Package evaluation of MLJTuning on Julia 1.11.4 (a71dd056e0*) started at 2025-04-08T18:52:44.267 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 8.56s ################################################################################ # Installation # Installing MLJTuning... Resolving package versions... Updating `~/.julia/environments/v1.11/Project.toml` [03970b2e] + MLJTuning v0.8.8 Updating `~/.julia/environments/v1.11/Manifest.toml` [7d9f7c33] + Accessors v0.1.42 [79e6a3ab] + Adapt v4.3.0 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [a9b6321e] + Atomix v1.1.1 [198e06fe] + BangBang v0.4.4 [9718e550] + Baselet v0.1.1 [324d7699] + CategoricalArrays v0.10.8 [af321ab8] + CategoricalDistributions v0.1.15 [d360d2e6] + ChainRulesCore v1.25.1 [3da002f7] + ColorTypes v0.12.1 [34da2185] + Compat v4.16.0 [a33af91c] + CompositionsBase v0.1.2 [ed09eef8] + ComputationalResources v0.3.2 [187b0558] + ConstructionBase v1.5.8 [6add18c4] + ContextVariablesX v0.1.3 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [8bb1440f] + DelimitedFiles v1.9.1 [31c24e10] + Distributions v0.25.118 [ffbed154] + DocStringExtensions v0.9.4 [cc61a311] + FLoops v0.2.2 [b9860ae5] + FLoopsBase v0.1.1 [1a297f60] + FillArrays v1.13.0 [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 v0.2.4 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.7.0 [b14d175d] + JuliaVariables v0.2.4 [63c18a36] + KernelAbstractions v0.9.34 [b964fa9f] + LaTeXStrings v1.4.0 [a5e1c1ea] + LatinHypercubeSampling v1.9.0 [92ad9a40] + LearnAPI v1.0.1 [2ab3a3ac] + LogExpFunctions v0.3.29 [c2834f40] + MLCore v1.0.0 [a7f614a8] + MLJBase v1.8.1 [e80e1ace] + MLJModelInterface v1.11.0 [03970b2e] + MLJTuning v0.8.8 [d8e11817] + MLStyle v0.4.17 [f1d291b0] + MLUtils v0.4.8 [1914dd2f] + MacroTools v0.5.15 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [872c559c] + NNlib v0.9.30 [71a1bf82] + NameResolution v0.1.5 [bac558e1] + OrderedCollections v1.8.0 [90014a1f] + PDMats v0.11.33 [d96e819e] + Parameters v0.12.3 ⌅ [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.4.3 [8162dcfd] + PrettyPrint v0.2.0 [08abe8d2] + PrettyTables v2.4.0 [92933f4c] + ProgressMeter v1.10.4 [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 v0.8.0 [321657f4] + ScientificTypes v3.1.0 [30f210dd] + ScientificTypesBase v3.0.0 [7e506255] + ScopedValues v1.3.0 [efcf1570] + Setfield v1.1.2 [605ecd9f] + ShowCases v0.1.0 [699a6c99] + SimpleTraits v0.9.4 [a2af1166] + SortingAlgorithms v1.2.1 [276daf66] + SpecialFunctions v2.5.0 [171d559e] + SplittablesBase v0.1.15 [860ef19b] + StableRNGs v1.0.2 [90137ffa] + StaticArrays v1.9.13 [1e83bf80] + StaticArraysCore v1.4.3 [c062fc1d] + StatisticalMeasuresBase v0.1.2 [64bff920] + StatisticalTraits v3.4.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.0 [2913bbd2] + StatsBase v0.34.4 [4c63d2b9] + StatsFuns v1.4.0 [892a3eda] + StringManipulation v0.4.1 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.0 [28d57a85] + Transducers v0.4.84 [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 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.11.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [de0858da] + Printf v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.11.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [4536629a] + OpenBLAS_jll v0.3.27+1 [05823500] + OpenLibm_jll v0.8.5+0 [bea87d4a] + SuiteSparse_jll v7.7.0+0 [8e850b90] + libblastrampoline_jll v5.11.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Installation completed after 4.57s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 39.31s ################################################################################ # Testing # Testing MLJTuning Status `/tmp/jl_G2IpBZ/Project.toml` [324d7699] CategoricalArrays v0.10.8 [ed09eef8] ComputationalResources v0.3.2 [7806a523] DecisionTree v0.12.4 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.118 [a5e1c1ea] LatinHypercubeSampling v1.9.0 [a7f614a8] MLJBase v1.8.1 [e80e1ace] MLJModelInterface v1.11.0 [03970b2e] MLJTuning v0.8.8 [6f286f6a] MultivariateStats v0.10.3 [b8a86587] NearestNeighbors v0.4.21 [92933f4c] ProgressMeter v1.10.4 [3cdcf5f2] RecipesBase v1.3.4 [321657f4] ScientificTypes v3.1.0 [860ef19b] StableRNGs v1.0.2 [a19d573c] StatisticalMeasures v0.2.1 [c062fc1d] StatisticalMeasuresBase v0.1.2 [10745b16] Statistics v1.11.1 [2913bbd2] StatsBase v0.34.4 [bd369af6] Tables v1.12.0 [8ba89e20] Distributed v1.11.0 [37e2e46d] LinearAlgebra v1.11.0 [9a3f8284] Random v1.11.0 [9e88b42a] Serialization v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_G2IpBZ/Manifest.toml` [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.42 [79e6a3ab] Adapt v4.3.0 [66dad0bd] AliasTables v1.1.3 [dce04be8] ArgCheck v2.5.0 [7d9fca2a] Arpack v0.5.4 [a9b6321e] Atomix v1.1.1 [198e06fe] BangBang v0.4.4 [9718e550] Baselet v0.1.1 [324d7699] CategoricalArrays v0.10.8 [af321ab8] CategoricalDistributions v0.1.15 [d360d2e6] ChainRulesCore v1.25.1 [3da002f7] ColorTypes v0.12.1 [34da2185] Compat v4.16.0 [a33af91c] CompositionsBase v0.1.2 [ed09eef8] ComputationalResources v0.3.2 [187b0558] ConstructionBase v1.5.8 [6add18c4] ContextVariablesX v0.1.3 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [7806a523] DecisionTree v0.12.4 [244e2a9f] DefineSingletons v0.1.2 [8bb1440f] DelimitedFiles v1.9.1 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.118 [ffbed154] DocStringExtensions v0.9.4 [cc61a311] FLoops v0.2.2 [b9860ae5] FLoopsBase v0.1.1 [1a297f60] FillArrays v1.13.0 [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 v0.2.4 [82899510] IteratorInterfaceExtensions v1.0.0 [692b3bcd] JLLWrappers v1.7.0 [b14d175d] JuliaVariables v0.2.4 [63c18a36] KernelAbstractions v0.9.34 [b964fa9f] LaTeXStrings v1.4.0 [a5e1c1ea] LatinHypercubeSampling v1.9.0 [92ad9a40] LearnAPI v1.0.1 [2ab3a3ac] LogExpFunctions v0.3.29 [c2834f40] MLCore v1.0.0 [a7f614a8] MLJBase v1.8.1 [e80e1ace] MLJModelInterface v1.11.0 [03970b2e] MLJTuning v0.8.8 [d8e11817] MLStyle v0.4.17 [f1d291b0] MLUtils v0.4.8 [1914dd2f] MacroTools v0.5.15 [128add7d] MicroCollections v0.2.0 [e1d29d7a] Missings v1.2.0 [6f286f6a] MultivariateStats v0.10.3 [872c559c] NNlib v0.9.30 [71a1bf82] NameResolution v0.1.5 [b8a86587] NearestNeighbors v0.4.21 [bac558e1] OrderedCollections v1.8.0 [90014a1f] PDMats v0.11.33 [d96e819e] Parameters v0.12.3 ⌅ [aea7be01] PrecompileTools v1.2.1 [21216c6a] Preferences v1.4.3 [8162dcfd] PrettyPrint v0.2.0 [08abe8d2] PrettyTables v2.4.0 [92933f4c] ProgressMeter v1.10.4 [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 v0.8.0 [321657f4] ScientificTypes v3.1.0 [30f210dd] ScientificTypesBase v3.0.0 [6e75b9c4] ScikitLearnBase v0.5.0 [7e506255] ScopedValues v1.3.0 [efcf1570] Setfield v1.1.2 [605ecd9f] ShowCases v0.1.0 [699a6c99] SimpleTraits v0.9.4 [a2af1166] SortingAlgorithms v1.2.1 [276daf66] SpecialFunctions v2.5.0 [171d559e] SplittablesBase v0.1.15 [860ef19b] StableRNGs v1.0.2 [90137ffa] StaticArrays v1.9.13 [1e83bf80] StaticArraysCore v1.4.3 [a19d573c] StatisticalMeasures v0.2.1 [c062fc1d] StatisticalMeasuresBase v0.1.2 [64bff920] StatisticalTraits v3.4.0 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.7.0 [2913bbd2] StatsBase v0.34.4 [4c63d2b9] StatsFuns v1.4.0 [892a3eda] StringManipulation v0.4.1 [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.0 [28d57a85] Transducers v0.4.84 [3a884ed6] UnPack v1.0.2 [013be700] UnsafeAtomics v0.3.0 ⌅ [68821587] Arpack_jll v3.5.1+1 [efe28fd5] OpenSpecFun_jll v0.5.6+0 [f50d1b31] Rmath_jll v0.5.1+0 [0dad84c5] ArgTools v1.1.2 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 v1.11.0 [ade2ca70] Dates v1.11.0 [8ba89e20] Distributed v1.11.0 [f43a241f] Downloads v1.6.0 [7b1f6079] FileWatching v1.11.0 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.11.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.2.0 [44cfe95a] Pkg v1.11.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.11.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] LibCURL_jll v8.6.0+0 [e37daf67] LibGit2_jll v1.7.2+0 [29816b5a] LibSSH2_jll v1.11.0+1 [c8ffd9c3] MbedTLS_jll v2.28.6+0 [14a3606d] MozillaCACerts_jll v2023.12.12 [4536629a] OpenBLAS_jll v0.3.27+1 [05823500] OpenLibm_jll v0.8.5+0 [bea87d4a] SuiteSparse_jll v7.7.0+0 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.59.0+0 [3f19e933] p7zip_jll v17.4.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... Precompiling DefaultMeasuresExt... 9769.2 ms ✓ MLJBase → DefaultMeasuresExt 1 dependency successfully precompiled in 18 seconds. 145 already precompiled. [ Info: nworkers: 2 [ Info: nthreads: 1 Loading some models for testing... WARNING: replacing module Models. Test Summary: | Pass Total Time utilities | 4 4 2.8s Test Summary: | Pass Total Time selection heuristics | 7 7 2.0s Testing progressmeter basic fit with CPU1{Nothing}(nothing) and CPU1 resampling [ Info: Attempting to evaluate 12 models. measurement: 1.903643502106285 measurement: 1.8313682528233075 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:19measurement: 1.725824054837585 measurement: 1.5876920544899495 measurement: 1.461278306396784 measurement: 1.3224538242874866 measurement: 1.2736828159099107 measurement: 1.1333245517941333 measurement: 1.050032852142519 measurement: 0.9515984846885978 measurement: 0.9657853181057472 measurement: 0.979226007963803 Evaluating over 12 metamodels: 100%[=========================] Time: 0:00:04 [ Info: Training machine(KNNRegressor(K = 4, …), …). Testing progressmeter basic fit with CPUProcesses{Nothing}(nothing) and CPU1 resampling [ Info: Attempting to evaluate 12 models. Evaluating over 12 metamodels: 17%[====> ] ETA: 0:05:46 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:03:29 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:02:19 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:01:38 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:01:10 Evaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:50 Evaluating over 12 metamodels: 67%[================> ] ETA: 0:00:35 Evaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:23 Evaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:14 Evaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:06 Evaluating over 12 metamodels: 100%[=========================] Time: 0:01:10 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: 0:00:20 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:09 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:06 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:04 Evaluating over 12 metamodels: 50%[============> ] 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: 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: 0%[> ] ETA: N/A Evaluating over 12 metamodels: 8%[==> ] ETA: 0:00:06 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:03 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:02 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:01 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:01 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:00:01 Evaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:00 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 CPUProcesses resampling [ Info: Attempting to evaluate 12 models. Evaluating over 12 metamodels: 0%[> ] ETA: N/A Evaluating over 12 metamodels: 8%[==> ] ETA: 0:00:00 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:00 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:00 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:00 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 12 metamodels: 58%[==============> ] 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: 0:01:34 Evaluating over 12 metamodels: 17%[====> ] ETA: 0:00:43 Evaluating over 12 metamodels: 25%[======> ] ETA: 0:00:27 Evaluating over 12 metamodels: 33%[========> ] ETA: 0:00:18 Evaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:12 Evaluating over 12 metamodels: 50%[============> ] ETA: 0:00:09 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: 100%[=========================] Time: 0:00:08 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: 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: 83%[====================> ] ETA: 0:00:00 Evaluating over 12 metamodels: 92%[======================> ] 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 8m02.7s Test Summary: | Pass Total Time range_methods | 33 33 11.7s [ 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: 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: 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 1m38.7s [ 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: 0:00:55 Evaluating over 1000 metamodels: 0%[> ] ETA: 0:00:22 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:16 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:12 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:10 Evaluating over 1000 metamodels: 1%[> ] ETA: 0:00:09 Evaluating over 1000 metamodels: 2%[> ] ETA: 0:00:08 Evaluating over 1000 metamodels: 2%[> ] ETA: 0:00:07 Evaluating over 1000 metamodels: 2%[> ] ETA: 0:00:06 Evaluating over 1000 metamodels: 2%[> ] ETA: 0:00:05 Evaluating over 1000 metamodels: 2%[> ] ETA: 0:00:05 Evaluating over 1000 metamodels: 2%[> ] ETA: 0:00:05 Evaluating over 1000 metamodels: 2%[> ] ETA: 0:00:05 Evaluating over 1000 metamodels: 3%[> ] ETA: 0:00:04 Evaluating over 1000 metamodels: 3%[> ] ETA: 0:00:04 Evaluating over 1000 metamodels: 3%[> ] ETA: 0:00:04 Evaluating over 1000 metamodels: 3%[> ] ETA: 0:00:04 Evaluating over 1000 metamodels: 3%[> ] ETA: 0:00:03 Evaluating over 1000 metamodels: 4%[> ] ETA: 0:00:03 Evaluating over 1000 metamodels: 4%[> ] ETA: 0:00:03 Evaluating over 1000 metamodels: 4%[> ] ETA: 0:00:03 Evaluating over 1000 metamodels: 4%[=> ] ETA: 0:00:03 Evaluating over 1000 metamodels: 4%[=> ] ETA: 0:00:03 Evaluating over 1000 metamodels: 4%[=> ] ETA: 0:00:03 Evaluating over 1000 metamodels: 4%[=> ] ETA: 0:00:03 Evaluating over 1000 metamodels: 5%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 5%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 5%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 5%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 6%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 6%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 6%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 6%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 6%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 6%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 6%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 7%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 7%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 7%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 7%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 7%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 7%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 8%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 8%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 8%[=> ] ETA: 0:00:02 Evaluating over 1000 metamodels: 8%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 8%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 8%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 9%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 9%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 9%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 9%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 9%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 11%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 11%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 11%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 11%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 11%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 11%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 12%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 12%[==> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 12%[===> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 12%[===> ] ETA: 0:00:01 Evaluating over 1000 metamodels: 12%[===> ] ETA: 0:00:01 Evaluating over 1000 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metamodels: 48%[===========> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 48%[===========> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 48%[============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 48%[============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 48%[============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 49%[============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 49%[============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 49%[============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 50%[============> ] ETA: 0:00:00 Evaluating over 1000 metamodels: 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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: 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: 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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: 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: 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: 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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: 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 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] 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: 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: 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: 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: 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: 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: 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: 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: 100%[=========================] Time: 0:00:00 Test Summary: | Pass Total Time random search | 19 19 16.0s ┌ Info: Only 19 (of 100) models evaluated. └ Model supply exhausted. Test Summary: | Pass Total Time Latin hypercube | 28 28 44.9s Test Summary: | Pass Total Time Explicit | 17 17 1m25.4s 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: 0:02:16 Evaluating over 30 metamodels: 7%[=> ] ETA: 0:01:10 Evaluating over 30 metamodels: 10%[==> ] ETA: 0:00:45 Evaluating over 30 metamodels: 13%[===> ] ETA: 0:00:32 Evaluating over 30 metamodels: 17%[====> ] ETA: 0:00:25 Evaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:20 Evaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:16 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: 0:00:07 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: 0:00:02 Evaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:02 Evaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:02 Evaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:02 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: 0:00:05 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: 0:00:00 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: 0:02:18 Evaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:34 Evaluating Learning curve with 3 rngs: 100%[==================] Time: 0:01:09 [ 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: 0:00:03 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: 0:01:26 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: 0:00:16 Evaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:13 Evaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:12 Evaluating over 30 metamodels: 27%[======> ] ETA: 0:00:10 Evaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:09 Evaluating over 30 metamodels: 33%[========> ] ETA: 0:00:08 Evaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:07 Evaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:06 Evaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:05 Evaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:04 Evaluating over 30 metamodels: 50%[============> ] ETA: 0:00:04 Evaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:03 Evaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:03 Evaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:03 Evaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:02 Evaluating over 30 metamodels: 67%[================> ] ETA: 0:00:02 Evaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:02 Evaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:01 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: 0:00:03 [ 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 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: 40%[==========> ] ETA: 0:00:00 Evaluating over 30 metamodels: 43%[==========> ] 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: 70%[=================> ] 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: 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: 67%[============> ] 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 1m52.0s [ Info: No measure specified. Setting measure=LPLoss(p = 2). Test Summary: | Pass Total Time Serialization | 13 13 8.4s Testing MLJTuning tests passed Testing completed after 593.56s PkgEval succeeded after 1070.1s