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1.5.2-pre-be8475f41a.log
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Julia Version 1.5.2-pre.25
Commit be8475f41a (2020-09-09 06:41 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-9.0.1 (ORCJIT, skylake-avx512)
Environment:
JULIA_DEPOT_PATH = ::/usr/local/share/julia
JULIA_NUM_THREADS = 2
Resolving package versions...
Installed DataValueInterfaces ────────── v1.0.0
Installed LearnBase ──────────────────── v0.4.1
Installed UnPack ─────────────────────── v1.0.2
Installed DataAPI ────────────────────── v1.3.0
Installed FilePathsBase ──────────────── v0.9.5
Installed MLJTuning ──────────────────── v0.4.3
Installed HTTP ───────────────────────── v0.8.17
Installed JLSO ───────────────────────── v2.3.3
Installed Rmath_jll ──────────────────── v0.2.2+1
Installed SortingAlgorithms ──────────── v0.3.1
Installed Zlib_jll ───────────────────── v1.2.11+16
Installed Parameters ─────────────────── v0.12.1
Installed CompilerSupportLibraries_jll ─ v0.3.3+0
Installed MbedTLS ────────────────────── v1.0.2
Installed StatsBase ──────────────────── v0.33.1
Installed Parsers ────────────────────── v1.0.10
Installed TranscodingStreams ─────────── v0.9.5
Installed Rmath ──────────────────────── v0.6.1
Installed MLJScientificTypes ─────────── v0.3.0
Installed Mocking ────────────────────── v0.7.1
Installed ScientificTypes ────────────── v1.0.0
Installed FillArrays ─────────────────── v0.9.6
Installed ComputationalResources ─────── v0.3.2
Installed TableTraits ────────────────── v1.0.0
Installed OpenSpecFun_jll ────────────── v0.5.3+3
Installed TimeZones ──────────────────── v1.3.2
Installed LossFunctions ──────────────── v0.6.2
Installed CodecZlib ──────────────────── v0.7.0
Installed Libiconv_jll ───────────────── v1.16.0+6
Installed Crayons ────────────────────── v4.0.4
Installed MLJModelInterface ──────────── v0.3.5
Installed Compat ─────────────────────── v3.15.0
Installed SpecialFunctions ───────────── v0.10.3
Installed MLJBase ────────────────────── v0.15.2
Installed IteratorInterfaceExtensions ── v1.0.0
Installed OrderedCollections ─────────── v1.3.0
Installed EzXML ──────────────────────── v1.1.0
Installed JSON ───────────────────────── v0.21.1
Installed Formatting ─────────────────── v0.4.1
Installed PDMats ─────────────────────── v0.10.0
Installed FixedPointNumbers ──────────── v0.8.4
Installed InvertedIndices ────────────── v1.0.0
Installed PrettyTables ───────────────── v0.9.1
Installed Memento ────────────────────── v1.1.1
Installed QuadGK ─────────────────────── v2.4.1
Installed Missings ───────────────────── v0.4.4
Installed BSON ───────────────────────── v0.2.6
Installed ColorTypes ─────────────────── v0.10.9
Installed Tables ─────────────────────── v1.0.5
Installed Syslogs ────────────────────── v0.3.0
Installed StructTypes ────────────────── v1.1.0
Installed CategoricalArrays ──────────── v0.8.2
Installed StatsFuns ──────────────────── v0.9.5
Installed RecipesBase ────────────────── v1.0.2
Installed ProgressMeter ──────────────── v1.3.2
Installed ExprTools ──────────────────── v0.1.2
Installed MbedTLS_jll ────────────────── v2.16.8+0
Installed Reexport ───────────────────── v0.2.0
Installed IniFile ────────────────────── v0.5.0
Installed DataStructures ─────────────── v0.18.4
Installed XML2_jll ───────────────────── v2.9.10+2
Installed Distributions ──────────────── v0.23.11
Updating `~/.julia/environments/v1.5/Project.toml`
[03970b2e] + MLJTuning v0.4.3
Updating `~/.julia/environments/v1.5/Manifest.toml`
[fbb218c0] + BSON v0.2.6
[324d7699] + CategoricalArrays v0.8.2
[944b1d66] + CodecZlib v0.7.0
[3da002f7] + ColorTypes v0.10.9
[34da2185] + Compat v3.15.0
[e66e0078] + CompilerSupportLibraries_jll v0.3.3+0
[ed09eef8] + ComputationalResources v0.3.2
[a8cc5b0e] + Crayons v4.0.4
[9a962f9c] + DataAPI v1.3.0
[864edb3b] + DataStructures v0.18.4
[e2d170a0] + DataValueInterfaces v1.0.0
[31c24e10] + Distributions v0.23.11
[e2ba6199] + ExprTools v0.1.2
[8f5d6c58] + EzXML v1.1.0
[48062228] + FilePathsBase v0.9.5
[1a297f60] + FillArrays v0.9.6
[53c48c17] + FixedPointNumbers v0.8.4
[59287772] + Formatting v0.4.1
[cd3eb016] + HTTP v0.8.17
[83e8ac13] + IniFile v0.5.0
[41ab1584] + InvertedIndices v1.0.0
[82899510] + IteratorInterfaceExtensions v1.0.0
[9da8a3cd] + JLSO v2.3.3
[682c06a0] + JSON v0.21.1
[7f8f8fb0] + LearnBase v0.4.1
[94ce4f54] + Libiconv_jll v1.16.0+6
[30fc2ffe] + LossFunctions v0.6.2
[a7f614a8] + MLJBase v0.15.2
[e80e1ace] + MLJModelInterface v0.3.5
[2e2323e0] + MLJScientificTypes v0.3.0
[03970b2e] + MLJTuning v0.4.3
[739be429] + MbedTLS v1.0.2
[c8ffd9c3] + MbedTLS_jll v2.16.8+0
[f28f55f0] + Memento v1.1.1
[e1d29d7a] + Missings v0.4.4
[78c3b35d] + Mocking v0.7.1
[efe28fd5] + OpenSpecFun_jll v0.5.3+3
[bac558e1] + OrderedCollections v1.3.0
[90014a1f] + PDMats v0.10.0
[d96e819e] + Parameters v0.12.1
[69de0a69] + Parsers v1.0.10
[08abe8d2] + PrettyTables v0.9.1
[92933f4c] + ProgressMeter v1.3.2
[1fd47b50] + QuadGK v2.4.1
[3cdcf5f2] + RecipesBase v1.0.2
[189a3867] + Reexport v0.2.0
[79098fc4] + Rmath v0.6.1
[f50d1b31] + Rmath_jll v0.2.2+1
[321657f4] + ScientificTypes v1.0.0
[a2af1166] + SortingAlgorithms v0.3.1
[276daf66] + SpecialFunctions v0.10.3
[2913bbd2] + StatsBase v0.33.1
[4c63d2b9] + StatsFuns v0.9.5
[856f2bd8] + StructTypes v1.1.0
[cea106d9] + Syslogs v0.3.0
[3783bdb8] + TableTraits v1.0.0
[bd369af6] + Tables v1.0.5
[f269a46b] + TimeZones v1.3.2
[3bb67fe8] + TranscodingStreams v0.9.5
[3a884ed6] + UnPack v1.0.2
[02c8fc9c] + XML2_jll v2.9.10+2
[83775a58] + Zlib_jll v1.2.11+16
[2a0f44e3] + Base64
[ade2ca70] + Dates
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[9fa8497b] + Future
[b77e0a4c] + InteractiveUtils
[76f85450] + LibGit2
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[44cfe95a] + Pkg
[de0858da] + Printf
[3fa0cd96] + REPL
[9a3f8284] + Random
[ea8e919c] + SHA
[9e88b42a] + Serialization
[1a1011a3] + SharedArrays
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[4607b0f0] + SuiteSparse
[8dfed614] + Test
[cf7118a7] + UUIDs
[4ec0a83e] + Unicode
Building TimeZones → `~/.julia/packages/TimeZones/v0mfN/deps/build.log`
Testing MLJTuning
Status `/tmp/jl_NdT1GR/Project.toml`
[324d7699] CategoricalArrays v0.8.2
[ed09eef8] ComputationalResources v0.3.2
[7806a523] DecisionTree v0.10.9
[b4f34e82] Distances v0.9.0
[31c24e10] Distributions v0.23.11
[a7f614a8] MLJBase v0.15.2
[e80e1ace] MLJModelInterface v0.3.5
[2e2323e0] MLJScientificTypes v0.3.0
[03970b2e] MLJTuning v0.4.3
[6f286f6a] MultivariateStats v0.7.0
[b8a86587] NearestNeighbors v0.4.6
[92933f4c] ProgressMeter v1.3.2
[3cdcf5f2] RecipesBase v1.0.2
[860ef19b] StableRNGs v0.1.1
[2913bbd2] StatsBase v0.33.1
[bd369af6] Tables v1.0.5
[8ba89e20] Distributed
[37e2e46d] LinearAlgebra
[9a3f8284] Random
[10745b16] Statistics
[8dfed614] Test
Status `/tmp/jl_NdT1GR/Manifest.toml`
[7d9fca2a] Arpack v0.4.0
[68821587] Arpack_jll v3.5.0+3
[fbb218c0] BSON v0.2.6
[324d7699] CategoricalArrays v0.8.2
[944b1d66] CodecZlib v0.7.0
[3da002f7] ColorTypes v0.10.9
[34da2185] Compat v3.15.0
[e66e0078] CompilerSupportLibraries_jll v0.3.3+0
[ed09eef8] ComputationalResources v0.3.2
[a8cc5b0e] Crayons v4.0.4
[9a962f9c] DataAPI v1.3.0
[864edb3b] DataStructures v0.18.4
[e2d170a0] DataValueInterfaces v1.0.0
[7806a523] DecisionTree v0.10.9
[b4f34e82] Distances v0.9.0
[31c24e10] Distributions v0.23.11
[e2ba6199] ExprTools v0.1.2
[8f5d6c58] EzXML v1.1.0
[48062228] FilePathsBase v0.9.5
[1a297f60] FillArrays v0.9.6
[53c48c17] FixedPointNumbers v0.8.4
[59287772] Formatting v0.4.1
[cd3eb016] HTTP v0.8.17
[83e8ac13] IniFile v0.5.0
[41ab1584] InvertedIndices v1.0.0
[82899510] IteratorInterfaceExtensions v1.0.0
[9da8a3cd] JLSO v2.3.3
[682c06a0] JSON v0.21.1
[7f8f8fb0] LearnBase v0.4.1
[94ce4f54] Libiconv_jll v1.16.0+6
[30fc2ffe] LossFunctions v0.6.2
[a7f614a8] MLJBase v0.15.2
[e80e1ace] MLJModelInterface v0.3.5
[2e2323e0] MLJScientificTypes v0.3.0
[03970b2e] MLJTuning v0.4.3
[739be429] MbedTLS v1.0.2
[c8ffd9c3] MbedTLS_jll v2.16.8+0
[f28f55f0] Memento v1.1.1
[e1d29d7a] Missings v0.4.4
[78c3b35d] Mocking v0.7.1
[6f286f6a] MultivariateStats v0.7.0
[b8a86587] NearestNeighbors v0.4.6
[4536629a] OpenBLAS_jll v0.3.10+0
[efe28fd5] OpenSpecFun_jll v0.5.3+3
[bac558e1] OrderedCollections v1.3.0
[90014a1f] PDMats v0.10.0
[d96e819e] Parameters v0.12.1
[69de0a69] Parsers v1.0.10
[08abe8d2] PrettyTables v0.9.1
[92933f4c] ProgressMeter v1.3.2
[1fd47b50] QuadGK v2.4.1
[3cdcf5f2] RecipesBase v1.0.2
[189a3867] Reexport v0.2.0
[79098fc4] Rmath v0.6.1
[f50d1b31] Rmath_jll v0.2.2+1
[321657f4] ScientificTypes v1.0.0
[6e75b9c4] ScikitLearnBase v0.5.0
[a2af1166] SortingAlgorithms v0.3.1
[276daf66] SpecialFunctions v0.10.3
[860ef19b] StableRNGs v0.1.1
[90137ffa] StaticArrays v0.12.4
[2913bbd2] StatsBase v0.33.1
[4c63d2b9] StatsFuns v0.9.5
[856f2bd8] StructTypes v1.1.0
[cea106d9] Syslogs v0.3.0
[3783bdb8] TableTraits v1.0.0
[bd369af6] Tables v1.0.5
[f269a46b] TimeZones v1.3.2
[3bb67fe8] TranscodingStreams v0.9.5
[3a884ed6] UnPack v1.0.2
[02c8fc9c] XML2_jll v2.9.10+2
[83775a58] Zlib_jll v1.2.11+16
[2a0f44e3] Base64
[ade2ca70] Dates
[8bb1440f] DelimitedFiles
[8ba89e20] Distributed
[9fa8497b] Future
[b77e0a4c] InteractiveUtils
[76f85450] LibGit2
[8f399da3] Libdl
[37e2e46d] LinearAlgebra
[56ddb016] Logging
[d6f4376e] Markdown
[a63ad114] Mmap
[44cfe95a] Pkg
[de0858da] Printf
[3fa0cd96] REPL
[9a3f8284] Random
[ea8e919c] SHA
[9e88b42a] Serialization
[1a1011a3] SharedArrays
[6462fe0b] Sockets
[2f01184e] SparseArrays
[10745b16] Statistics
[4607b0f0] SuiteSparse
[8dfed614] Test
[cf7118a7] UUIDs
[4ec0a83e] Unicode
WARNING: Method definition (::Type{Base.OrderStyle})(Type{Union{}}) in module Base at traits.jl:12 overwritten in module CategoricalArrays at /home/pkgeval/.julia/packages/CategoricalArrays/hxUIH/src/CategoricalArrays.jl:19.
** incremental compilation may be fatally broken for this module **
WARNING: Method definition (::Type{Base.OrderStyle})(Type{Union{}}) in module Base at traits.jl:12 overwritten in module CategoricalArrays at /home/pkgeval/.julia/packages/CategoricalArrays/hxUIH/src/CategoricalArrays.jl:19.
** incremental compilation may be fatally broken for this module **
WARNING: Method definition (::Type{Base.OrderStyle})(Type{Union{}}) in module Base at traits.jl:12 overwritten in module CategoricalArrays at /home/pkgeval/.julia/packages/CategoricalArrays/hxUIH/src/CategoricalArrays.jl:19.
** incremental compilation may be fatally broken for this module **
WARNING: Method definition (::Type{Base.OrderStyle})(Type{Union{}}) in module Base at traits.jl:12 overwritten in module CategoricalArrays at /home/pkgeval/.julia/packages/CategoricalArrays/hxUIH/src/CategoricalArrays.jl:19.
** incremental compilation may be fatally broken for this module **
[ Info: nworkers: 2
[ Info: nthreads: 2
Loading some models for testing... WARNING: replacing module Models.
Test Summary: | Pass Total
utilities | 2 2
┌ Error: Problem fitting the machine [34mMachine{Resampler{CV,…}} @243[39m, possibly because an upstream node in a learning network is providing data of incompatible scitype.
└ @ MLJBase ~/.julia/packages/MLJBase/uKzAz/src/machines.jl:422
[ Info: Running type checks...
[ Info: Type checks okay.
Testing progressmeter basic fit with CPU1{Nothing}(nothing) and CPU1 resampling
[ Info: Attempting to evaluate 12 models.
Evaluating over 12 metamodels: 0%[> ] ETA: N/A[KEvaluating over 12 metamodels: 8%[==> ] ETA: 0:00:36[KEvaluating over 12 metamodels: 17%[====> ] ETA: 0:00:18[KEvaluating over 12 metamodels: 25%[======> ] ETA: 0:00:11[KEvaluating over 12 metamodels: 67%[================> ] ETA: 0:00:02[KEvaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:01[KEvaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:01[KEvaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 100%[=========================] Time: 0:00:03[K
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[KEvaluating over 12 metamodels: 8%[==> ] ETA: 0:06:18[KEvaluating over 12 metamodels: 17%[====> ] ETA: 0:02:52[KEvaluating over 12 metamodels: 25%[======> ] ETA: 0:01:45[KEvaluating over 12 metamodels: 33%[========> ] ETA: 0:01:10[KEvaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:49[KEvaluating over 12 metamodels: 50%[============> ] ETA: 0:00:35[KEvaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:25[KEvaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:12[KEvaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:07[KEvaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:03[KEvaluating over 12 metamodels: 100%[=========================] Time: 0:00:35[K
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[KEvaluating over 12 metamodels: 8%[==> ] ETA: 0:00:05[KEvaluating over 12 metamodels: 33%[========> ] ETA: 0:00:01[KEvaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 100%[=========================] Time: 0:00:00[K
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[KEvaluating over 12 metamodels: 8%[==> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 100%[=========================] Time: 0:00:00[K
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[KEvaluating over 12 metamodels: 8%[==> ] ETA: 0:00:22[KEvaluating over 12 metamodels: 17%[====> ] ETA: 0:00:10[KEvaluating over 12 metamodels: 25%[======> ] ETA: 0:00:06[KEvaluating over 12 metamodels: 33%[========> ] ETA: 0:00:04[KEvaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:03[KEvaluating over 12 metamodels: 50%[============> ] ETA: 0:00:02[KEvaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:01[KEvaluating over 12 metamodels: 67%[================> ] ETA: 0:00:01[KEvaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:01[KEvaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 100%[=========================] Time: 0:00:02[K
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[KEvaluating over 12 metamodels: 8%[==> ] ETA: 0:00:12[KEvaluating over 12 metamodels: 100%[=========================] Time: 0:00:01[K
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[KEvaluating over 12 metamodels: 8%[==> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 100%[=========================] Time: 0:00:00[K
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[KEvaluating over 12 metamodels: 8%[==> ] ETA: 0:01:06[KEvaluating over 12 metamodels: 17%[====> ] ETA: 0:00:31[KEvaluating over 12 metamodels: 25%[======> ] ETA: 0:00:18[KEvaluating over 12 metamodels: 33%[========> ] ETA: 0:00:12[KEvaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:09[KEvaluating over 12 metamodels: 50%[============> ] ETA: 0:00:06[KEvaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:04[KEvaluating over 12 metamodels: 67%[================> ] ETA: 0:00:03[KEvaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:02[KEvaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:01[KEvaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:01[KEvaluating over 12 metamodels: 100%[=========================] Time: 0:00:06[K
Testing progressmeter basic fit with CPUThreads{Nothing}(nothing) and CPUProcesses resampling
[ Info: Attempting to evaluate 12 models.
Evaluating over 12 metamodels: 0%[> ] ETA: N/A[KEvaluating over 12 metamodels: 8%[==> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 100%[=========================] Time: 0:00:00[K
[ Info: Training [34mMachine{DeterministicTunedModel{Explicit,…}} @998[39m.
[ Info: Attempting to evaluate 4 models.
Evaluating over 4 metamodels: 0%[> ] ETA: N/A[KEvaluating over 4 metamodels: 25%[======> ] ETA: 0:00:00[KEvaluating over 4 metamodels: 50%[============> ] ETA: 0:00:00[KEvaluating over 4 metamodels: 75%[==================> ] ETA: 0:00:00[KEvaluating over 4 metamodels: 100%[=========================] Time: 0:00:00[K
[ Info: Training [34mMachine{DeterministicTunedModel{Explicit,…}} @446[39m.
[ Info: Attempting to evaluate 4 models.
Evaluating over 4 metamodels: 0%[> ] ETA: N/A[KEvaluating over 4 metamodels: 25%[======> ] ETA: 0:00:00[KEvaluating over 4 metamodels: 100%[=========================] Time: 0:00:00[K
[ Info: Training [34mMachine{DeterministicTunedModel{Explicit,…}} @434[39m.
[ Info: Attempting to evaluate 4 models.
Evaluating over 4 metamodels: 0%[> ] ETA: N/A[KEvaluating over 4 metamodels: 25%[======> ] ETA: 0:00:00[KEvaluating over 4 metamodels: 100%[=========================] Time: 0:00:00[K
Test Summary: | Pass Total
tuned_models.jl | 63 63
Test Summary: | Pass Total
range_methods | 33 33
[ Info: Training [34mMachine{ProbabilisticTunedModel{Grid,…}} @500[39m.
[ Info: Attempting to evaluate 12 models.
Evaluating over 12 metamodels: 0%[> ] ETA: N/A[KEvaluating over 12 metamodels: 8%[==> ] ETA: 0:00:24[KEvaluating over 12 metamodels: 17%[====> ] ETA: 0:00:12[KEvaluating over 12 metamodels: 25%[======> ] ETA: 0:00:07[KEvaluating over 12 metamodels: 33%[========> ] ETA: 0:00:05[KEvaluating over 12 metamodels: 42%[==========> ] ETA: 0:00:03[KEvaluating over 12 metamodels: 50%[============> ] ETA: 0:00:02[KEvaluating over 12 metamodels: 58%[==============> ] ETA: 0:00:02[KEvaluating over 12 metamodels: 67%[================> ] ETA: 0:00:01[KEvaluating over 12 metamodels: 75%[==================> ] ETA: 0:00:01[KEvaluating over 12 metamodels: 83%[====================> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 92%[======================> ] ETA: 0:00:00[KEvaluating over 12 metamodels: 100%[=========================] Time: 0:00:02[K
Test Summary: | Pass Total
grid | 31 31
[ Info: Training [34mMachine{DeterministicTunedModel{RandomSearch,…}} @748[39m.
[ Info: Attempting to evaluate 1000 models.
Evaluating over 1000 metamodels: 0%[> ] ETA: N/A[KEvaluating over 1000 metamodels: 0%[> ] ETA: 0:00:36[KEvaluating over 1000 metamodels: 0%[> ] ETA: 0:01:01[KEvaluating over 1000 metamodels: 0%[> ] ETA: 0:00:41[KEvaluating over 1000 metamodels: 0%[> ] ETA: 0:00:31[KEvaluating over 1000 metamodels: 0%[> ] ETA: 0:00:25[KEvaluating over 1000 metamodels: 1%[> ] ETA: 0:00:21[KEvaluating over 1000 metamodels: 1%[> ] ETA: 0:00:18[KEvaluating over 1000 metamodels: 1%[> ] ETA: 0:00:16[KEvaluating over 1000 metamodels: 1%[> ] ETA: 0:00:14[KEvaluating over 1000 metamodels: 1%[> ] ETA: 0:00:13[KEvaluating over 1000 metamodels: 1%[> ] ETA: 0:00:12[KEvaluating over 1000 metamodels: 1%[> ] ETA: 0:00:11[KEvaluating over 1000 metamodels: 1%[> ] ETA: 0:00:10[KEvaluating over 1000 metamodels: 1%[> ] ETA: 0:00:09[KEvaluating over 1000 metamodels: 2%[> ] ETA: 0:00:09[KEvaluating over 1000 metamodels: 2%[> ] ETA: 0:00:08[KEvaluating over 1000 metamodels: 2%[> ] ETA: 0:00:08[KEvaluating over 1000 metamodels: 2%[> ] ETA: 0:00:07[KEvaluating over 1000 metamodels: 2%[> ] ETA: 0:00:06[KEvaluating over 1000 metamodels: 2%[> ] ETA: 0:00:06[KEvaluating over 1000 metamodels: 2%[> ] ETA: 0:00:06[KEvaluating over 1000 metamodels: 2%[> ] ETA: 0:00:06[KEvaluating over 1000 metamodels: 2%[> ] ETA: 0:00:05[KEvaluating over 1000 metamodels: 3%[> ] ETA: 0:00:05[KEvaluating over 1000 metamodels: 3%[> ] ETA: 0:00:05[KEvaluating over 1000 metamodels: 3%[> ] ETA: 0:00:05[KEvaluating over 1000 metamodels: 3%[> ] ETA: 0:00:05[KEvaluating over 1000 metamodels: 3%[> ] ETA: 0:00:05[KEvaluating over 1000 metamodels: 3%[> ] ETA: 0:00:04[KEvaluating over 1000 metamodels: 3%[> ] ETA: 0:00:04[KEvaluating over 1000 metamodels: 3%[> ] ETA: 0:00:04[KEvaluating over 1000 metamodels: 3%[> ] ETA: 0:00:04[KEvaluating over 1000 metamodels: 4%[> ] ETA: 0:00:04[KEvaluating over 1000 metamodels: 4%[> ] ETA: 0:00:04[KEvaluating over 1000 metamodels: 4%[> ] ETA: 0:00:04[KEvaluating over 1000 metamodels: 4%[> ] ETA: 0:00:04[KEvaluating over 1000 metamodels: 4%[> ] ETA: 0:00:04[KEvaluating over 1000 metamodels: 4%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 4%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 4%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 4%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 4%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 4%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 5%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 5%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 5%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 5%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 5%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 5%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 5%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 5%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 6%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 6%[=> ] ETA: 0:00:03[KEvaluating over 1000 metamodels: 6%[=> ] ETA: 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0:00:02[KEvaluating over 1000 metamodels: 8%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 8%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 8%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 8%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 9%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 9%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 9%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 9%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 9%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 9%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 9%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 9%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 10%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 11%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 11%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 11%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 11%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 11%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 11%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 11%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 11%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 11%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 12%[==> ] ETA: 0:00:02[KEvaluating over 1000 metamodels: 12%[==> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 12%[===> ] ETA: 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1000 metamodels: 25%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 26%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 26%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 26%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 26%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 26%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 26%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 26%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 27%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 27%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 27%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 27%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 27%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 27%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 27%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 27%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 28%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 28%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 28%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 28%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 28%[======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 28%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 28%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 28%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 28%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 28%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 28%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 29%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 29%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 29%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 29%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 29%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 29%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 29%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 29%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 29%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 30%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 30%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 30%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 30%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 30%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 30%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 30%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 30%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 30%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 30%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 30%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 31%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 31%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 31%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 31%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 31%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 31%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 31%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 31%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 32%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 32%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 32%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 32%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 32%[=======> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 32%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 32%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 32%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 32%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 32%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 32%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 33%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 33%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 33%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 33%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 33%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 33%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 33%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 33%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 33%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 34%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 34%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 34%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 34%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 34%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 34%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 34%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 34%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 34%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 34%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 34%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 35%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 35%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 35%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 35%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 35%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 35%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 35%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 35%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 35%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 36%[========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 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ETA: 0:00:01[KEvaluating over 1000 metamodels: 39%[=========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 39%[=========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 39%[=========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 39%[=========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 39%[=========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 39%[=========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 40%[=========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 40%[=========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 40%[=========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 40%[=========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 40%[=========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 40%[==========> ] ETA: 0:00:01[KEvaluating over 1000 metamodels: 40%[==========> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 40%[==========> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 40%[==========> ] ETA: 0:00:00[KEvaluating over 1000 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ETA: 0:00:00[KEvaluating over 1000 metamodels: 51%[============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 51%[============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 51%[============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 51%[============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 51%[============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 51%[============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 51%[============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 52%[============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 52%[============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 52%[============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 52%[============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 52%[=============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 52%[=============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 52%[=============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 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ETA: 0:00:00[KEvaluating over 1000 metamodels: 59%[==============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 59%[==============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 60%[==============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 60%[==============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 60%[==============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 60%[===============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 60%[===============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 60%[===============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 60%[===============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 60%[===============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 60%[===============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 61%[===============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 61%[===============> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 61%[===============> ] ETA: 0:00:00[KEvaluating 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96%[=======================> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 96%[=======================> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 96%[=======================> ] ETA: 0:00:00[KEvaluating over 1000 metamodels: 96%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 96%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 96%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 96%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 96%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 96%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 98%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 99%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 100%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 100%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 100%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 100%[========================>] ETA: 0:00:00[KEvaluating over 1000 metamodels: 100%[=========================] Time: 0:00:00[K
Test Summary: | Pass Total
random search | 19 19
Testing progressmeter rngs option with CPU1{Nothing}(nothing) and CPU1 grid
Evaluating over 30 metamodels: 0%[> ] ETA: N/A[KEvaluating over 30 metamodels: 3%[> ] ETA: 0:01:46[KEvaluating over 30 metamodels: 7%[=> ] ETA: 0:00:58[KEvaluating over 30 metamodels: 10%[==> ] ETA: 0:00:37[KEvaluating over 30 metamodels: 13%[===> ] ETA: 0:00:27[KEvaluating over 30 metamodels: 17%[====> ] ETA: 0:00:21[KEvaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:17[KEvaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:14[KEvaluating over 30 metamodels: 27%[======> ] ETA: 0:00:12[KEvaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:10[KEvaluating over 30 metamodels: 33%[========> ] ETA: 0:00:08[KEvaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:07[KEvaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:06[KEvaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:06[KEvaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:05[KEvaluating over 30 metamodels: 50%[============> ] ETA: 0:00:04[KEvaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:04[KEvaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:03[KEvaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:03[KEvaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:02[KEvaluating over 30 metamodels: 67%[================> ] ETA: 0:00:02[KEvaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:02[KEvaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:02[KEvaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:01[KEvaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:01[KEvaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:01[KEvaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:01[KEvaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 30 metamodels: 100%[=========================] Time: 0:00:04[K
[ Info: No measure specified. Setting measure=rms.
Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A[KEvaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:00:03[KEvaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:01[KEvaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:01[K
[ Info: No measure specified. Setting measure=rms.
[ Info: No measure specified. Setting measure=rms.
[ Info: No measure specified. Setting measure=rms.
Testing progressmeter rngs option with CPUProcesses{Nothing}(nothing) and CPU1 grid
[ Info: No measure specified. Setting measure=rms.
[ Info: Training [34mMachine{DeterministicTunedModel{Grid,…}} @523[39m.
[ Info: Attempting to evaluate 30 models.
Evaluating over 30 metamodels: 0%[> ] ETA: N/A[KEvaluating over 30 metamodels: 3%[> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 7%[=> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 10%[==> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 13%[===> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 17%[====> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 27%[======> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 33%[========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 50%[============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 67%[================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 30 metamodels: 100%[=========================] Time: 0:00:00[K
[ Info: No measure specified. Setting measure=rms.
Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A[KEvaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:01:14[KEvaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:19[KEvaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:37[K
[ Info: No measure specified. Setting measure=rms.
[ Info: No measure specified. Setting measure=rms.
[ Info: No measure specified. Setting measure=rms.
Testing progressmeter rngs option with CPUThreads{Nothing}(nothing) and CPU1 grid
[ Info: No measure specified. Setting measure=rms.
[ Info: Training [34mMachine{DeterministicTunedModel{Grid,…}} @793[39m.
[ Info: Attempting to evaluate 30 models.
Evaluating over 30 metamodels: 0%[> ] ETA: N/A[KEvaluating over 30 metamodels: 3%[> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 7%[=> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 10%[==> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 13%[===> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 17%[====> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 27%[======> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 33%[========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 50%[============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 67%[================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 30 metamodels: 100%[=========================] Time: 0:00:00[K
[ Info: No measure specified. Setting measure=rms.
Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A[KEvaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:00:01[KEvaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:00[K
[ Info: No measure specified. Setting measure=rms.
[ Info: No measure specified. Setting measure=rms.
[ Info: No measure specified. Setting measure=rms.
Testing progressmeter rngs option with CPU1{Nothing}(nothing) and CPUThreads grid
Evaluating over 30 metamodels: 0%[> ] ETA: N/A[KEvaluating over 30 metamodels: 3%[> ] ETA: 0:00:14[KEvaluating over 30 metamodels: 10%[==> ] ETA: 0:00:04[KEvaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:02[KEvaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:01[KEvaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:01[KEvaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 67%[================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 30 metamodels: 100%[=========================] Time: 0:00:00[K
[ Info: No measure specified. Setting measure=rms.
Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A[KEvaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:00:02[KEvaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:00[KEvaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:01[K
[ Info: No measure specified. Setting measure=rms.
[ Info: No measure specified. Setting measure=rms.
[ Info: No measure specified. Setting measure=rms.
Testing progressmeter rngs option with CPUProcesses{Nothing}(nothing) and CPUThreads grid
[ Info: No measure specified. Setting measure=rms.
[ Info: Training [34mMachine{DeterministicTunedModel{Grid,…}} @590[39m.
[ Info: Attempting to evaluate 30 models.
Evaluating over 30 metamodels: 0%[> ] ETA: N/A[KEvaluating over 30 metamodels: 3%[> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 13%[===> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 50%[============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 100%[=========================] Time: 0:00:00[K
[ Info: No measure specified. Setting measure=rms.
Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A[KEvaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:00:03[KEvaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:01[KEvaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:01[K
[ Info: No measure specified. Setting measure=rms.
[ Info: No measure specified. Setting measure=rms.
[ Info: No measure specified. Setting measure=rms.
Testing progressmeter rngs option with CPUThreads{Nothing}(nothing) and CPUThreads grid
[ Info: No measure specified. Setting measure=rms.
[ Info: Training [34mMachine{DeterministicTunedModel{Grid,…}} @760[39m.
[ Info: Attempting to evaluate 30 models.
Evaluating over 30 metamodels: 0%[> ] ETA: N/A[KEvaluating over 30 metamodels: 3%[> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 10%[==> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 30 metamodels: 100%[=========================] Time: 0:00:00[K
[ Info: No measure specified. Setting measure=rms.
Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A[KEvaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:00:00[KEvaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:00[K
[ Info: No measure specified. Setting measure=rms.
[ Info: No measure specified. Setting measure=rms.
[ Info: No measure specified. Setting measure=rms.
Testing progressmeter rngs option with CPU1{Nothing}(nothing) and CPUProcesses grid
Evaluating over 30 metamodels: 0%[> ] ETA: N/A[KEvaluating over 30 metamodels: 3%[> ] ETA: 0:00:41[KEvaluating over 30 metamodels: 7%[=> ] ETA: 0:00:20[KEvaluating over 30 metamodels: 10%[==> ] ETA: 0:00:13[KEvaluating over 30 metamodels: 13%[===> ] ETA: 0:00:10[KEvaluating over 30 metamodels: 17%[====> ] ETA: 0:00:08[KEvaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:06[KEvaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:06[KEvaluating over 30 metamodels: 27%[======> ] ETA: 0:00:05[KEvaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:04[KEvaluating over 30 metamodels: 33%[========> ] ETA: 0:00:04[KEvaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:03[KEvaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:03[KEvaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:02[KEvaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:02[KEvaluating over 30 metamodels: 50%[============> ] ETA: 0:00:02[KEvaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:02[KEvaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:01[KEvaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:01[KEvaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:01[KEvaluating over 30 metamodels: 67%[================> ] ETA: 0:00:01[KEvaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:01[KEvaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:01[KEvaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:01[KEvaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:01[KEvaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 30 metamodels: 100%[=========================] Time: 0:00:02[K
[ Info: No measure specified. Setting measure=rms.
Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A[KEvaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:00:01[KEvaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:00[KEvaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:00[K
[ Info: No measure specified. Setting measure=rms.
[ Info: No measure specified. Setting measure=rms.
[ Info: No measure specified. Setting measure=rms.
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/Bbgvk/src/learning_curves.jl:160
[ Info: No measure specified. Setting measure=rms.
[ Info: Training [34mMachine{DeterministicTunedModel{Grid,…}} @103[39m.
[ Info: Attempting to evaluate 30 models.
Evaluating over 30 metamodels: 0%[> ] ETA: N/A[KEvaluating over 30 metamodels: 3%[> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 7%[=> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 17%[====> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 23%[=====> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 27%[======> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 33%[========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 40%[==========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 43%[==========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 47%[===========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 50%[============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 53%[=============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 57%[==============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 60%[===============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 63%[===============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 67%[================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 70%[=================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 73%[==================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 80%[====================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 83%[====================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 87%[=====================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 90%[======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 30 metamodels: 100%[=========================] Time: 0:00:00[K
┌ 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/Bbgvk/src/learning_curves.jl:160
[ Info: No measure specified. Setting measure=rms.
Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A[KEvaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:00:05[KEvaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:01[KEvaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:02[K
┌ 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/Bbgvk/src/learning_curves.jl:160
[ Info: No measure specified. Setting measure=rms.
┌ 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/Bbgvk/src/learning_curves.jl:160
[ Info: No measure specified. Setting measure=rms.
┌ 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/Bbgvk/src/learning_curves.jl:160
[ Info: No measure specified. Setting measure=rms.
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/Bbgvk/src/learning_curves.jl:170
[ Info: No measure specified. Setting measure=rms.
[ Info: Training [34mMachine{DeterministicTunedModel{Grid,…}} @971[39m.
[ Info: Attempting to evaluate 30 models.
Evaluating over 30 metamodels: 0%[> ] ETA: N/A[KEvaluating over 30 metamodels: 3%[> ] ETA: 0:00:01[KEvaluating over 30 metamodels: 13%[===> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 20%[=====> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 30%[=======> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 37%[=========> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 50%[============> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 67%[================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 77%[===================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 93%[=======================> ] ETA: 0:00:00[KEvaluating over 30 metamodels: 97%[========================>] ETA: 0:00:00[KEvaluating over 30 metamodels: 100%[=========================] Time: 0:00:00[K
┌ 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/Bbgvk/src/learning_curves.jl:170
[ Info: No measure specified. Setting measure=rms.
Evaluating Learning curve with 3 rngs: 0%[> ] ETA: N/A[KEvaluating Learning curve with 3 rngs: 33%[======> ] ETA: 0:00:00[KEvaluating Learning curve with 3 rngs: 67%[============> ] ETA: 0:00:00[KEvaluating Learning curve with 3 rngs: 100%[==================] Time: 0:00:00[K
┌ 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/Bbgvk/src/learning_curves.jl:170
[ Info: No measure specified. Setting measure=rms.
┌ 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/Bbgvk/src/learning_curves.jl:170
[ Info: No measure specified. Setting measure=rms.
┌ 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/Bbgvk/src/learning_curves.jl:170
[ Info: No measure specified. Setting measure=rms.
Test Summary: | Pass Total
learning curves | 85 85
Testing MLJTuning tests passed