Package evaluation to test MLJTuning on Julia 1.14.0-DEV.1372 (893635dc59*) started at 2025-12-16T20:05:11.841 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.14` Set-up completed after 6.72s ################################################################################ # 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 v0.1.43 [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 v0.2.1 [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 v0.19.3 [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 v1.15.0 [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 v1.11.0 [e80e1ace] + MLJModelInterface v1.12.1 [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 v0.9.32 [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 v3.1.2 [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 v3.1.2 [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 v1.0.4 [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 v1.8.0 [2913bbd2] + StatsBase v0.34.9 [4c63d2b9] + StatsFuns v1.5.2 [892a3eda] + StringManipulation v0.4.2 [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 v1.13.0 [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 v1.13.0 [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 Installation completed after 3.5s ################################################################################ # Precompilation # ERROR: LoadError: MethodError: no method matching setindex!(::Base.ScopedValues.ScopedValue{IO}, ::Nothing) The function `setindex!` exists, but no method is defined for this combination of argument types. Stacktrace: [1] top-level scope @ /PkgEval.jl/scripts/precompile.jl:10 [2] include(mod::Module, _path::String) @ Base ./Base.jl:309 [3] exec_options(opts::Base.JLOptions) @ Base ./client.jl:344 [4] _start() @ Base ./client.jl:577 in expression starting at /PkgEval.jl/scripts/precompile.jl:6 caused by: MethodError: no method matching setindex!(::Base.ScopedValues.ScopedValue{IO}, ::Base.DevNull) The function `setindex!` exists, but no method is defined for this combination of argument types. Stacktrace: [1] top-level scope @ /PkgEval.jl/scripts/precompile.jl:7 [2] include(mod::Module, _path::String) @ Base ./Base.jl:309 [3] exec_options(opts::Base.JLOptions) @ Base ./client.jl:344 [4] _start() @ Base ./client.jl:577 Precompilation failed after 11.06s ################################################################################ # Testing # Testing MLJTuning Status `/tmp/jl_Xy1YsV/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 v1.11.0 [e80e1ace] MLJModelInterface v1.12.1 [03970b2e] MLJTuning v0.8.8 [6f286f6a] MultivariateStats v0.10.3 [b8a86587] NearestNeighbors v0.4.26 [92933f4c] ProgressMeter v1.11.0 [3cdcf5f2] RecipesBase v1.3.4 [321657f4] ScientificTypes v3.1.2 [860ef19b] StableRNGs v1.0.4 [a19d573c] StatisticalMeasures v0.3.3 [c062fc1d] StatisticalMeasuresBase v0.1.3 [10745b16] Statistics v1.11.1 [2913bbd2] StatsBase v0.34.9 [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_Xy1YsV/Manifest.toml` [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.43 [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 v0.2.1 [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 v0.19.3 [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 v1.15.0 [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 v1.11.0 [e80e1ace] MLJModelInterface v1.12.1 [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 v0.9.32 [71a1bf82] NameResolution v0.1.5 [b8a86587] NearestNeighbors v0.4.26 [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 v3.1.2 [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 v3.1.2 [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 v1.0.4 [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 v1.8.0 [2913bbd2] StatsBase v0.34.9 [4c63d2b9] StatsFuns v1.5.2 [892a3eda] StringManipulation v0.4.2 [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 v3.5.2+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 v1.13.0 [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 v1.13.0 [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. Testing Running tests... ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("bedc08ee-2515-548f-b156-215fe950b8cd"), "NNlibSpecialFunctionsExt") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=0, inline=true, opt_level=0) Stacktrace:  [1] error(s::String)  @ Base ./error.jl:44  [2] __require_prelocked(pkg::Base.PkgId, env::Nothing)  @ Base ./loading.jl:2813  [3] _require_prelocked(uuidkey::Base.PkgId, env::Nothing)  @ Base ./loading.jl:2665  [4] _require_prelocked  @ ./loading.jl:2659 [inlined]  [5] run_extension_callbacks(extid::Base.ExtensionId)  @ Base ./loading.jl:1711  [6] run_extension_callbacks(pkgid::Base.PkgId)  @ Base ./loading.jl:1748  [7] run_package_callbacks(modkey::Base.PkgId)  @ Base ./loading.jl:1564  [8] _require_search_from_serialized(pkg::Base.PkgId, sourcespec::Base.PkgLoadSpec, build_id::UInt128, stalecheck::Bool; reasons::Dict{String, Int64}, DEPOT_PATH::Vector{String})  @ Base ./loading.jl:2274  [9] _require_search_from_serialized  @ ./loading.jl:2149 [inlined]  [10] __require_prelocked(pkg::Base.PkgId, env::String)  @ Base ./loading.jl:2806  [11] _require_prelocked(uuidkey::Base.PkgId, env::String)  @ Base ./loading.jl:2665  [12] macro expansion  @ ./loading.jl:2593 [inlined]  [13] macro expansion  @ ./lock.jl:376 [inlined]  [14] __require(into::Module, mod::Symbol)  @ Base ./loading.jl:2557  [15] require  @ ./loading.jl:2533 [inlined]  [16] eval_import_path  @ ./module.jl:36 [inlined]  [17] _eval_import(imported::Bool, to::Module, from::Nothing, paths::Expr)  @ Base ./module.jl:111  [18] top-level scope  @ ~/.julia/packages/MLJBase/oYYNJ/src/MLJBase.jl:92  [19] include(mod::Module, _path::String)  @ Base ./Base.jl:309  [20] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)  @ Base ./loading.jl:3250  [21] top-level scope  @ stdin:5  [22] eval(m::Module, e::Any)  @ Core ./boot.jl:489  [23] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)  @ Base ./loading.jl:3092  [24] include_string  @ ./loading.jl:3102 [inlined]  [25] exec_options(opts::Base.JLOptions)  @ Base ./client.jl:342  [26] _start()  @ Base ./client.jl:577 in expression starting at /home/pkgeval/.julia/packages/MLJBase/oYYNJ/src/MLJBase.jl:1 in expression starting at stdin:5 WARNING: Method definition test_insertdelete_laws(Any, Any, Any) in module TestExt at /home/pkgeval/.julia/packages/Accessors/cfsTn/ext/TestExt.jl:31 overwritten in module TestExt on the same line (check for duplicate calls to `include`). WARNING: Method definition kwcall(NamedTuple{names, T} where T<:Tuple where names, typeof(Accessors.test_getsetall_laws), Any, Any, Any, Any) in module TestExt at /home/pkgeval/.julia/packages/Accessors/cfsTn/ext/TestExt.jl:40 overwritten in module TestExt on the same line (check for duplicate calls to `include`). WARNING: Method definition kwcall(NamedTuple{names, T} where T<:Tuple where names, typeof(Accessors.test_getset_laws), Any, Any, Any, Any) in module TestExt at /home/pkgeval/.julia/packages/Accessors/cfsTn/ext/TestExt.jl:6 overwritten in module TestExt on the same line (check for duplicate calls to `include`). WARNING: Method definition kwcall(NamedTuple{names, T} where T<:Tuple where names, typeof(Accessors.test_insertdelete_laws), Any, Any, Any) in module TestExt at /home/pkgeval/.julia/packages/Accessors/cfsTn/ext/TestExt.jl:31 overwritten in module TestExt on the same line (check for duplicate calls to `include`). WARNING: Method definition kwcall(NamedTuple{names, T} where T<:Tuple where names, typeof(Accessors.test_modify_law), Any, Any, Any) in module TestExt at /home/pkgeval/.julia/packages/Accessors/cfsTn/ext/TestExt.jl:23 overwritten in module TestExt on the same line (check for duplicate calls to `include`). WARNING: Method definition test_getset_laws(Any, Any, Any, Any) in module TestExt at /home/pkgeval/.julia/packages/Accessors/cfsTn/ext/TestExt.jl:6 overwritten in module TestExt on the same line (check for duplicate calls to `include`). WARNING: Method definition test_modify_law(Any, Any, Any) in module TestExt at /home/pkgeval/.julia/packages/Accessors/cfsTn/ext/TestExt.jl:23 overwritten in module TestExt on the same line (check for duplicate calls to `include`). WARNING: Method definition test_getsetall_laws(Any, Any, Any, Any) in module TestExt at /home/pkgeval/.julia/packages/Accessors/cfsTn/ext/TestExt.jl:40 overwritten in module TestExt on the same line (check for duplicate calls to `include`). ┌ Warning: Replacing module `TestExt` └ @ Base loading.jl:2718 [ Info: nworkers: 2 [ Info: nthreads: 1 Loading some models for testing... Test Summary: | Pass Total Time utilities | 4 4 4.2s Test Summary: | Pass Total Time selection heuristics | 7 7 2.3s 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: 0:00:15measurement: 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: 0:00:03 [ 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: 0%[> ] ETA: N/A Evaluating over 12 metamodels: 8%[==> ] ETA: 0:12:13 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:03:21 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:01:34 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:01:07 Evaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:48 Evaluating over 12 metamodels: 67%[================> ] ETA: 0:00:34 Evaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:23 Evaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:06 Evaluating over 12 metamodels: 100%[=========================] Time: 0:01:07 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: 0:00:24 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:11 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:07 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:05 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:03 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:00:02 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: 0:00:00 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: 0:00:05 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:03 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:02 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: 0%[> ] ETA: N/A Evaluating over 12 metamodels: 8%[==> ] 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: 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 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: 0:01:11 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:38 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:23 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:15 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: 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: 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: 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: 0:00:00 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 [ Info: No measure specified. Setting measure=LogLoss(tol = 2.22045e-16). Test Summary: | Pass Total Time tuned_models.jl | 119 119 6m47.0s Test Summary: | Pass Total Time range_methods | 33 33 8.2s [ Info: Training machine(ProbabilisticTunedModel(model = KNNClassifier(K = 5, …), …), …). [ Info: Attempting to evaluate 12 models. Evaluating over 12 metamodels: 0%[> ] ETA: N/A Evaluating over 12 metamodels: 8%[==> ] ETA: 0:00:46 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:22 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:13 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:09 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:06 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:00:04 Evaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:03 Evaluating over 12 metamodels: 67%[================> ] ETA: 0:00:02 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: 0:00:04 Evaluating over 3 metamodels: 0%[> ] ETA: N/A Evaluating over 3 metamodels: 33%[========> ] ETA: 0:00:00 Evaluating over 3 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 3 metamodels: 100%[=========================] Time: 0:00:00 Evaluating over 7 metamodels: 29%[=======> ] ETA: 0:00:00 Evaluating over 7 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 7 metamodels: 71%[=================> ] ETA: 0:00:00 Evaluating over 7 metamodels: 86%[=====================> ] ETA: 0:00:00 Evaluating over 7 metamodels: 100%[=========================] Time: 0:00:00 Test Summary: | Pass Total Time grid | 36 36 1m19.2s [ Info: Training machine(DeterministicTunedModel(model = DummyModel(lambda = 1, …), …), …). [ Info: Attempting to evaluate 1000 models. Evaluating over 1000 metamodels: 0%[> ] ETA: N/A Evaluating over 1000 metamodels: 0%[> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 0%[> ] ETA: 0:00:53 Evaluating over 1000 metamodels: 0%[> ] ETA: 0:00:36 Evaluating over 1000 metamodels: 0%[> ] ETA: 0:00:27 Evaluating over 1000 metamodels: 0%[> ] ETA: 0:00:21 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:18 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:15 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:13 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:12 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:11 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:10 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:09 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:08 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:08 Evaluating over 1000 metamodels: 2%[> ] ETA: 0:00:07 Evaluating over 1000 metamodels: 2%[> ] ETA: 0:00:07 Evaluating over 1000 metamodels: 2%[> ] ETA: 0:00:06 Evaluating over 1000 metamodels: 2%[> ] ETA: 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1000 metamodels: 56%[=============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 56%[=============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 56%[=============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 56%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 56%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 56%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 56%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 56%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 56%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 58%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 58%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 58%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 58%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 58%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 58%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 58%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 58%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 58%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 58%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 58%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 59%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 59%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 59%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 59%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 59%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 59%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 59%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 59%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 59%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 60%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 60%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 60%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 60%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 60%[==============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 60%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 60%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 60%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 60%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 60%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 60%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 61%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 61%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 61%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 61%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 61%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 61%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 61%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 61%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 61%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 62%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 62%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 62%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 62%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 62%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 62%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 62%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 62%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 62%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 64%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 64%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 64%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 64%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 64%[===============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 64%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 64%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 64%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 64%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 64%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 64%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 65%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 65%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 65%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 65%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 65%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 65%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 65%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 65%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 65%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 66%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 66%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 66%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 66%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 66%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 66%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 66%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 66%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 66%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 66%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 66%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 68%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 68%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 68%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 68%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 68%[================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 68%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 68%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 68%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 68%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 68%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 68%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 69%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 69%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 69%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 69%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 69%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 69%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 69%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 69%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 69%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 71%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 71%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 71%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 71%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 71%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 71%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 71%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 71%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 72%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 72%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 72%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 72%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 72%[=================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 72%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 72%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 72%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 72%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 72%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 72%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 74%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 74%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 74%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 74%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 74%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 74%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 74%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 74%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 74%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 74%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 74%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 75%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 75%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 75%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 75%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 75%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 75%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 75%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 75%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 75%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 76%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 76%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 76%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 76%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 76%[==================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 76%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 76%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 76%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 76%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 76%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 76%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 77%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 77%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 77%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 77%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 77%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 77%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 77%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 77%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 78%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 78%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 78%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 78%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 78%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 78%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 78%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 78%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 78%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 78%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 78%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 79%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 79%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 79%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 79%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 79%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 79%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 79%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 79%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 79%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 80%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 80%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 80%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 80%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 80%[===================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 80%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 80%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 80%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 80%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 80%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 80%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 81%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 81%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 100%[=========================] Time: 0:00:04 Test Summary: | Pass Total Time random search | 19 19 18.6s ┌ Info: Only 19 (of 100) models evaluated. └ Model supply exhausted. Test Summary: | Pass Total Time Latin hypercube | 28 28 38.7s Test Summary: | Pass Total Time Explicit | 17 17 1m12.0s Testing progressmeter rngs option with CPU1{Nothing}(nothing) and CPU1 grid Evaluating over 30 metamodels: 0%[> ] ETA: N/A Evaluating over 30 metamodels: 3%[> ] ETA: 0:01:51 Evaluating over 30 metamodels: 7%[=> ] ETA: 0:01:11 Evaluating over 30 metamodels: 10%[==> ] ETA: 0:00:45 Evaluating over 30 metamodels: 13%[===> ] ETA: 0:00:33 Evaluating over 30 metamodels: 17%[====> ] ETA: 0:00:25 Evaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:20 Evaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:17 Evaluating over 30 metamodels: 27%[======> ] ETA: 0:00:14 Evaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:12 Evaluating over 30 metamodels: 33%[========> ] ETA: 0:00:10 Evaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:09 Evaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:08 Evaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:07 Evaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:06 Evaluating over 30 metamodels: 50%[============> ] ETA: 0:00:05 Evaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:04 Evaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:04 Evaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:03 Evaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:03 Evaluating over 30 metamodels: 67%[================> ] ETA: 0:00:03 Evaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:02 Evaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:02 Evaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:02 Evaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:01 Evaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:01 Evaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:01 Evaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:01 Evaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 30 metamodels: 100%[=========================] Time: 0:00:05 [ Info: No measure specified. Setting measure=LPLoss(p = 2). Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A Evaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:00:04 Evaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:01 Evaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:02 [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: No measure specified. Setting measure=LPLoss(p = 2). Testing progressmeter rngs option with CPUProcesses{Nothing}(nothing) and CPU1 grid [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: Training machine(DeterministicTunedModel(model = DeterministicEnsembleModel(atom = FooBarRegressor(lambda = 0.0), …), …), …). [ Info: Attempting to evaluate 30 models. Evaluating over 30 metamodels: 0%[> ] ETA: N/A Evaluating over 30 metamodels: 3%[> ] ETA: 0:00:00 Evaluating over 30 metamodels: 7%[=> ] ETA: 0:00:00 Evaluating over 30 metamodels: 10%[==> ] ETA: 0:00:00 Evaluating over 30 metamodels: 13%[===> ] ETA: 0:00:00 Evaluating over 30 metamodels: 17%[====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 27%[======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 33%[========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 30 metamodels: 100%[=========================] Time: 0:00:00 [ Info: No measure specified. Setting measure=LPLoss(p = 2). Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A Evaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:01:32 Evaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:23 Evaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:46 [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: No measure specified. Setting measure=LPLoss(p = 2). Testing progressmeter rngs option with CPUThreads{Nothing}(nothing) and CPU1 grid [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: Training machine(DeterministicTunedModel(model = DeterministicEnsembleModel(atom = FooBarRegressor(lambda = 0.0), …), …), …). [ Info: Attempting to evaluate 30 models. Evaluating over 30 metamodels: 0%[> ] ETA: N/A Evaluating over 30 metamodels: 3%[> ] ETA: 0:00:00 Evaluating over 30 metamodels: 7%[=> ] ETA: 0:00:00 Evaluating over 30 metamodels: 10%[==> ] ETA: 0:00:00 Evaluating over 30 metamodels: 13%[===> ] ETA: 0:00:00 Evaluating over 30 metamodels: 17%[====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 27%[======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 33%[========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 30 metamodels: 100%[=========================] Time: 0:00:00 [ Info: No measure specified. Setting measure=LPLoss(p = 2). Evaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:00 Evaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:00 [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: No measure specified. Setting measure=LPLoss(p = 2). Testing progressmeter rngs option with CPU1{Nothing}(nothing) and CPUThreads grid Evaluating over 30 metamodels: 0%[> ] ETA: N/A Evaluating over 30 metamodels: 3%[> ] ETA: 0:00:00 Evaluating over 30 metamodels: 7%[=> ] ETA: 0:00:00 Evaluating over 30 metamodels: 10%[==> ] ETA: 0:00:00 Evaluating over 30 metamodels: 13%[===> ] ETA: 0:00:00 Evaluating over 30 metamodels: 17%[====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 27%[======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 33%[========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 30 metamodels: 100%[=========================] Time: 0:00:00 [ Info: No measure specified. Setting measure=LPLoss(p = 2). Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A Evaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:00:01 Evaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:00 Evaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:00 [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: No measure specified. Setting measure=LPLoss(p = 2). Testing progressmeter rngs option with CPUProcesses{Nothing}(nothing) and CPUThreads grid [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: Training machine(DeterministicTunedModel(model = DeterministicEnsembleModel(atom = FooBarRegressor(lambda = 0.0), …), …), …). [ Info: Attempting to evaluate 30 models. Evaluating over 30 metamodels: 0%[> ] ETA: N/A Evaluating over 30 metamodels: 3%[> ] ETA: 0:00:00 Evaluating over 30 metamodels: 7%[=> ] ETA: 0:00:00 Evaluating over 30 metamodels: 10%[==> ] ETA: 0:00:00 Evaluating over 30 metamodels: 13%[===> ] ETA: 0:00:00 Evaluating over 30 metamodels: 17%[====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 27%[======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 33%[========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 30 metamodels: 100%[=========================] Time: 0:00:00 [ Info: No measure specified. Setting measure=LPLoss(p = 2). Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A Evaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:00:03 Evaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:01 Evaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:01 [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: No measure specified. Setting measure=LPLoss(p = 2). Testing progressmeter rngs option with CPUThreads{Nothing}(nothing) and CPUThreads grid [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: Training machine(DeterministicTunedModel(model = DeterministicEnsembleModel(atom = FooBarRegressor(lambda = 0.0), …), …), …). [ Info: Attempting to evaluate 30 models. Evaluating over 30 metamodels: 7%[=> ] ETA: 0:00:00 Evaluating over 30 metamodels: 10%[==> ] ETA: 0:00:00 Evaluating over 30 metamodels: 13%[===> ] ETA: 0:00:00 Evaluating over 30 metamodels: 17%[====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 27%[======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 33%[========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 30 metamodels: 100%[=========================] Time: 0:00:00 [ Info: No measure specified. Setting measure=LPLoss(p = 2). Evaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:00 Evaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:00 [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: No measure specified. Setting measure=LPLoss(p = 2). Testing progressmeter rngs option with CPU1{Nothing}(nothing) and CPUProcesses grid Evaluating over 30 metamodels: 0%[> ] ETA: N/A Evaluating over 30 metamodels: 3%[> ] ETA: 0:01:09 Evaluating over 30 metamodels: 10%[==> ] ETA: 0:00:21 Evaluating over 30 metamodels: 13%[===> ] ETA: 0:00:15 Evaluating over 30 metamodels: 17%[====> ] ETA: 0:00:13 Evaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:10 Evaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:07 Evaluating over 30 metamodels: 33%[========> ] ETA: 0:00:06 Evaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:05 Evaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:04 Evaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:04 Evaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:03 Evaluating over 30 metamodels: 50%[============> ] ETA: 0:00:03 Evaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:03 Evaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:02 Evaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:02 Evaluating over 30 metamodels: 67%[================> ] ETA: 0:00:01 Evaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:01 Evaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:01 Evaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:01 Evaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:01 Evaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 30 metamodels: 100%[=========================] Time: 0:00:02 [ Info: No measure specified. Setting measure=LPLoss(p = 2). Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A Evaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:00:02 Evaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:00 Evaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:00 [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: No measure specified. Setting measure=LPLoss(p = 2). Testing progressmeter rngs option with CPUProcesses{Nothing}(nothing) and CPUProcesses grid ┌ Warning: The combination acceleration=CPUProcesses{Nothing}(nothing) and acceleration_grid=CPUProcesses{Nothing}(nothing) is not generally optimal. You may want to consider setting `acceleration = CPUProcesses()` and `acceleration_grid = CPUThreads()`. └ @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/learning_curves.jl:137 [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: Training machine(DeterministicTunedModel(model = DeterministicEnsembleModel(atom = FooBarRegressor(lambda = 0.0), …), …), …). [ Info: Attempting to evaluate 30 models. Evaluating over 30 metamodels: 0%[> ] ETA: N/A Evaluating over 30 metamodels: 3%[> ] ETA: 0:00:00 Evaluating over 30 metamodels: 10%[==> ] ETA: 0:00:00 Evaluating over 30 metamodels: 13%[===> ] ETA: 0:00:00 Evaluating over 30 metamodels: 17%[====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 27%[======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 33%[========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 30 metamodels: 100%[=========================] Time: 0:00:00 ┌ Warning: The combination acceleration=CPUProcesses{Nothing}(nothing) and acceleration_grid=CPUProcesses{Nothing}(nothing) is not generally optimal. You may want to consider setting `acceleration = CPUProcesses()` and `acceleration_grid = CPUThreads()`. └ @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/learning_curves.jl:137 [ Info: No measure specified. Setting measure=LPLoss(p = 2). Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A Evaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:00:08 Evaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:02 Evaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:03 ┌ Warning: The combination acceleration=CPUProcesses{Nothing}(nothing) and acceleration_grid=CPUProcesses{Nothing}(nothing) is not generally optimal. You may want to consider setting `acceleration = CPUProcesses()` and `acceleration_grid = CPUThreads()`. └ @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/learning_curves.jl:137 [ Info: No measure specified. Setting measure=LPLoss(p = 2). ┌ Warning: The combination acceleration=CPUProcesses{Nothing}(nothing) and acceleration_grid=CPUProcesses{Nothing}(nothing) is not generally optimal. You may want to consider setting `acceleration = CPUProcesses()` and `acceleration_grid = CPUThreads()`. └ @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/learning_curves.jl:137 [ Info: No measure specified. Setting measure=LPLoss(p = 2). ┌ Warning: The combination acceleration=CPUProcesses{Nothing}(nothing) and acceleration_grid=CPUProcesses{Nothing}(nothing) is not generally optimal. You may want to consider setting `acceleration = CPUProcesses()` and `acceleration_grid = CPUThreads()`. └ @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/learning_curves.jl:137 [ Info: No measure specified. Setting measure=LPLoss(p = 2). Testing progressmeter rngs option with CPUThreads{Nothing}(nothing) and CPUProcesses grid ┌ Warning: The combination acceleration=CPUThreads{Nothing}(nothing) and acceleration_grid=CPUProcesses{Nothing}(nothing) isn't supported. │ Resetting to `acceleration = CPUProcesses()` and `acceleration_grid = CPUThreads()`. └ @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/learning_curves.jl:147 [ Info: No measure specified. Setting measure=LPLoss(p = 2). [ Info: Training machine(DeterministicTunedModel(model = DeterministicEnsembleModel(atom = FooBarRegressor(lambda = 0.0), …), …), …). [ Info: Attempting to evaluate 30 models. Evaluating over 30 metamodels: 0%[> ] ETA: N/A Evaluating over 30 metamodels: 3%[> ] ETA: 0:00:00 Evaluating over 30 metamodels: 7%[=> ] ETA: 0:00:00 Evaluating over 30 metamodels: 10%[==> ] ETA: 0:00:00 Evaluating over 30 metamodels: 13%[===> ] ETA: 0:00:00 Evaluating over 30 metamodels: 17%[====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:00 Evaluating over 30 metamodels: 27%[======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 33%[========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 67%[================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 30 metamodels: 100%[=========================] Time: 0:00:00 ┌ Warning: The combination acceleration=CPUThreads{Nothing}(nothing) and acceleration_grid=CPUProcesses{Nothing}(nothing) isn't supported. │ Resetting to `acceleration = CPUProcesses()` and `acceleration_grid = CPUThreads()`. └ @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/learning_curves.jl:147 [ Info: No measure specified. Setting measure=LPLoss(p = 2). Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A Evaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:00:00 Evaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:00 ┌ Warning: The combination acceleration=CPUThreads{Nothing}(nothing) and acceleration_grid=CPUProcesses{Nothing}(nothing) isn't supported. │ Resetting to `acceleration = CPUProcesses()` and `acceleration_grid = CPUThreads()`. └ @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/learning_curves.jl:147 [ Info: No measure specified. Setting measure=LPLoss(p = 2). ┌ Warning: The combination acceleration=CPUThreads{Nothing}(nothing) and acceleration_grid=CPUProcesses{Nothing}(nothing) isn't supported. │ Resetting to `acceleration = CPUProcesses()` and `acceleration_grid = CPUThreads()`. └ @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/learning_curves.jl:147 [ Info: No measure specified. Setting measure=LPLoss(p = 2). ┌ Warning: The combination acceleration=CPUThreads{Nothing}(nothing) and acceleration_grid=CPUProcesses{Nothing}(nothing) isn't supported. │ Resetting to `acceleration = CPUProcesses()` and `acceleration_grid = CPUThreads()`. └ @ MLJTuning ~/.julia/packages/MLJTuning/vMe8s/src/learning_curves.jl:147 [ Info: No measure specified. Setting measure=LPLoss(p = 2). Test Summary: | Pass Total Time learning curves | 85 85 1m25.6s [ Info: No measure specified. Setting measure=LPLoss(p = 2). Test Summary: | Pass Total Time Serialization | 13 13 8.3s Testing MLJTuning tests passed Testing completed after 1069.2s PkgEval succeeded after 1111.72s