Package evaluation to test MLJTuning on Julia 1.12.7-DEV.43 (fe4faaea78*) started at 2026-06-22T01:46:31.455 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.12` Set-up completed after 8.49s ################################################################################ # Installation # Installing MLJTuning... Resolving package versions... Updating `~/.julia/environments/v1.12/Project.toml` [03970b2e] + MLJTuning v0.8.9 Updating `~/.julia/environments/v1.12/Manifest.toml` [79e6a3ab] + Adapt v4.6.1 [66dad0bd] + AliasTables v1.1.3 [a9b6321e] + Atomix v1.1.3 [49dc2e85] + Calculus v0.5.2 [324d7699] + CategoricalArrays v1.1.1 [af321ab8] + CategoricalDistributions v0.2.2 [d360d2e6] + ChainRulesCore v1.26.1 [3da002f7] + ColorTypes v0.12.1 [34da2185] + Compat v4.18.1 [ed09eef8] + ComputationalResources v0.3.2 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.19.5 [e2d170a0] + DataValueInterfaces v1.0.0 [8bb1440f] + DelimitedFiles v1.9.1 [31c24e10] + Distributions v0.25.127 [ffbed154] + DocStringExtensions v0.9.5 [1a297f60] + FillArrays v1.16.0 ⌅ [53c48c17] + FixedPointNumbers v0.8.6 [46192b85] + GPUArraysCore v0.2.0 [076d061b] + HashArrayMappedTries v0.2.0 [34004b35] + HypergeometricFunctions v0.3.28 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.6 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.8.0 [63c18a36] + KernelAbstractions v0.9.41 [b964fa9f] + LaTeXStrings v1.4.0 [a5e1c1ea] + LatinHypercubeSampling v1.9.0 [92ad9a40] + LearnAPI v2.0.1 [2ab3a3ac] + LogExpFunctions v1.0.1 [c2834f40] + MLCore v1.0.2 [a7f614a8] + MLJBase v1.13.1 [e80e1ace] + MLJModelInterface v1.12.1 [03970b2e] + MLJTuning v0.8.9 [f1d291b0] + MLUtils v0.4.9 [1914dd2f] + MacroTools v0.5.16 [eff96d63] + Measurements v2.14.1 [e1d29d7a] + Missings v1.2.0 [872c559c] + NNlib v0.9.36 ⌅ [bac558e1] + OrderedCollections v1.8.2 [90014a1f] + PDMats v0.11.37 [d96e819e] + Parameters v0.13.1 [aea7be01] + PrecompileTools v1.3.4 [21216c6a] + Preferences v1.5.2 [08abe8d2] + PrettyTables v3.3.2 [92933f4c] + ProgressMeter v1.11.0 [43287f4e] + PtrArrays v1.4.0 [1fd47b50] + QuadGK v2.11.3 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [79098fc4] + Rmath v0.9.0 [321657f4] + ScientificTypes v3.3.0 [30f210dd] + ScientificTypesBase v3.1.0 [7e506255] + ScopedValues v1.6.2 [605ecd9f] + ShowCases v0.1.0 [699a6c99] + SimpleTraits v0.9.6 [a2af1166] + SortingAlgorithms v1.2.3 [276daf66] + SpecialFunctions v2.8.0 [860ef19b] + StableRNGs v1.0.4 [90137ffa] + StaticArrays v1.9.18 [1e83bf80] + StaticArraysCore v1.4.4 [c062fc1d] + StatisticalMeasuresBase v0.1.4 [64bff920] + StatisticalTraits v3.5.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.8.0 [2913bbd2] + StatsBase v0.34.12 [4c63d2b9] + StatsFuns v2.2.0 [892a3eda] + StringManipulation v0.4.4 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [3a884ed6] + UnPack v1.0.2 [013be700] + UnsafeAtomics v0.3.1 [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 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.12.0 [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 v0.7.0 [9e88b42a] + Serialization v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.12.0 [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 v7.8.3+2 [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 6.43s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompiling packages... 1783.6 ms ✓ Calculus 1072.7 ms ✓ Parameters 4369.9 ms ✓ UnsafeAtomics 4091.2 ms ✓ ShowCases 1012.3 ms ✓ ComputationalResources 894.1 ms ✓ HashArrayMappedTries 1020.0 ms ✓ ScikitLearnBase 1650.3 ms ✓ PDMats → StatsBaseExt 1715.4 ms ✓ ScientificTypesBase 6507.9 ms ✓ CategoricalArrays 1822.7 ms ✓ LearnAPI 2396.4 ms ✓ LatinHypercubeSampling 4050.8 ms ✓ MLCore 1527.5 ms ✓ Arpack_jll 4843.6 ms ✓ SpecialFunctions → SpecialFunctionsChainRulesCoreExt 2849.2 ms ✓ HypergeometricFunctions 1461.6 ms ✓ Measurements 949.5 ms ✓ Atomix 1029.8 ms ✓ ScopedValues 2046.8 ms ✓ DecisionTree 1437.2 ms ✓ StatisticalTraits 1643.2 ms ✓ CategoricalArrays → CategoricalArraysStatsBaseExt 3967.0 ms ✓ CategoricalArrays → CategoricalArraysRecipesBaseExt 1559.4 ms ✓ Arpack 13916.1 ms ✓ StatsFuns 1044.2 ms ✓ Measurements → MeasurementsSpecialFunctionsExt 1336.3 ms ✓ Measurements → MeasurementsRecipesBaseExt 14052.5 ms ✓ KernelAbstractions 2926.2 ms ✓ MLJModelInterface 4537.4 ms ✓ StatsFuns → StatsFunsChainRulesCoreExt 17110.5 ms ✓ Distributions 3237.2 ms ✓ KernelAbstractions → SparseArraysExt 2367.7 ms ✓ KernelAbstractions → LinearAlgebraExt 3804.6 ms ✓ Distributions → DistributionsTestExt 6004.6 ms ✓ Distributions → DistributionsChainRulesCoreExt 124231.5 ms ✓ ScientificTypes 7010.4 ms ✓ MultivariateStats 38842.9 ms ✓ NNlib 12735.0 ms ✓ CategoricalDistributions 2816.3 ms ✓ NNlib → NNlibSpecialFunctionsExt 50813.2 ms ✓ MLUtils 20581.6 ms ✓ StatisticalMeasuresBase 70909.3 ms ✓ StatisticalMeasures 35695.8 ms ✓ MLJBase 9642.4 ms ✓ StatisticalMeasures → ScientificTypesExt 9677.4 ms ✓ MLJBase → DefaultMeasuresExt 21529.6 ms ✓ MLJTuning 47 dependencies successfully precompiled in 531 seconds. 89 already precompiled. 9 dependencies precompiled but different versions are currently loaded (Base64, Dates, JuliaSyntaxHighlighting, Logging, Markdown, Printf, StyledStrings, TOML and UUIDs). Restart julia to access the new versions. Otherwise, 68 dependents of these packages may trigger further precompilation to work with the unexpected versions. Precompilation completed after 547.92s ################################################################################ # Testing # Testing MLJTuning Status `/tmp/jl_rx7RiL/Project.toml` [324d7699] CategoricalArrays v1.1.1 [ed09eef8] ComputationalResources v0.3.2 [7806a523] DecisionTree v0.12.4 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.127 [a5e1c1ea] LatinHypercubeSampling v1.9.0 [a7f614a8] MLJBase v1.13.1 [e80e1ace] MLJModelInterface v1.12.1 [03970b2e] MLJTuning v0.8.9 [6f286f6a] MultivariateStats v0.10.4 [b8a86587] NearestNeighbors v0.4.27 [92933f4c] ProgressMeter v1.11.0 [3cdcf5f2] RecipesBase v1.3.4 [321657f4] ScientificTypes v3.3.0 [860ef19b] StableRNGs v1.0.4 [a19d573c] StatisticalMeasures v0.3.6 [c062fc1d] StatisticalMeasuresBase v0.1.4 [10745b16] Statistics v1.11.1 [2913bbd2] StatsBase v0.34.12 [bd369af6] Tables v1.12.1 [8ba89e20] Distributed v1.11.0 [37e2e46d] LinearAlgebra v1.12.0 [9a3f8284] Random v1.11.0 [9e88b42a] Serialization v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_rx7RiL/Manifest.toml` [1520ce14] AbstractTrees v0.4.5 [79e6a3ab] Adapt v4.6.1 [66dad0bd] AliasTables v1.1.3 [7d9fca2a] Arpack v0.5.4 [a9b6321e] Atomix v1.1.3 [49dc2e85] Calculus v0.5.2 [324d7699] CategoricalArrays v1.1.1 [af321ab8] CategoricalDistributions v0.2.2 [d360d2e6] ChainRulesCore v1.26.1 [3da002f7] ColorTypes v0.12.1 [34da2185] Compat v4.18.1 [ed09eef8] ComputationalResources v0.3.2 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [864edb3b] DataStructures v0.19.5 [e2d170a0] DataValueInterfaces v1.0.0 [7806a523] DecisionTree v0.12.4 [8bb1440f] DelimitedFiles v1.9.1 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.127 [ffbed154] DocStringExtensions v0.9.5 [1a297f60] FillArrays v1.16.0 ⌅ [53c48c17] FixedPointNumbers v0.8.6 [46192b85] GPUArraysCore v0.2.0 [076d061b] HashArrayMappedTries v0.2.0 [34004b35] HypergeometricFunctions v0.3.28 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.6 [82899510] IteratorInterfaceExtensions v1.0.0 [692b3bcd] JLLWrappers v1.8.0 [63c18a36] KernelAbstractions v0.9.41 [b964fa9f] LaTeXStrings v1.4.0 [a5e1c1ea] LatinHypercubeSampling v1.9.0 [92ad9a40] LearnAPI v2.0.1 [2ab3a3ac] LogExpFunctions v1.0.1 [c2834f40] MLCore v1.0.2 [a7f614a8] MLJBase v1.13.1 [e80e1ace] MLJModelInterface v1.12.1 [03970b2e] MLJTuning v0.8.9 [f1d291b0] MLUtils v0.4.9 [1914dd2f] MacroTools v0.5.16 [eff96d63] Measurements v2.14.1 [e1d29d7a] Missings v1.2.0 [6f286f6a] MultivariateStats v0.10.4 [872c559c] NNlib v0.9.36 [b8a86587] NearestNeighbors v0.4.27 ⌅ [bac558e1] OrderedCollections v1.8.2 [90014a1f] PDMats v0.11.37 [d96e819e] Parameters v0.13.1 [aea7be01] PrecompileTools v1.3.4 [21216c6a] Preferences v1.5.2 [08abe8d2] PrettyTables v3.3.2 [92933f4c] ProgressMeter v1.11.0 [43287f4e] PtrArrays v1.4.0 [1fd47b50] QuadGK v2.11.3 [3cdcf5f2] RecipesBase v1.3.4 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 [79098fc4] Rmath v0.9.0 [321657f4] ScientificTypes v3.3.0 [30f210dd] ScientificTypesBase v3.1.0 [6e75b9c4] ScikitLearnBase v0.5.0 [7e506255] ScopedValues v1.6.2 [605ecd9f] ShowCases v0.1.0 [699a6c99] SimpleTraits v0.9.6 [a2af1166] SortingAlgorithms v1.2.3 [276daf66] SpecialFunctions v2.8.0 [860ef19b] StableRNGs v1.0.4 [90137ffa] StaticArrays v1.9.18 [1e83bf80] StaticArraysCore v1.4.4 [a19d573c] StatisticalMeasures v0.3.6 [c062fc1d] StatisticalMeasuresBase v0.1.4 [64bff920] StatisticalTraits v3.5.0 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.8.0 [2913bbd2] StatsBase v0.34.12 [4c63d2b9] StatsFuns v2.2.0 [892a3eda] StringManipulation v0.4.4 [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.1 [3a884ed6] UnPack v1.0.2 [013be700] UnsafeAtomics v0.3.1 ⌅ [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 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [ac6e5ff7] JuliaSyntaxHighlighting v1.12.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.12.0 [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 v0.7.0 [9e88b42a] Serialization v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.12.0 [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 v7.8.3+2 [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... Precompiling packages... 7542.8 ms ✓ Distributions → DistributionsTestExt 1 dependency successfully precompiled in 9 seconds. 48 already precompiled. 48 dependencies precompiled but different versions are currently loaded (AliasTables, Base64, CompilerSupportLibraries_jll, DataAPI, DataStructures, Dates, Distributions, DocStringExtensions, FillArrays, FillArrays → FillArraysPDMatsExt, FillArrays → FillArraysSparseArraysExt, FillArrays → FillArraysStatisticsExt, HypergeometricFunctions, InteractiveUtils, IrrationalConstants, JLLWrappers, JuliaSyntaxHighlighting, LogExpFunctions, Logging, Markdown, Missings, OpenLibm_jll, OpenSpecFun_jll, OrderedCollections, PDMats, PDMats → StatsBaseExt, Preferences, Printf, PtrArrays, QuadGK, Reexport, Rmath, Rmath_jll, Serialization, SortingAlgorithms, SparseArrays, SpecialFunctions, Statistics, Statistics → SparseArraysExt, StatsAPI, StatsBase, StatsFuns, StyledStrings, SuiteSparse, SuiteSparse_jll, TOML, Test and Unicode). Restart julia to access the new versions. Otherwise, 1 dependent of these packages may trigger further precompilation to work with the unexpected versions. [ Info: nworkers: 2 [ Info: nthreads: 1 Loading some models for testing...Precompiling packages... 1048.7 ms ✓ Distances → DistancesChainRulesCoreExt 1 dependency successfully precompiled in 1 seconds. 11 already precompiled. 11 dependencies precompiled but different versions are currently loaded (ChainRulesCore, Compat, Compat → CompatLinearAlgebraExt, Dates, Distances, Printf, Statistics, StatsAPI, TOML, UUIDs and Unicode). Restart julia to access the new versions. Otherwise, 1 dependent of these packages may trigger further precompilation to work with the unexpected versions. Precompiling packages... 10057.6 ms ✓ NearestNeighbors 1 dependency successfully precompiled in 10 seconds. 13 already precompiled. 13 dependencies precompiled but different versions are currently loaded (AbstractTrees, Dates, Distances, PrecompileTools, Preferences, Printf, StaticArrays, StaticArrays → StaticArraysStatisticsExt, StaticArraysCore, Statistics, StatsAPI, TOML and Unicode). Restart julia to access the new versions. Otherwise, 1 dependent of these packages may trigger further precompilation to work with the unexpected versions. Test Summary: | Pass Total Time utilities | 11 11 4.7s Test Summary: | Pass Total Time selection heuristics | 7 7 2.8s 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: 0:00:22measurement: 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: 0%[> ] ETA: N/A Evaluating over 12 metamodels: 8%[==> ] ETA: 0:19:59 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:09:08 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:05:30 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:03:42 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:02:36 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:01:52 Evaluating over 12 metamodels: 58%[==============> ] ETA: 0:01:20 Evaluating over 12 metamodels: 67%[================> ] ETA: 0:00:56 Evaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:37 Evaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:23 Evaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:10 Evaluating over 12 metamodels: 100%[=========================] Time: 0:01:53 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: 0:00:32 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:15 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:10 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:07 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:05 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:00:03 Evaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:02 Evaluating over 12 metamodels: 67%[================> ] ETA: 0:00:02 Evaluating over 12 metamodels: 100%[=========================] Time: 0:00:03 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: 75%[==================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 83%[====================> ] 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: 0:01:49 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:51 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:31 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:21 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:15 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:00:10 Evaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:07 Evaluating over 12 metamodels: 67%[================> ] ETA: 0:00:05 Evaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:03 Evaluating over 12 metamodels: 100%[=========================] Time: 0:00:10 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: 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 [ Info: No measure specified. Setting measure=LogLoss(tol = 2.22045e-16). Test Summary: | Pass Total Time tuned_models.jl | 119 119 10m12.5s Test Summary: | Pass Total Time range_methods | 33 33 13.0s [ 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: 0:00:59 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:28 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:17 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:11 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:08 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:00:06 Evaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:04 Evaluating over 12 metamodels: 67%[================> ] ETA: 0:00:03 Evaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:02 Evaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:01 Evaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:01 Evaluating over 12 metamodels: 100%[=========================] Time: 0:00:05 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: 0%[> ] ETA: N/A Evaluating over 7 metamodels: 14%[===> ] ETA: 0:00:00 Evaluating over 7 metamodels: 29%[=======> ] ETA: 0:00:00 Evaluating over 7 metamodels: 43%[==========> ] 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 1m52.5s [ Info: Training machine(DeterministicTunedModel(model = DummyModel(lambda = 1, …), …), …). [ Info: Attempting to evaluate 1000 models. Evaluating over 1000 metamodels: 0%[> ] ETA: 0:01:24 Evaluating over 1000 metamodels: 0%[> ] ETA: 0:00:56 Evaluating over 1000 metamodels: 0%[> ] ETA: 0:00:42 Evaluating over 1000 metamodels: 0%[> ] ETA: 0:00:34 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:28 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:24 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:21 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:19 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:17 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:15 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:14 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:13 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:12 Evaluating over 1000 metamodels: 2%[> ] ETA: 0:00:11 Evaluating over 1000 metamodels: 2%[> ] ETA: 0:00:11 Evaluating over 1000 metamodels: 2%[> ] ETA: 0:00:10 Evaluating over 1000 metamodels: 2%[> ] ETA: 0:00:09 Evaluating over 1000 metamodels: 2%[> ] ETA: 0:00:09 Evaluating over 1000 metamodels: 2%[> ] ETA: 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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: 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: 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: 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: 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: 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: 81%[====================> ] 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: 81%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 82%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 82%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 82%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 82%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 82%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 82%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 82%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 82%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 82%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 83%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 84%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 84%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 84%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 84%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 84%[====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 84%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 84%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 84%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 84%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 84%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 84%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 85%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 85%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 85%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 85%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 85%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 85%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 85%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 86%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 86%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 86%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 86%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 86%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 86%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 86%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 86%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 86%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 87%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 87%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 87%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 87%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 87%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 87%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 87%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 88%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 88%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 88%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 88%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 88%[=====================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 88%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 88%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 88%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 88%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 88%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 88%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 89%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 89%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 89%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 89%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 89%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 89%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 89%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 90%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 91%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 91%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 91%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 91%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 91%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 91%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 91%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 91%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 92%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 92%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 92%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 92%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 92%[======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 92%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 92%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 92%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 92%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 93%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 94%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 94%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 94%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 94%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 94%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 94%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 94%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 94%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 95%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 95%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 95%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 95%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 95%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 95%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 95%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 96%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 96%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 96%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 96%[=======================> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 96%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 96%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 96%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 96%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 96%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00 Evaluating over 1000 metamodels: 100%[=========================] Time: 0:00:00 Test Summary: | Pass Total Time random search | 19 19 19.6s ┌ Info: Only 19 (of 100) models evaluated. └ Model supply exhausted. Test Summary: | Pass Total Time Latin hypercube | 28 28 56.5s Test Summary: | Pass Total Time Explicit | 17 17 1m51.1s 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: 0:03:00 Evaluating over 30 metamodels: 7%[=> ] ETA: 0:01:41 Evaluating over 30 metamodels: 10%[==> ] ETA: 0:01:05 Evaluating over 30 metamodels: 13%[===> ] ETA: 0:00:47 Evaluating over 30 metamodels: 17%[====> ] ETA: 0:00:36 Evaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:29 Evaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:24 Evaluating over 30 metamodels: 27%[======> ] ETA: 0:00:20 Evaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:17 Evaluating over 30 metamodels: 33%[========> ] ETA: 0:00:14 Evaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:12 Evaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:11 Evaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:09 Evaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:08 Evaluating over 30 metamodels: 50%[============> ] ETA: 0:00:07 Evaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:06 Evaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:06 Evaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:05 Evaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:04 Evaluating over 30 metamodels: 67%[================> ] ETA: 0:00:04 Evaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:03 Evaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:03 Evaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:02 Evaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:02 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: 0:00:01 Evaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00 Evaluating over 30 metamodels: 100%[=========================] Time: 0:00:07 [ 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: 0:00:05 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: 0:00:02 Evaluating over 30 metamodels: 7%[=> ] ETA: 0:00:01 Evaluating over 30 metamodels: 10%[==> ] ETA: 0:00:01 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: 0:02:13 Evaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:33 Evaluating Learning curve with 3 rngs: 100%[==================] Time: 0:01:06 [ 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: 0%[> ] ETA: N/A Evaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:00:00 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: 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: 0:00:04 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 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: 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: 0:00:00 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: 0:01:28 Evaluating over 30 metamodels: 7%[=> ] ETA: 0:00:42 Evaluating over 30 metamodels: 10%[==> ] ETA: 0:00:27 Evaluating over 30 metamodels: 13%[===> ] ETA: 0:00:20 Evaluating over 30 metamodels: 17%[====> ] ETA: 0:00:17 Evaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:14 Evaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:14 Evaluating over 30 metamodels: 27%[======> ] ETA: 0:00:12 Evaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:10 Evaluating over 30 metamodels: 33%[========> ] ETA: 0:00:09 Evaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:08 Evaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:07 Evaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:06 Evaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:05 Evaluating over 30 metamodels: 50%[============> ] ETA: 0:00:04 Evaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:04 Evaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:03 Evaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:03 Evaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:03 Evaluating over 30 metamodels: 67%[================> ] ETA: 0:00:02 Evaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:02 Evaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:02 Evaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:01 Evaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:01 Evaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:01 Evaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:01 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: 0:00:04 [ 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: 0:00:01 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 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/xiLEY/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: 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: 27%[======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:00 Evaluating over 30 metamodels: 40%[==========> ] 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: 63%[===============> ] ETA: 0:00:00 Evaluating over 30 metamodels: 67%[================> ] 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: 90%[======================> ] ETA: 0:00:00 Evaluating over 30 metamodels: 93%[=======================> ] 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/xiLEY/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: 0:00:09 Evaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:02 Evaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:04 ┌ 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/xiLEY/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/xiLEY/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/xiLEY/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/xiLEY/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/xiLEY/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/xiLEY/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/xiLEY/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/xiLEY/src/learning_curves.jl:147 [ Info: No measure specified. Setting measure=LPLoss(p = 2). Test Summary: | Pass Total Time learning curves | 85 85 1m57.2s [ Info: No measure specified. Setting measure=LPLoss(p = 2). Test Summary: | Pass Total Time Serialization | 13 13 11.3s Test Summary: | Pass Total Time density estimatation | 3 3 16.5s Testing MLJTuning tests passed Testing completed after 1236.94s PkgEval succeeded after 1812.53s