}) @ Base ./array.jl:828 [12] evaluate!(::Machine{DeterministicConstantRegressor, DeterministicConstantRegressor, true}, ::Holdout, ::Nothing, ::Nothing, ::Nothing, ::Int64, ::Int64, ::Vector{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.FussyMeasure{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.Multimeasure{StatisticalMeasuresBase.SupportsMissingsMeasure{StatisticalMeasures.LPLossOnScalars{Int64}}, Nothing, StatisticalMeasuresBase.Mean, typeof(identity)}}, Nothing}}}, ::Vector{typeof(predict)}, ::CPUThreads{Int64}, ::Bool, ::Bool, ::Nothing, ::Holdout, ::Bool) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:1607 [13] fit(::Resampler{Holdout, Nothing}, ::Int64, ::Tables.MatrixTable{Matrix{Float64}}, ::Vector{Float64}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:1773 [14] fit_only!(mach::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}; rows::Nothing, verbosity::Int64, force::Bool, composite::Nothing) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:693 [15] fit_only! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:617 [inlined] [16] #fit!#66 @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:790 [inlined] [17] fit! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:787 [inlined] [18] event!(metamodel::DeterministicConstantRegressor, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, verbosity::Int64, tuning::Explicit, history::Nothing, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:469 [19] (::MLJTuning.var"#assemble_events!##0#assemble_events!##1"{Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, Int64, Explicit, Nothing, MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, ProgressMeter.Progress})(m::DeterministicConstantRegressor) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:508 [20] iterate @ ./generator.jl:48 [inlined] [21] _collect(c::Vector{Any}, itr::Base.Generator{Vector{Any}, MLJTuning.var"#assemble_events!##0#assemble_events!##1"{Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, Int64, Explicit, Nothing, MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, ProgressMeter.Progress}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base ./array.jl:848 [22] collect_similar @ ./array.jl:763 [inlined] [23] map @ ./abstractarray.jl:3390 [inlined] [24] assemble_events!(metamodels::Vector{Any}, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, verbosity::Int64, tuning::Explicit, history::Nothing, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, acceleration::CPU1{Nothing}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:507 [25] build!(history::Nothing, n::Int64, tuning::Explicit, model::DeterministicConstantRegressor, model_buffer::Channel{Any}, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Tuple{DeterministicConstantRegressor, Int64}}, verbosity::Int64, acceleration::CPU1{Nothing}, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:702 [26] fit(::MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, ::Int64, ::Tables.MatrixTable{Matrix{Float64}}, ::Vector{Float64}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:786 [27] fit_only!(mach::Machine{MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, false}; rows::Nothing, verbosity::Int64, force::Bool, composite::Nothing) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:693 [28] fit_only! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:617 [inlined] [29] #fit!#66 @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:790 [inlined] [30] kwcall(::@NamedTuple{verbosity::Int64}, ::typeof(fit!), mach::Machine{MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, false}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:787 [31] top-level scope @ ~/.julia/packages/MLJTuning/vMe8s/test/test_utilities.jl:36 [32] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/tuned_models.jl:344 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [34] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/tuned_models.jl:355 [inlined] [35] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [36] eval_test_function(func::Any, args::Any, kwargs::Any, quoted_func::Union{Expr, Symbol}, source::LineNumberNode, negate::Bool) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:394 [37] top-level scope @ ~/.julia/packages/MLJTuning/vMe8s/test/runtests.jl:32 [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [39] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/runtests.jl:32 [inlined] [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:750 [inlined] [41] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [42] top-level scope @ none:6 [43] eval(m::Module, e::Any) @ Core ./boot.jl:489 [44] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [45] _start() @ Base ./client.jl:577 [ Info: No measure specified. Setting measure=LogLoss(tol = 2.22045e-16). ┌ Error: Problem fitting the machine machine(Resampler(model = DeterministicConstantRegressor(), …), …). └ @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:695 [ Info: Running type checks... [ Info: Type checks okay. ┌ Error: Problem fitting the machine machine(DeterministicTunedModel(model = DeterministicConstantRegressor(), …), …). └ @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:695 [ Info: Running type checks... [ Info: Type checks okay. evaluation object: Error During Test at /home/pkgeval/.julia/packages/MLJTuning/vMe8s/test/test_utilities.jl:19 Got exception outside of a @test UndefVarError: `ltm52` not defined in `Random` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty @ ./Base_compiler.jl:50 [inlined] [2] _shuffle!(r::StableRNGs.LehmerRNG, a::Vector{Int64}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:154 [3] shuffle!(r::StableRNGs.LehmerRNG, a::Vector{Int64}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:144 [4] partition(X::UnitRange{Int64}, fractions::Float64; shuffle::Bool, rng::StableRNGs.LehmerRNG, stratify::Nothing, multi::Bool) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/data/data.jl:181 [5] kwcall(::@NamedTuple{shuffle::Bool, rng::StableRNGs.LehmerRNG}, ::typeof(partition), X::UnitRange{Int64}, fractions::Float64) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/data/data.jl:149 [6] train_test_pairs(holdout::Holdout, rows::UnitRange{Int64}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:239 [7] train_test_pairs @ ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:129 [inlined] [8] train_test_pairs @ ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:127 [inlined] [9] #evaluate!##22 @ ./none:-1 [inlined] [10] iterate @ ./generator.jl:48 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, MLJBase.var"#evaluate!##22#evaluate!##23"{Holdout, UnitRange{Int64}, Tuple{Tables.MatrixTable{Matrix{Float64}}, Vector{Float64}}}}) @ Base ./array.jl:828 [12] evaluate!(::Machine{DeterministicConstantRegressor, DeterministicConstantRegressor, true}, ::Holdout, ::Nothing, ::Nothing, ::Nothing, ::Int64, ::Int64, ::Vector{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.FussyMeasure{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.Multimeasure{StatisticalMeasuresBase.SupportsMissingsMeasure{StatisticalMeasures.LPLossOnScalars{Int64}}, Nothing, StatisticalMeasuresBase.Mean, typeof(identity)}}, Nothing}}}, ::Vector{typeof(predict)}, ::CPU1{Nothing}, ::Bool, ::Bool, ::Nothing, ::Holdout, ::Bool) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:1607 [13] fit(::Resampler{Holdout, Nothing}, ::Int64, ::Tables.MatrixTable{Matrix{Float64}}, ::Vector{Float64}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:1773 [14] fit_only!(mach::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}; rows::Nothing, verbosity::Int64, force::Bool, composite::Nothing) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:693 [15] fit_only! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:617 [inlined] [16] #fit!#66 @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:790 [inlined] [17] fit! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:787 [inlined] [18] event!(metamodel::DeterministicConstantRegressor, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, verbosity::Int64, tuning::Explicit, history::Nothing, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:469 [19] (::MLJTuning.var"#assemble_events!##0#assemble_events!##1"{Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, Int64, Explicit, Nothing, MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, ProgressMeter.Progress})(m::DeterministicConstantRegressor) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:508 [20] iterate @ ./generator.jl:48 [inlined] [21] _collect(c::Vector{Any}, itr::Base.Generator{Vector{Any}, MLJTuning.var"#assemble_events!##0#assemble_events!##1"{Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, Int64, Explicit, Nothing, MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, ProgressMeter.Progress}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base ./array.jl:848 [22] collect_similar @ ./array.jl:763 [inlined] [23] map @ ./abstractarray.jl:3390 [inlined] [24] assemble_events!(metamodels::Vector{Any}, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, verbosity::Int64, tuning::Explicit, history::Nothing, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, acceleration::CPU1{Nothing}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:507 [25] build!(history::Nothing, n::Int64, tuning::Explicit, model::DeterministicConstantRegressor, model_buffer::Channel{Any}, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Tuple{DeterministicConstantRegressor, Int64}}, verbosity::Int64, acceleration::CPU1{Nothing}, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:702 [26] fit(::MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, ::Int64, ::Tables.MatrixTable{Matrix{Float64}}, ::Vector{Float64}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:786 [27] fit_only!(mach::Machine{MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, false}; rows::Nothing, verbosity::Int64, force::Bool, composite::Nothing) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:693 [28] fit_only! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:617 [inlined] [29] #fit!#66 @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:790 [inlined] [30] kwcall(::@NamedTuple{verbosity::Int64}, ::typeof(fit!), mach::Machine{MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, false}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:787 [31] top-level scope @ ~/.julia/packages/MLJTuning/vMe8s/test/test_utilities.jl:19 [32] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/tuned_models.jl:496 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [34] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/tuned_models.jl:506 [inlined] [35] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [36] eval_test_function(func::Any, args::Any, kwargs::Any, quoted_func::Union{Expr, Symbol}, source::LineNumberNode, negate::Bool) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:394 [37] top-level scope @ ~/.julia/packages/MLJTuning/vMe8s/test/runtests.jl:32 [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [39] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/runtests.jl:32 [inlined] [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:750 [inlined] [41] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [42] top-level scope @ none:6 [43] eval(m::Module, e::Any) @ Core ./boot.jl:489 [44] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [45] _start() @ Base ./client.jl:577 ┌ Error: Problem fitting the machine machine(Resampler(model = DeterministicConstantRegressor(), …), …). └ @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:695 [ Info: Running type checks... [ Info: Type checks okay. ┌ Error: Problem fitting the machine machine(DeterministicTunedModel(model = DeterministicConstantRegressor(), …), …). └ @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:695 [ Info: Running type checks... [ Info: Type checks okay. evaluation object (accelerated with typename(CPUProcesses)): Error During Test at /home/pkgeval/.julia/packages/MLJTuning/vMe8s/test/test_utilities.jl:36 Got exception outside of a @test UndefVarError: `ltm52` not defined in `Random` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty @ ./Base_compiler.jl:50 [inlined] [2] _shuffle!(r::StableRNGs.LehmerRNG, a::Vector{Int64}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:154 [3] shuffle!(r::StableRNGs.LehmerRNG, a::Vector{Int64}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:144 [4] partition(X::UnitRange{Int64}, fractions::Float64; shuffle::Bool, rng::StableRNGs.LehmerRNG, stratify::Nothing, multi::Bool) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/data/data.jl:181 [5] kwcall(::@NamedTuple{shuffle::Bool, rng::StableRNGs.LehmerRNG}, ::typeof(partition), X::UnitRange{Int64}, fractions::Float64) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/data/data.jl:149 [6] train_test_pairs(holdout::Holdout, rows::UnitRange{Int64}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:239 [7] train_test_pairs @ ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:129 [inlined] [8] train_test_pairs @ ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:127 [inlined] [9] #evaluate!##22 @ ./none:-1 [inlined] [10] iterate @ ./generator.jl:48 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, MLJBase.var"#evaluate!##22#evaluate!##23"{Holdout, UnitRange{Int64}, Tuple{Tables.MatrixTable{Matrix{Float64}}, Vector{Float64}}}}) @ Base ./array.jl:828 [12] evaluate!(::Machine{DeterministicConstantRegressor, DeterministicConstantRegressor, true}, ::Holdout, ::Nothing, ::Nothing, ::Nothing, ::Int64, ::Int64, ::Vector{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.FussyMeasure{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.Multimeasure{StatisticalMeasuresBase.SupportsMissingsMeasure{StatisticalMeasures.LPLossOnScalars{Int64}}, Nothing, StatisticalMeasuresBase.Mean, typeof(identity)}}, Nothing}}}, ::Vector{typeof(predict)}, ::CPUProcesses{Nothing}, ::Bool, ::Bool, ::Nothing, ::Holdout, ::Bool) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:1607 [13] fit(::Resampler{Holdout, Nothing}, ::Int64, ::Tables.MatrixTable{Matrix{Float64}}, ::Vector{Float64}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:1773 [14] fit_only!(mach::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}; rows::Nothing, verbosity::Int64, force::Bool, composite::Nothing) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:693 [15] fit_only! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:617 [inlined] [16] #fit!#66 @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:790 [inlined] [17] fit! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:787 [inlined] [18] event!(metamodel::DeterministicConstantRegressor, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, verbosity::Int64, tuning::Explicit, history::Nothing, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:469 [19] (::MLJTuning.var"#assemble_events!##0#assemble_events!##1"{Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, Int64, Explicit, Nothing, MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, ProgressMeter.Progress})(m::DeterministicConstantRegressor) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:508 [20] iterate @ ./generator.jl:48 [inlined] [21] _collect(c::Vector{Any}, itr::Base.Generator{Vector{Any}, MLJTuning.var"#assemble_events!##0#assemble_events!##1"{Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, Int64, Explicit, Nothing, MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, ProgressMeter.Progress}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base ./array.jl:848 [22] collect_similar @ ./array.jl:763 [inlined] [23] map @ ./abstractarray.jl:3390 [inlined] [24] assemble_events!(metamodels::Vector{Any}, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, verbosity::Int64, tuning::Explicit, history::Nothing, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, acceleration::CPU1{Nothing}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:507 [25] build!(history::Nothing, n::Int64, tuning::Explicit, model::DeterministicConstantRegressor, model_buffer::Channel{Any}, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Tuple{DeterministicConstantRegressor, Int64}}, verbosity::Int64, acceleration::CPU1{Nothing}, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:702 [26] fit(::MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, ::Int64, ::Tables.MatrixTable{Matrix{Float64}}, ::Vector{Float64}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:786 [27] fit_only!(mach::Machine{MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, false}; rows::Nothing, verbosity::Int64, force::Bool, composite::Nothing) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:693 [28] fit_only! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:617 [inlined] [29] #fit!#66 @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:790 [inlined] [30] kwcall(::@NamedTuple{verbosity::Int64}, ::typeof(fit!), mach::Machine{MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, false}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:787 [31] top-level scope @ ~/.julia/packages/MLJTuning/vMe8s/test/test_utilities.jl:36 [32] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/tuned_models.jl:496 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [34] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/tuned_models.jl:506 [inlined] [35] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [36] eval_test_function(func::Any, args::Any, kwargs::Any, quoted_func::Union{Expr, Symbol}, source::LineNumberNode, negate::Bool) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:394 [37] top-level scope @ ~/.julia/packages/MLJTuning/vMe8s/test/runtests.jl:32 [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [39] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/runtests.jl:32 [inlined] [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:750 [inlined] [41] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [42] top-level scope @ none:6 [43] eval(m::Module, e::Any) @ Core ./boot.jl:489 [44] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [45] _start() @ Base ./client.jl:577 ┌ Error: Problem fitting the machine machine(Resampler(model = DeterministicConstantRegressor(), …), …). └ @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:695 [ Info: Running type checks... [ Info: Type checks okay. ┌ Error: Problem fitting the machine machine(DeterministicTunedModel(model = DeterministicConstantRegressor(), …), …). └ @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:695 [ Info: Running type checks... [ Info: Type checks okay. evaluation object (accelerated with typename(CPUThreads)): Error During Test at /home/pkgeval/.julia/packages/MLJTuning/vMe8s/test/test_utilities.jl:36 Got exception outside of a @test UndefVarError: `ltm52` not defined in `Random` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] getproperty @ ./Base_compiler.jl:50 [inlined] [2] _shuffle!(r::StableRNGs.LehmerRNG, a::Vector{Int64}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:154 [3] shuffle!(r::StableRNGs.LehmerRNG, a::Vector{Int64}) @ StableRNGs ~/.julia/packages/StableRNGs/t609C/src/StableRNGs.jl:144 [4] partition(X::UnitRange{Int64}, fractions::Float64; shuffle::Bool, rng::StableRNGs.LehmerRNG, stratify::Nothing, multi::Bool) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/data/data.jl:181 [5] kwcall(::@NamedTuple{shuffle::Bool, rng::StableRNGs.LehmerRNG}, ::typeof(partition), X::UnitRange{Int64}, fractions::Float64) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/data/data.jl:149 [6] train_test_pairs(holdout::Holdout, rows::UnitRange{Int64}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:239 [7] train_test_pairs @ ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:129 [inlined] [8] train_test_pairs @ ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:127 [inlined] [9] #evaluate!##22 @ ./none:-1 [inlined] [10] iterate @ ./generator.jl:48 [inlined] [11] collect(itr::Base.Generator{UnitRange{Int64}, MLJBase.var"#evaluate!##22#evaluate!##23"{Holdout, UnitRange{Int64}, Tuple{Tables.MatrixTable{Matrix{Float64}}, Vector{Float64}}}}) @ Base ./array.jl:828 [12] evaluate!(::Machine{DeterministicConstantRegressor, DeterministicConstantRegressor, true}, ::Holdout, ::Nothing, ::Nothing, ::Nothing, ::Int64, ::Int64, ::Vector{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.FussyMeasure{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.Multimeasure{StatisticalMeasuresBase.SupportsMissingsMeasure{StatisticalMeasures.LPLossOnScalars{Int64}}, Nothing, StatisticalMeasuresBase.Mean, typeof(identity)}}, Nothing}}}, ::Vector{typeof(predict)}, ::CPUThreads{Int64}, ::Bool, ::Bool, ::Nothing, ::Holdout, ::Bool) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:1607 [13] fit(::Resampler{Holdout, Nothing}, ::Int64, ::Tables.MatrixTable{Matrix{Float64}}, ::Vector{Float64}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/resampling.jl:1773 [14] fit_only!(mach::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}; rows::Nothing, verbosity::Int64, force::Bool, composite::Nothing) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:693 [15] fit_only! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:617 [inlined] [16] #fit!#66 @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:790 [inlined] [17] fit! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:787 [inlined] [18] event!(metamodel::DeterministicConstantRegressor, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, verbosity::Int64, tuning::Explicit, history::Nothing, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:469 [19] (::MLJTuning.var"#assemble_events!##0#assemble_events!##1"{Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, Int64, Explicit, Nothing, MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, ProgressMeter.Progress})(m::DeterministicConstantRegressor) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:508 [20] iterate @ ./generator.jl:48 [inlined] [21] _collect(c::Vector{Any}, itr::Base.Generator{Vector{Any}, MLJTuning.var"#assemble_events!##0#assemble_events!##1"{Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, Int64, Explicit, Nothing, MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, ProgressMeter.Progress}}, ::Base.EltypeUnknown, isz::Base.HasShape{1}) @ Base ./array.jl:848 [22] collect_similar @ ./array.jl:763 [inlined] [23] map @ ./abstractarray.jl:3390 [inlined] [24] assemble_events!(metamodels::Vector{Any}, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}, verbosity::Int64, tuning::Explicit, history::Nothing, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Nothing}, acceleration::CPU1{Nothing}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:507 [25] build!(history::Nothing, n::Int64, tuning::Explicit, model::DeterministicConstantRegressor, model_buffer::Channel{Any}, state::MLJTuning.ExplicitState{Vector{DeterministicConstantRegressor}, Tuple{DeterministicConstantRegressor, Int64}}, verbosity::Int64, acceleration::CPU1{Nothing}, resampling_machine::Machine{Resampler{Holdout, Nothing}, Resampler{Holdout, Nothing}, false}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:702 [26] fit(::MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, ::Int64, ::Tables.MatrixTable{Matrix{Float64}}, ::Vector{Float64}) @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/tuned_models.jl:786 [27] fit_only!(mach::Machine{MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, false}; rows::Nothing, verbosity::Int64, force::Bool, composite::Nothing) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:693 [28] fit_only! @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:617 [inlined] [29] #fit!#66 @ ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:790 [inlined] [30] kwcall(::@NamedTuple{verbosity::Int64}, ::typeof(fit!), mach::Machine{MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, MLJTuning.DeterministicTunedModel{Explicit, Deterministic, Nothing}, false}) @ MLJBase ~/.julia/packages/MLJBase/fpnxN/src/machines.jl:787 [31] top-level scope @ ~/.julia/packages/MLJTuning/vMe8s/test/test_utilities.jl:36 [32] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/tuned_models.jl:496 [inlined] [33] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [34] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/tuned_models.jl:506 [inlined] [35] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [36] eval_test_function(func::Any, args::Any, kwargs::Any, quoted_func::Union{Expr, Symbol}, source::LineNumberNode, negate::Bool) @ Test /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:394 [37] top-level scope @ ~/.julia/packages/MLJTuning/vMe8s/test/runtests.jl:32 [38] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined] [39] macro expansion @ ~/.julia/packages/MLJTuning/vMe8s/test/runtests.jl:32 [inlined] [40] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:750 [inlined] [41] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [42] top-level scope @ none:6 [43] eval(m::Module, e::Any) @ Core ./boot.jl:489 [44] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [45] _start() @ Base ./client.jl:577 Test Summary: | Pass Error Total Time tuned_models.jl | 101 6 107 8m23.0s constructor | 24 24 3.1s measure compatibility check | 2 2 8.1s basic fit (CPU1) | 7 7 9.0s basic fit (CPU1) (accelerated with typename(CPUProcesses)) | 7 7 1m28.6s basic fit (CPU1) (accelerated with typename(CPUThreads)) | 7 7 1.2s Basic fit (CPUThreads) | 1 1 0.2s Basic fit (CPUThreads) (accelerated with typename(CPUProcesses)) | 1 1 2.8s Basic fit (CPUThreads) (accelerated with typename(CPUThreads)) | 1 1 0.9s Basic fit (CPUProcesses) | 1 1 0.2s Basic fit (CPUProcesses) (accelerated with typename(CPUProcesses)) | 1 1 8.2s Basic fit (CPUProcesses) (accelerated with typename(CPUThreads)) | 1 1 0.2s Feature Importances | 3 3 33.5s Feature Importances (accelerated with typename(CPUProcesses)) | 3 3 1m04.7s Feature Importances (accelerated with typename(CPUThreads)) | 3 3 1.1s under/over supply of models | 5 5 3.6s under/over supply of models (accelerated with typename(CPUProcesses)) | 5 5 4.0s under/over supply of models (accelerated with typename(CPUThreads)) | 5 5 1.7s passing of model metadata | 1 1 2.7s passing of model metadata (accelerated with typename(CPUProcesses)) | 1 1 3.4s passing of model metadata (accelerated with typename(CPUThreads)) | 1 1 0.7s data caching | 2 2 29.7s issue #128 | 2 2 30.9s Resampling reproducibility | 1 1 5.7s Resampling reproducibility (accelerated with typename(CPUProcesses)) | 1 1 0.3s Resampling reproducibility (accelerated with typename(CPUThreads)) | 1 1 0.5s deterministic metrics for probabilistic models | 1 1 34.3s weights and class_weights are being passed | 3 3 25.6s weights and class_weights are being passed (accelerated with typename(CPUProcesses)) | 3 3 1m03.4s weights and class_weights are being passed (accelerated with typename(CPUThreads)) | 3 3 0.8s data caching at outer level suppressed | 2 2 3.1s save and restore | 3 3 12.6s evaluation object | 1 1 0.1s evaluation object (accelerated with typename(CPUProcesses)) | 1 1 0.1s evaluation object (accelerated with typename(CPUThreads)) | 1 1 0.1s default logger | 1 1 8.1s RNG of the outermost testset: Xoshiro(0x8173371939f5dad5, 0xe31119ae371e49f8, 0xcf706f2f3e4ee877, 0x37df92e59fdc369a, 0xbef5b45775e0c146) ERROR: LoadError: Some tests did not pass: 101 passed, 0 failed, 6 errored, 0 broken. in expression starting at /home/pkgeval/.julia/packages/MLJTuning/vMe8s/test/runtests.jl:31 Testing failed after 717.7s ERROR: LoadError: Package MLJTuning errored during testing Stacktrace: [1] pkgerror(msg::String) @ Pkg.Types /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Types.jl:68 [2] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, julia_args::Cmd, test_args::Cmd, test_fn::Nothing, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool) @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Operations.jl:2946 [3] test @ /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Operations.jl:2795 [inlined] [4] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, test_fn::Nothing, julia_args::Cmd, test_args::Cmd, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool, kwargs::@Kwargs{io::IOContext{IO}}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:572 [5] kwcall(::@NamedTuple{julia_args::Cmd, io::IOContext{IO}}, ::typeof(Pkg.API.test), ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:548 [6] test(pkgs::Vector{PackageSpec}; io::IOContext{IO}, kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:172 [7] kwcall(::@NamedTuple{julia_args::Cmd}, ::typeof(Pkg.API.test), pkgs::Vector{PackageSpec}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:161 [8] test(pkgs::Vector{String}; kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:160 [9] test @ /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:160 [inlined] [10] kwcall(::@NamedTuple{julia_args::Cmd}, ::typeof(Pkg.API.test), pkg::String) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:159 [11] top-level scope @ /PkgEval.jl/scripts/evaluate.jl:219 [12] include(mod::Module, _path::String) @ Base ./Base.jl:309 [13] exec_options(opts::Base.JLOptions) @ Base ./client.jl:344 [14] _start() @ Base ./client.jl:577 in expression starting at /PkgEval.jl/scripts/evaluate.jl:210 PkgEval failed after 927.77s: package tests unexpectedly errored