If you think that there is an error in how your package is being tested or represented, please file an issue at NewPkgEval.jl , making sure to read the FAQ first.
Results with Julia v1.2.0
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Last evaluation was ago and took 11 minutes, 13 seconds.
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Resolving package versions...
Installed Missings ──────────────────── v0.4.3
Installed DataAPI ───────────────────── v1.1.0
Installed LearnBase ─────────────────── v0.2.2
Installed PDMats ────────────────────── v0.9.10
Installed TableTraits ───────────────── v1.0.0
Installed ComputationalResources ────── v0.3.0
Installed MLJ ───────────────────────── v0.5.5
Installed ScientificTypes ───────────── v0.2.6
Installed BinaryProvider ────────────── v0.5.8
Installed StatsBase ─────────────────── v0.32.0
Installed URIParser ─────────────────── v0.4.0
Installed StatsFuns ─────────────────── v0.9.0
Installed InvertedIndices ───────────── v1.0.0
Installed DataValueInterfaces ───────── v1.0.0
Installed DocStringExtensions ───────── v0.8.1
Installed Requires ──────────────────── v0.5.2
Installed Compat ────────────────────── v2.2.0
Installed Reexport ──────────────────── v0.2.0
Installed Rmath ─────────────────────── v0.5.1
Installed OrderedCollections ────────── v1.1.0
Installed RecipesBase ───────────────── v0.7.0
Installed Tables ────────────────────── v0.2.11
Installed Parsers ───────────────────── v0.3.10
Installed DataStructures ────────────── v0.17.6
Installed FixedPointNumbers ─────────── v0.6.1
Installed Distributions ─────────────── v0.21.9
Installed Crayons ───────────────────── v4.0.1
Installed MultivariateStats ─────────── v0.7.0
Installed Parameters ────────────────── v0.12.0
Installed LossFunctions ─────────────── v0.5.1
Installed JSON ──────────────────────── v0.21.0
Installed IteratorInterfaceExtensions ─ v1.0.0
Installed PrettyTables ──────────────── v0.6.0
Installed CategoricalArrays ─────────── v0.7.3
Installed SortingAlgorithms ─────────── v0.3.1
Installed QuadGK ────────────────────── v2.1.1
Installed MLJModels ─────────────────── v0.5.9
Installed ColorTypes ────────────────── v0.8.0
Installed SpecialFunctions ──────────── v0.8.0
Installed Distances ─────────────────── v0.8.2
Installed Formatting ────────────────── v0.4.1
Installed ProgressMeter ─────────────── v1.2.0
Installed BinDeps ───────────────────── v0.8.10
Installed MLJBase ───────────────────── v0.8.4
Installed Arpack ────────────────────── v0.3.1
Updating `~/.julia/environments/v1.2/Project.toml`
[add582a8] + MLJ v0.5.5
Updating `~/.julia/environments/v1.2/Manifest.toml`
[7d9fca2a] + Arpack v0.3.1
[9e28174c] + BinDeps v0.8.10
[b99e7846] + BinaryProvider v0.5.8
[324d7699] + CategoricalArrays v0.7.3
[3da002f7] + ColorTypes v0.8.0
[34da2185] + Compat v2.2.0
[ed09eef8] + ComputationalResources v0.3.0
[a8cc5b0e] + Crayons v4.0.1
[9a962f9c] + DataAPI v1.1.0
[864edb3b] + DataStructures v0.17.6
[e2d170a0] + DataValueInterfaces v1.0.0
[b4f34e82] + Distances v0.8.2
[31c24e10] + Distributions v0.21.9
[ffbed154] + DocStringExtensions v0.8.1
[53c48c17] + FixedPointNumbers v0.6.1
[59287772] + Formatting v0.4.1
[41ab1584] + InvertedIndices v1.0.0
[82899510] + IteratorInterfaceExtensions v1.0.0
[682c06a0] + JSON v0.21.0
[7f8f8fb0] + LearnBase v0.2.2
[30fc2ffe] + LossFunctions v0.5.1
[add582a8] + MLJ v0.5.5
[a7f614a8] + MLJBase v0.8.4
[d491faf4] + MLJModels v0.5.9
[e1d29d7a] + Missings v0.4.3
[6f286f6a] + MultivariateStats v0.7.0
[bac558e1] + OrderedCollections v1.1.0
[90014a1f] + PDMats v0.9.10
[d96e819e] + Parameters v0.12.0
[69de0a69] + Parsers v0.3.10
[08abe8d2] + PrettyTables v0.6.0
[92933f4c] + ProgressMeter v1.2.0
[1fd47b50] + QuadGK v2.1.1
[3cdcf5f2] + RecipesBase v0.7.0
[189a3867] + Reexport v0.2.0
[ae029012] + Requires v0.5.2
[79098fc4] + Rmath v0.5.1
[321657f4] + ScientificTypes v0.2.6
[a2af1166] + SortingAlgorithms v0.3.1
[276daf66] + SpecialFunctions v0.8.0
[2913bbd2] + StatsBase v0.32.0
[4c63d2b9] + StatsFuns v0.9.0
[3783bdb8] + TableTraits v1.0.0
[bd369af6] + Tables v0.2.11
[30578b45] + URIParser v0.4.0
[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 Rmath ───────────→ `~/.julia/packages/Rmath/4wt82/deps/build.log`
Building SpecialFunctions → `~/.julia/packages/SpecialFunctions/ne2iw/deps/build.log`
Building Arpack ──────────→ `~/.julia/packages/Arpack/cu5By/deps/build.log`
Testing MLJ
Resolving package versions...
Installed NearestNeighbors ─── v0.4.4
Installed ScikitLearnBase ──── v0.5.0
Installed ArrayLayouts ─────── v0.1.5
Installed UnicodePlots ─────── v1.1.0
Installed DataFrames ───────── v0.19.4
Installed DecisionTree ─────── v0.9.1
Installed PooledArrays ─────── v0.5.2
Installed RData ────────────── v0.6.3
Installed FilePathsBase ────── v0.7.0
Installed FillArrays ───────── v0.8.2
Installed WeakRefStrings ───── v0.6.1
Installed TimeZones ────────── v0.10.3
Installed TranscodingStreams ─ v0.9.5
Installed CodecZlib ────────── v0.6.0
Installed Mocking ──────────── v0.7.0
Installed LazyArrays ───────── v0.14.10
Installed StaticArrays ─────── v0.12.1
Installed MacroTools ───────── v0.5.2
Installed FileIO ───────────── v1.1.0
Installed CSV ──────────────── v0.5.18
Installed RDatasets ────────── v0.6.5
Installed EzXML ────────────── v0.9.5
Building EzXML ────→ `~/.julia/packages/EzXML/QtGgF/deps/build.log`
Building TimeZones → `~/.julia/packages/TimeZones/pjvlM/deps/build.log`
Building CodecZlib → `~/.julia/packages/CodecZlib/5t9zO/deps/build.log`
Status `/tmp/jl_7MtIdl/Manifest.toml`
[7d9fca2a] Arpack v0.3.1
[4c555306] ArrayLayouts v0.1.5
[9e28174c] BinDeps v0.8.10
[b99e7846] BinaryProvider v0.5.8
[336ed68f] CSV v0.5.18
[324d7699] CategoricalArrays v0.7.3
[944b1d66] CodecZlib v0.6.0
[3da002f7] ColorTypes v0.8.0
[34da2185] Compat v2.2.0
[ed09eef8] ComputationalResources v0.3.0
[a8cc5b0e] Crayons v4.0.1
[9a962f9c] DataAPI v1.1.0
[a93c6f00] DataFrames v0.19.4
[864edb3b] DataStructures v0.17.6
[e2d170a0] DataValueInterfaces v1.0.0
[7806a523] DecisionTree v0.9.1
[b4f34e82] Distances v0.8.2
[31c24e10] Distributions v0.21.9
[ffbed154] DocStringExtensions v0.8.1
[8f5d6c58] EzXML v0.9.5
[5789e2e9] FileIO v1.1.0
[48062228] FilePathsBase v0.7.0
[1a297f60] FillArrays v0.8.2
[53c48c17] FixedPointNumbers v0.6.1
[59287772] Formatting v0.4.1
[41ab1584] InvertedIndices v1.0.0
[82899510] IteratorInterfaceExtensions v1.0.0
[682c06a0] JSON v0.21.0
[5078a376] LazyArrays v0.14.10
[7f8f8fb0] LearnBase v0.2.2
[30fc2ffe] LossFunctions v0.5.1
[add582a8] MLJ v0.5.5
[a7f614a8] MLJBase v0.8.4
[d491faf4] MLJModels v0.5.9
[1914dd2f] MacroTools v0.5.2
[e1d29d7a] Missings v0.4.3
[78c3b35d] Mocking v0.7.0
[6f286f6a] MultivariateStats v0.7.0
[b8a86587] NearestNeighbors v0.4.4
[bac558e1] OrderedCollections v1.1.0
[90014a1f] PDMats v0.9.10
[d96e819e] Parameters v0.12.0
[69de0a69] Parsers v0.3.10
[2dfb63ee] PooledArrays v0.5.2
[08abe8d2] PrettyTables v0.6.0
[92933f4c] ProgressMeter v1.2.0
[1fd47b50] QuadGK v2.1.1
[df47a6cb] RData v0.6.3
[ce6b1742] RDatasets v0.6.5
[3cdcf5f2] RecipesBase v0.7.0
[189a3867] Reexport v0.2.0
[ae029012] Requires v0.5.2
[79098fc4] Rmath v0.5.1
[321657f4] ScientificTypes v0.2.6
[6e75b9c4] ScikitLearnBase v0.5.0
[a2af1166] SortingAlgorithms v0.3.1
[276daf66] SpecialFunctions v0.8.0
[90137ffa] StaticArrays v0.12.1
[2913bbd2] StatsBase v0.32.0
[4c63d2b9] StatsFuns v0.9.0
[3783bdb8] TableTraits v1.0.0
[bd369af6] Tables v0.2.11
[f269a46b] TimeZones v0.10.3
[3bb67fe8] TranscodingStreams v0.9.5
[30578b45] URIParser v0.4.0
[b8865327] UnicodePlots v1.1.0
[ea10d353] WeakRefStrings v0.6.1
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[9fa8497b] Future [`@stdlib/Future`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[4607b0f0] SuiteSparse [`@stdlib/SuiteSparse`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
[ Info: Model metadata loaded from registry.
Test Summary: | Pass Total
utilities | 12 12
Test Summary: | Pass Total
parameters | 17 17
┌ Info: MLJModels.DecisionTree_.DecisionTreeRegressor does not support sample weights and the supplied weights will be ignored in training.
└ However, supplied weights will be passed to weight-supporting measures on calls to `evaluate!` and in tuning.
┌ Info: MLJModels.DecisionTree_.DecisionTreeRegressor does not support sample weights and the supplied weights will be ignored in training.
└ However, supplied weights will be passed to weight-supporting measures on calls to `evaluate!` and in tuning.
[ Info: Training [34mMachine{ConstantClassifier} @ 1…96[39m.
[ Info: Training [34mMachine{ConstantClassifier} @ 1…61[39m.
Test Summary: | Pass Total
Machines | 19 19
Training ensemble: 25%[============> ] ETA: 0:00:03[K
Training ensemble: 100%[==================================================] Time: 0:00:01[K
Test Summary: | Pass Total
networks | 37 37
[ Info: Training [34mNodalMachine{FeatureSelector} @ 6…65[39m.
[ Info: Training [34mNodalMachine{FooBarRegressor} @ 1…88[39m.
[ Info: Training [34mNodalMachine{SimpleDeterministicCompositeModel} @ 1…66[39m.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 7…16[39m.
[ Info: Training [34mNodalMachine{FooBarRegressor} @ 7…45[39m.
[ Info: Updating [34mNodalMachine{SimpleDeterministicCompositeModel} @ 1…66[39m.
[ Info: Updating [34mNodalMachine{FeatureSelector} @ 7…16[39m.
[ Info: Training [34mNodalMachine{FooBarRegressor} @ 7…45[39m.
[ Info: Training [34mNodalMachine{SimpleDeterministicCompositeModel} @ 1…66[39m.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 9…05[39m.
[ Info: Training [34mNodalMachine{FooBarRegressor} @ 1…91[39m.
[ Info: Training [34mMachine{WrappedRidge} @ 1…26[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 1…52[39m.
[ Info: Training [34mNodalMachine{UnivariateBoxCoxTransformer} @ 1…66[39m.
[ Info: Training [34mNodalMachine{FooBarRegressor} @ 1…51[39m.
[ Info: Updating [34mMachine{WrappedRidge} @ 1…26[39m.
┌ Info: Not retraining [34mNodalMachine{Standardizer} @ 1…52[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
┌ Info: Not retraining [34mNodalMachine{UnivariateBoxCoxTransformer} @ 1…66[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
[ Info: Updating [34mNodalMachine{FooBarRegressor} @ 1…51[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 1…65[39m.
[ Info: Spawning 3 sub-features to one-hot encode feature :x1.
[ Info: Training [34mNodalMachine{UnivariateStandardizer} @ 1…08[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 3…04[39m.
[ Info: Training [34mNodalMachine{DecisionTreeRegressor} @ 4…80[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 1…26[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 1…17[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 1…26[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 1…17[39m.
[ Info: Training [34mMachine{Composite3} @ 7…08[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 1…31[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 3…72[39m.
[ Info: Training [34mMachine{Composite3} @ 7…97[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 2…01[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 1…30[39m.
Test Summary: | Pass Total
composites | 71 71
[ Info: Training [34mNodalMachine{FeatureSelector} @ 1…14[39m.
[ Info: Training [34mNodalMachine{UnivariateStandardizer} @ 7…36[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 1…57[39m.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 3…91[39m.
[ Info: Training [34mNodalMachine{UnivariateStandardizer} @ 1…87[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 1…93[39m.
[ Info: Updating [34mNodalMachine{FeatureSelector} @ 1…14[39m.
┌ Info: Not retraining [34mNodalMachine{UnivariateStandardizer} @ 7…36[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 1…57[39m.
[ Info: Updating [34mNodalMachine{FeatureSelector} @ 3…91[39m.
┌ Info: Not retraining [34mNodalMachine{UnivariateStandardizer} @ 1…87[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 1…93[39m.
[ Info: Training [34mMachine{Pipe} @ 1…03[39m.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 3…71[39m.
[ Info: Training [34mNodalMachine{UnivariateStandardizer} @ 1…73[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 5…98[39m.
[ Info: Training [34mMachine{Pipe21} @ 2…42[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 1…40[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 9…31[39m.
[ Info: Training [34mMachine{Piper3} @ 4…75[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 1…46[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 7…99[39m.
[ Info: Training [34mMachine{Piper3} @ 1…85[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 9…36[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 1…64[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 1…32[39m.
[ Info: Spawning 3 sub-features to one-hot encode feature :x3.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 1…00[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 1…36[39m.
[ Info: Training [34mMachine{Pipe4} @ 1…80[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 9…26[39m.
[ Info: Spawning 3 sub-features to one-hot encode feature :x3.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 1…09[39m.
[ Info: Training [34mNodalMachine{StaticTransformer} @ 1…10[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 9…27[39m.
[ Info: Training [34mNodalMachine{StaticTransformer} @ 9…90[39m.
[ Info: Training [34mMachine{Pipe9} @ 1…94[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 7…33[39m.
[ Info: Spawning 2 sub-features to one-hot encode feature :gender.
[ Info: Training [34mNodalMachine{UnivariateStandardizer} @ 5…91[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 1…97[39m.
Test Summary: | Pass Total
pipelines | 133 133
Evaluating over 5 folds: 17%[====> ] ETA: 0:00:00[K
Evaluating over 5 folds: 33%[========> ] ETA: 0:00:03[K
Evaluating over 5 folds: 50%[============> ] ETA: 0:00:02[K
Evaluating over 5 folds: 67%[================> ] ETA: 0:00:01[K
Evaluating over 5 folds: 83%[====================> ] ETA: 0:00:00[K
Evaluating over 5 folds: 100%[=========================] Time: 0:00:01[K
┌─────────┬─────────────────────┐
│ measure │ measurement │
├─────────┼─────────────────────┤
│ my_rms │ 0.6 │
│ my_mav │ 0.6 │
│ rmslp1 │ 0.25095859542946747 │
└─────────┴─────────────────────┘
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:00[K
┌─────────┬─────────────────────┐
│ measure │ measurement │
├─────────┼─────────────────────┤
│ rms │ 0.6666666666666667 │
│ rmslp1 │ 0.25131442828090633 │
└─────────┴─────────────────────┘
Evaluating over 5 folds: 17%[====> ] ETA: 0:00:00[K
Evaluating over 5 folds: 33%[========> ] ETA: 0:00:00[K┌─────────┬─────────────────────┐
│ measure │ measurement │
├─────────┼─────────────────────┤
│ rms │ 0.6123724356957945 │
│ rmslp1 │ 0.25095859542946747 │
└─────────┴─────────────────────┘
Evaluating over 5 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 5 folds: 67%[================> ] ETA: 0:00:00[K
Evaluating over 5 folds: 83%[====================> ] ETA: 0:00:00[K
Evaluating over 5 folds: 100%[=========================] Time: 0:00:00[K
Evaluating over 6 folds: 14%[===> ] ETA: 0:00:00[K
Evaluating over 6 folds: 29%[=======> ] ETA: 0:00:00[K
Evaluating over 6 folds: 43%[==========> ] ETA: 0:00:00[K
Evaluating over 6 folds: 57%[==============> ] ETA: 0:00:00[K
Evaluating over 6 folds: 71%[=================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 86%[=====================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 100%[=========================] Time: 0:00:00[K
[ Info: Updating [34mMachine{Resampler} @ 3…62[39m.
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:00[K
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:01[K
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:01[K
[ Info: Passing machine sample weights to any supported measures.
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:00[K
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:00[K
[ Info: Passing machine sample weights to any supported measures.
Evaluating over 6 folds: 14%[===> ] ETA: 0:00:00[K
Evaluating over 6 folds: 29%[=======> ] ETA: 0:00:08[K
Evaluating over 6 folds: 43%[==========> ] ETA: 0:00:04[K
Evaluating over 6 folds: 57%[==============> ] ETA: 0:00:02[K
Evaluating over 6 folds: 71%[=================> ] ETA: 0:00:01[K
Evaluating over 6 folds: 86%[=====================> ] ETA: 0:00:01[K
Evaluating over 6 folds: 100%[=========================] Time: 0:00:03[K
[ Info: Passing machine sample weights to any supported measures.
[ Info: Creating subsamples from a subset of all rows.
Evaluating over 6 folds: 14%[===> ] ETA: 0:00:00[K
Evaluating over 6 folds: 29%[=======> ] ETA: 0:00:00[K
Evaluating over 6 folds: 43%[==========> ] ETA: 0:00:00[K
Evaluating over 6 folds: 57%[==============> ] ETA: 0:00:00[K
Evaluating over 6 folds: 71%[=================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 86%[=====================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 100%[=========================] Time: 0:00:00[K
[ Info: Training [34mMachine{Resampler} @ 3…13[39m.
[ Info: Passing machine sample weights to any supported measures.
[ Info: Passing machine sample weights to any supported measures.
Evaluating over 6 folds: 14%[===> ] ETA: 0:00:00[K
Evaluating over 6 folds: 29%[=======> ] ETA: 0:00:00[K
Evaluating over 6 folds: 43%[==========> ] ETA: 0:00:00[K
Evaluating over 6 folds: 57%[==============> ] ETA: 0:00:00[K
Evaluating over 6 folds: 71%[=================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 86%[=====================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 100%[=========================] Time: 0:00:00[K
[ Info: Training [34mMachine{Resampler} @ 1…59[39m.
Evaluating over 6 folds: 14%[===> ] ETA: 0:00:00[K
Evaluating over 6 folds: 29%[=======> ] ETA: 0:00:00[K
Evaluating over 6 folds: 43%[==========> ] ETA: 0:00:00[K
Evaluating over 6 folds: 57%[==============> ] ETA: 0:00:00[K
Evaluating over 6 folds: 71%[=================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 86%[=====================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 100%[=========================] Time: 0:00:00[K
Test Summary: | Pass Total
resampling | 52 52
[ Info: Training [34mMachine{DeterministicTunedModel} @ 1…06[39m.
[ Info: Mimimizing rms.
Iterating over a 40-point grid: 2%[> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 7%[=> ] ETA: 0:00:42[K
Iterating over a 40-point grid: 10%[==> ] ETA: 0:00:38[K
Iterating over a 40-point grid: 12%[===> ] ETA: 0:00:34[K
Iterating over a 40-point grid: 15%[===> ] ETA: 0:00:28[K
Iterating over a 40-point grid: 17%[====> ] ETA: 0:00:23[K
Iterating over a 40-point grid: 20%[====> ] ETA: 0:00:20[K
Iterating over a 40-point grid: 22%[=====> ] ETA: 0:00:17[K
Iterating over a 40-point grid: 24%[======> ] ETA: 0:00:15[K
Iterating over a 40-point grid: 27%[======> ] ETA: 0:00:13[K
Iterating over a 40-point grid: 29%[=======> ] ETA: 0:00:12[K
Iterating over a 40-point grid: 32%[=======> ] ETA: 0:00:10[K
Iterating over a 40-point grid: 34%[========> ] ETA: 0:00:09[K
Iterating over a 40-point grid: 37%[=========> ] ETA: 0:00:08[K
Iterating over a 40-point grid: 39%[=========> ] ETA: 0:00:08[K
Iterating over a 40-point grid: 41%[==========> ] ETA: 0:00:07[K
Iterating over a 40-point grid: 44%[==========> ] ETA: 0:00:06[K
Iterating over a 40-point grid: 46%[===========> ] ETA: 0:00:06[K
Iterating over a 40-point grid: 49%[============> ] ETA: 0:00:05[K
Iterating over a 40-point grid: 51%[============> ] ETA: 0:00:05[K
Iterating over a 40-point grid: 54%[=============> ] ETA: 0:00:04[K
Iterating over a 40-point grid: 56%[==============> ] ETA: 0:00:04[K
Iterating over a 40-point grid: 59%[==============> ] ETA: 0:00:03[K
Iterating over a 40-point grid: 61%[===============> ] ETA: 0:00:03[K
Iterating over a 40-point grid: 63%[===============> ] ETA: 0:00:03[K
Iterating over a 40-point grid: 66%[================> ] ETA: 0:00:03[K
Iterating over a 40-point grid: 68%[=================> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 71%[=================> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 73%[==================> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 76%[==================> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 78%[===================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 80%[====================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 83%[====================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 85%[=====================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 88%[=====================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 90%[======================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 93%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 95%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 98%[========================>] ETA: 0:00:00[K
Iterating over a 40-point grid: 100%[=========================] Time: 0:00:04[K
[ Info: Training best model on all supplied data.
[ Info: Updating [34mMachine{DeterministicTunedModel} @ 1…06[39m.
[ Info: Mimimizing rms.
Iterating over a 40-point grid: 2%[> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 7%[=> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 10%[==> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 12%[===> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 15%[===> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 17%[====> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 20%[====> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 22%[=====> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 24%[======> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 27%[======> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 29%[=======> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 32%[=======> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 34%[========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 37%[=========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 39%[=========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 41%[==========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 44%[==========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 46%[===========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 49%[============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 51%[============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 54%[=============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 56%[==============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 59%[==============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 61%[===============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 63%[===============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 66%[================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 68%[=================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 71%[=================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 73%[==================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 76%[==================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 78%[===================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 80%[====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 83%[====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 85%[=====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 88%[=====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 90%[======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 93%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 95%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 98%[========================>] ETA: 0:00:00[K
Iterating over a 40-point grid: 100%[=========================] Time: 0:00:00[K
[ Info: Training best model on all supplied data.
[ Info: Updating [34mMachine{DeterministicTunedModel} @ 1…06[39m.
[ Info: Mimimizing rms.
Iterating over a 40-point grid: 2%[> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 7%[=> ] ETA: 0:00:05[K
Iterating over a 40-point grid: 10%[==> ] ETA: 0:00:03[K
Iterating over a 40-point grid: 12%[===> ] ETA: 0:00:03[K
Iterating over a 40-point grid: 15%[===> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 17%[====> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 20%[====> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 22%[=====> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 24%[======> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 27%[======> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 29%[=======> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 32%[=======> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 34%[========> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 37%[=========> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 39%[=========> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 41%[==========> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 44%[==========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 46%[===========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 49%[============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 51%[============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 54%[=============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 56%[==============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 59%[==============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 61%[===============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 63%[===============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 66%[================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 68%[=================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 71%[=================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 73%[==================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 76%[==================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 78%[===================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 80%[====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 83%[====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 85%[=====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 88%[=====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 90%[======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 93%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 95%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 98%[========================>] ETA: 0:00:00[K
Iterating over a 40-point grid: 100%[=========================] Time: 0:00:00[K
[ Info: Training best model on all supplied data.
┌ Info: A model type "KNNRegressor" is already loaded.
└ No new code loaded.
[ Info: Training [34mMachine{DeterministicTunedModel} @ 2…77[39m.
[ Info: Mimimizing rms.
Iterating over a 72-point grid: 1%[> ] ETA: 0:00:00[K
Iterating over a 72-point grid: 4%[=> ] ETA: 0:02:41[K
Iterating over a 72-point grid: 5%[=> ] ETA: 0:02:24[K
Iterating over a 72-point grid: 7%[=> ] ETA: 0:01:54[K
Iterating over a 72-point grid: 8%[==> ] ETA: 0:01:34[K
Iterating over a 72-point grid: 10%[==> ] ETA: 0:01:20[K
Iterating over a 72-point grid: 11%[==> ] ETA: 0:01:09[K
Iterating over a 72-point grid: 12%[===> ] ETA: 0:01:01[K
Iterating over a 72-point grid: 14%[===> ] ETA: 0:00:54[K
Iterating over a 72-point grid: 15%[===> ] ETA: 0:00:48[K
Iterating over a 72-point grid: 16%[====> ] ETA: 0:00:44[K
Iterating over a 72-point grid: 18%[====> ] ETA: 0:00:40[K
Iterating over a 72-point grid: 19%[====> ] ETA: 0:00:37[K
Iterating over a 72-point grid: 21%[=====> ] ETA: 0:00:34[K
Iterating over a 72-point grid: 22%[=====> ] ETA: 0:00:31[K
Iterating over a 72-point grid: 23%[=====> ] ETA: 0:00:29[K
Iterating over a 72-point grid: 25%[======> ] ETA: 0:00:27[K
Iterating over a 72-point grid: 26%[======> ] ETA: 0:00:25[K
Iterating over a 72-point grid: 27%[======> ] ETA: 0:00:24[K
Iterating over a 72-point grid: 29%[=======> ] ETA: 0:00:22[K
Iterating over a 72-point grid: 30%[=======> ] ETA: 0:00:21[K
Iterating over a 72-point grid: 32%[=======> ] ETA: 0:00:20[K
Iterating over a 72-point grid: 33%[========> ] ETA: 0:00:19[K
Iterating over a 72-point grid: 34%[========> ] ETA: 0:00:18[K
Iterating over a 72-point grid: 36%[========> ] ETA: 0:00:17[K
Iterating over a 72-point grid: 37%[=========> ] ETA: 0:00:16[K
Iterating over a 72-point grid: 38%[=========> ] ETA: 0:00:15[K
Iterating over a 72-point grid: 40%[=========> ] ETA: 0:00:14[K
Iterating over a 72-point grid: 41%[==========> ] ETA: 0:00:13[K
Iterating over a 72-point grid: 42%[==========> ] ETA: 0:00:13[K
Iterating over a 72-point grid: 44%[==========> ] ETA: 0:00:12[K
Iterating over a 72-point grid: 45%[===========> ] ETA: 0:00:11[K
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Iterating over a 72-point grid: 48%[===========> ] ETA: 0:00:10[K
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Iterating over a 72-point grid: 52%[=============> ] ETA: 0:00:09[K
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Iterating over a 72-point grid: 68%[=================> ] ETA: 0:00:05[K
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Iterating over a 72-point grid: 71%[=================> ] ETA: 0:00:04[K
Iterating over a 72-point grid: 73%[==================> ] ETA: 0:00:04[K
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Iterating over a 72-point grid: 88%[=====================> ] ETA: 0:00:02[K
Iterating over a 72-point grid: 89%[======================> ] ETA: 0:00:01[K
Iterating over a 72-point grid: 90%[======================> ] ETA: 0:00:01[K
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Iterating over a 72-point grid: 93%[=======================> ] ETA: 0:00:01[K
Iterating over a 72-point grid: 95%[=======================> ] ETA: 0:00:01[K
Iterating over a 72-point grid: 96%[=======================> ] ETA: 0:00:00[K
Iterating over a 72-point grid: 97%[========================>] ETA: 0:00:00[K
Iterating over a 72-point grid: 99%[========================>] ETA: 0:00:00[K
Iterating over a 72-point grid: 100%[=========================] Time: 0:00:11[K
[ Info: Training best model on all supplied data.
[ Info: Updating [34mMachine{DeterministicTunedModel} @ 2…77[39m.
[ Info: Mimimizing rms.
Iterating over a 56-point grid: 2%[> ] ETA: 0:00:00[K
Iterating over a 56-point grid: 5%[=> ] ETA: 0:00:01[K
Iterating over a 56-point grid: 7%[=> ] ETA: 0:00:01[K
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Iterating over a 56-point grid: 68%[=================> ] ETA: 0:00:01[K
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Iterating over a 56-point grid: 100%[=========================] Time: 0:00:02[K
[ Info: Training best model on all supplied data.
[ Info: Updating [34mMachine{DeterministicTunedModel} @ 2…77[39m.
┌ Warning: No resolution specified for forest.bagging_fraction. Will use a value of 5.
└ @ MLJ ~/.julia/packages/MLJ/LDDzK/src/tuning.jl:189
[ Info: Mimimizing rms.
Iterating over a 60-point grid: 2%[> ] ETA: 0:00:00[K
Iterating over a 60-point grid: 5%[=> ] ETA: 0:00:01[K
Iterating over a 60-point grid: 7%[=> ] ETA: 0:00:01[K
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[ Info: Training best model on all supplied data.
┌ Warning: No measure specified. Setting measure=rms.
└ @ MLJ ~/.julia/packages/MLJ/LDDzK/src/machines.jl:148
[ Info: Training [34mMachine{DeterministicTunedModel} @ 7…21[39m.
[ Info: Mimimizing rms.
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[ Info: Training best model on all supplied data.
[ Info: Training [34mMachine{DeterministicTunedModel} @ 7…46[39m.
[ Info: Mimimizing rms.
atom.K=3 bagging_fraction=0.4 measurement=0.2973770670340338
atom.K=4 bagging_fraction=0.4 measurement=0.29487431347616055
atom.K=3 bagging_fraction=0.7 measurement=0.3053084971552806
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atom.K=3 bagging_fraction=1.0 measurement=0.322315125056536
atom.K=4 bagging_fraction=1.0 measurement=0.31259942522260187
[ Info: Training best model on all supplied data.
[ Info: Training [34mMachine{DeterministicEnsembleModel{KNNRegressor}} @ 1…31[39m.
[ Info: Training [34mMachine{ProbabilisticTunedModel} @ 7…58[39m.
[ Info: Maximizing BrierScore(UnivariateFinite).
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[ Info: Training best model on all supplied data.
[ Info: Training [34mMachine{ProbabilisticTunedModel} @ 8…49[39m.
[ Info: Maximizing BrierScore(UnivariateFinite).
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[ Info: Training best model on all supplied data.
[ Info: Training [34mMachine{ProbabilisticTunedModel} @ 9…89[39m.
[ Info: Maximizing BrierScore(UnivariateFinite).
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[ Info: Training best model on all supplied data.
[ Info: Training [34mMachine{DeterministicTunedModel} @ 1…24[39m.
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┌ Info: Training of best model suppressed.
└ To train tuning machine `mach` on all supplied data, call `fit!(mach.fitresult)`.
[ Info: Training [34mMachine{DeterministicTunedModel} @ 1…55[39m.
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┌ Info: Training of best model suppressed.
└ To train tuning machine `mach` on all supplied data, call `fit!(mach.fitresult)`.
Test Summary: | Pass Total
tuning | 18 18
Training ensemble: 20%[==========> ] ETA: 0:00:04[K
Training ensemble: 100%[==================================================] Time: 0:00:01[K
Training ensemble: 20%[==========> ] ETA: 0:00:02[K
Training ensemble: 100%[==================================================] Time: 0:00:00[K
[ Info: Training [34mMachine{DeterministicEnsembleModel{KNNRegressor}} @ 1…62[39m.
Test Summary: | Pass Total
ensembles | 41 41
Test Summary: | Pass Total
matching models to data | 11 11
┌ Info:
│ is_probabilistic = true
│ input_scitype = ScientificTypes.Table{Union{AbstractArray{Count,1}, AbstractArray{Unknown,1}}}
└ target_scitype = AbstractArray{Count,1}
┌ Warning: Missing values encountered coercing scitype to Count.
│ Coerced to Union{Missing,Count} instead.
└ @ ScientificTypes ~/.julia/packages/ScientificTypes/XsivS/src/conventions/mlj/mlj.jl:5
┌ Info:
│ is_probabilistic = false
│ input_scitype = ScientificTypes.Table{Union{AbstractArray{Continuous,1}, AbstractArray{Multiclass{4},1}, AbstractArray{Union{Missing, Count},1}}}
└ target_scitype = AbstractArray{Count,1}
Test Summary: | Pass Total
tasks | 20 20
Test Summary: | Pass Total
scitypes | 3 3
Testing MLJ tests passed
Results with Julia v1.3.0
Testing was successful .
Last evaluation was ago and took 11 minutes, 33 seconds.
Click here to download the log file.
Click here to show the log contents.
Resolving package versions...
Installed URIParser ─────────────────── v0.4.0
Installed SortingAlgorithms ─────────── v0.3.1
Installed Arpack ────────────────────── v0.3.1
Installed DataStructures ────────────── v0.17.6
Installed ScientificTypes ───────────── v0.2.6
Installed MLJ ───────────────────────── v0.5.5
Installed DocStringExtensions ───────── v0.8.1
Installed LossFunctions ─────────────── v0.5.1
Installed QuadGK ────────────────────── v2.1.1
Installed Compat ────────────────────── v2.2.0
Installed CategoricalArrays ─────────── v0.7.3
Installed StatsFuns ─────────────────── v0.9.0
Installed MultivariateStats ─────────── v0.7.0
Installed Parsers ───────────────────── v0.3.10
Installed InvertedIndices ───────────── v1.0.0
Installed BinaryProvider ────────────── v0.5.8
Installed Missings ──────────────────── v0.4.3
Installed ProgressMeter ─────────────── v1.2.0
Installed Distributions ─────────────── v0.21.9
Installed LearnBase ─────────────────── v0.2.2
Installed MLJBase ───────────────────── v0.8.4
Installed Parameters ────────────────── v0.12.0
Installed Rmath ─────────────────────── v0.5.1
Installed OrderedCollections ────────── v1.1.0
Installed FixedPointNumbers ─────────── v0.6.1
Installed TableTraits ───────────────── v1.0.0
Installed Crayons ───────────────────── v4.0.1
Installed JSON ──────────────────────── v0.21.0
Installed SpecialFunctions ──────────── v0.8.0
Installed ColorTypes ────────────────── v0.8.0
Installed RecipesBase ───────────────── v0.7.0
Installed DataAPI ───────────────────── v1.1.0
Installed BinDeps ───────────────────── v0.8.10
Installed Distances ─────────────────── v0.8.2
Installed Tables ────────────────────── v0.2.11
Installed DataValueInterfaces ───────── v1.0.0
Installed ComputationalResources ────── v0.3.0
Installed Requires ──────────────────── v0.5.2
Installed IteratorInterfaceExtensions ─ v1.0.0
Installed PDMats ────────────────────── v0.9.10
Installed Reexport ──────────────────── v0.2.0
Installed MLJModels ─────────────────── v0.5.9
Installed StatsBase ─────────────────── v0.32.0
Installed Formatting ────────────────── v0.4.1
Installed PrettyTables ──────────────── v0.6.0
Updating `~/.julia/environments/v1.3/Project.toml`
[add582a8] + MLJ v0.5.5
Updating `~/.julia/environments/v1.3/Manifest.toml`
[7d9fca2a] + Arpack v0.3.1
[9e28174c] + BinDeps v0.8.10
[b99e7846] + BinaryProvider v0.5.8
[324d7699] + CategoricalArrays v0.7.3
[3da002f7] + ColorTypes v0.8.0
[34da2185] + Compat v2.2.0
[ed09eef8] + ComputationalResources v0.3.0
[a8cc5b0e] + Crayons v4.0.1
[9a962f9c] + DataAPI v1.1.0
[864edb3b] + DataStructures v0.17.6
[e2d170a0] + DataValueInterfaces v1.0.0
[b4f34e82] + Distances v0.8.2
[31c24e10] + Distributions v0.21.9
[ffbed154] + DocStringExtensions v0.8.1
[53c48c17] + FixedPointNumbers v0.6.1
[59287772] + Formatting v0.4.1
[41ab1584] + InvertedIndices v1.0.0
[82899510] + IteratorInterfaceExtensions v1.0.0
[682c06a0] + JSON v0.21.0
[7f8f8fb0] + LearnBase v0.2.2
[30fc2ffe] + LossFunctions v0.5.1
[add582a8] + MLJ v0.5.5
[a7f614a8] + MLJBase v0.8.4
[d491faf4] + MLJModels v0.5.9
[e1d29d7a] + Missings v0.4.3
[6f286f6a] + MultivariateStats v0.7.0
[bac558e1] + OrderedCollections v1.1.0
[90014a1f] + PDMats v0.9.10
[d96e819e] + Parameters v0.12.0
[69de0a69] + Parsers v0.3.10
[08abe8d2] + PrettyTables v0.6.0
[92933f4c] + ProgressMeter v1.2.0
[1fd47b50] + QuadGK v2.1.1
[3cdcf5f2] + RecipesBase v0.7.0
[189a3867] + Reexport v0.2.0
[ae029012] + Requires v0.5.2
[79098fc4] + Rmath v0.5.1
[321657f4] + ScientificTypes v0.2.6
[a2af1166] + SortingAlgorithms v0.3.1
[276daf66] + SpecialFunctions v0.8.0
[2913bbd2] + StatsBase v0.32.0
[4c63d2b9] + StatsFuns v0.9.0
[3783bdb8] + TableTraits v1.0.0
[bd369af6] + Tables v0.2.11
[30578b45] + URIParser v0.4.0
[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 Arpack ──────────→ `~/.julia/packages/Arpack/cu5By/deps/build.log`
Building Rmath ───────────→ `~/.julia/packages/Rmath/4wt82/deps/build.log`
Building SpecialFunctions → `~/.julia/packages/SpecialFunctions/ne2iw/deps/build.log`
Testing MLJ
Resolving package versions...
Installed NearestNeighbors ─── v0.4.4
Installed UnicodePlots ─────── v1.1.0
Installed LazyArrays ───────── v0.14.10
Installed FileIO ───────────── v1.1.0
Installed StaticArrays ─────── v0.12.1
Installed DecisionTree ─────── v0.9.1
Installed MacroTools ───────── v0.5.2
Installed ArrayLayouts ─────── v0.1.5
Installed RData ────────────── v0.6.3
Installed Mocking ──────────── v0.7.0
Installed EzXML ────────────── v0.9.5
Installed ScikitLearnBase ──── v0.5.0
Installed FilePathsBase ────── v0.7.0
Installed TimeZones ────────── v0.10.3
Installed CodecZlib ────────── v0.6.0
Installed PooledArrays ─────── v0.5.2
Installed WeakRefStrings ───── v0.6.1
Installed DataFrames ───────── v0.19.4
Installed RDatasets ────────── v0.6.5
Installed CSV ──────────────── v0.5.18
Installed FillArrays ───────── v0.8.2
Installed TranscodingStreams ─ v0.9.5
Building EzXML ────→ `~/.julia/packages/EzXML/QtGgF/deps/build.log`
Building TimeZones → `~/.julia/packages/TimeZones/pjvlM/deps/build.log`
Building CodecZlib → `~/.julia/packages/CodecZlib/5t9zO/deps/build.log`
Status `/tmp/jl_ZEDsy2/Manifest.toml`
[7d9fca2a] Arpack v0.3.1
[4c555306] ArrayLayouts v0.1.5
[9e28174c] BinDeps v0.8.10
[b99e7846] BinaryProvider v0.5.8
[336ed68f] CSV v0.5.18
[324d7699] CategoricalArrays v0.7.3
[944b1d66] CodecZlib v0.6.0
[3da002f7] ColorTypes v0.8.0
[34da2185] Compat v2.2.0
[ed09eef8] ComputationalResources v0.3.0
[a8cc5b0e] Crayons v4.0.1
[9a962f9c] DataAPI v1.1.0
[a93c6f00] DataFrames v0.19.4
[864edb3b] DataStructures v0.17.6
[e2d170a0] DataValueInterfaces v1.0.0
[7806a523] DecisionTree v0.9.1
[b4f34e82] Distances v0.8.2
[31c24e10] Distributions v0.21.9
[ffbed154] DocStringExtensions v0.8.1
[8f5d6c58] EzXML v0.9.5
[5789e2e9] FileIO v1.1.0
[48062228] FilePathsBase v0.7.0
[1a297f60] FillArrays v0.8.2
[53c48c17] FixedPointNumbers v0.6.1
[59287772] Formatting v0.4.1
[41ab1584] InvertedIndices v1.0.0
[82899510] IteratorInterfaceExtensions v1.0.0
[682c06a0] JSON v0.21.0
[5078a376] LazyArrays v0.14.10
[7f8f8fb0] LearnBase v0.2.2
[30fc2ffe] LossFunctions v0.5.1
[add582a8] MLJ v0.5.5
[a7f614a8] MLJBase v0.8.4
[d491faf4] MLJModels v0.5.9
[1914dd2f] MacroTools v0.5.2
[e1d29d7a] Missings v0.4.3
[78c3b35d] Mocking v0.7.0
[6f286f6a] MultivariateStats v0.7.0
[b8a86587] NearestNeighbors v0.4.4
[bac558e1] OrderedCollections v1.1.0
[90014a1f] PDMats v0.9.10
[d96e819e] Parameters v0.12.0
[69de0a69] Parsers v0.3.10
[2dfb63ee] PooledArrays v0.5.2
[08abe8d2] PrettyTables v0.6.0
[92933f4c] ProgressMeter v1.2.0
[1fd47b50] QuadGK v2.1.1
[df47a6cb] RData v0.6.3
[ce6b1742] RDatasets v0.6.5
[3cdcf5f2] RecipesBase v0.7.0
[189a3867] Reexport v0.2.0
[ae029012] Requires v0.5.2
[79098fc4] Rmath v0.5.1
[321657f4] ScientificTypes v0.2.6
[6e75b9c4] ScikitLearnBase v0.5.0
[a2af1166] SortingAlgorithms v0.3.1
[276daf66] SpecialFunctions v0.8.0
[90137ffa] StaticArrays v0.12.1
[2913bbd2] StatsBase v0.32.0
[4c63d2b9] StatsFuns v0.9.0
[3783bdb8] TableTraits v1.0.0
[bd369af6] Tables v0.2.11
[f269a46b] TimeZones v0.10.3
[3bb67fe8] TranscodingStreams v0.9.5
[30578b45] URIParser v0.4.0
[b8865327] UnicodePlots v1.1.0
[ea10d353] WeakRefStrings v0.6.1
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[9fa8497b] Future [`@stdlib/Future`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[4607b0f0] SuiteSparse [`@stdlib/SuiteSparse`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
[ Info: Model metadata loaded from registry.
Test Summary: | Pass Total
utilities | 12 12
Test Summary: | Pass Total
parameters | 17 17
┌ Info: MLJModels.DecisionTree_.DecisionTreeRegressor does not support sample weights and the supplied weights will be ignored in training.
└ However, supplied weights will be passed to weight-supporting measures on calls to `evaluate!` and in tuning.
┌ Info: MLJModels.DecisionTree_.DecisionTreeRegressor does not support sample weights and the supplied weights will be ignored in training.
└ However, supplied weights will be passed to weight-supporting measures on calls to `evaluate!` and in tuning.
[ Info: Training [34mMachine{ConstantClassifier} @ 1…55[39m.
[ Info: Training [34mMachine{ConstantClassifier} @ 7…01[39m.
Test Summary: | Pass Total
Machines | 19 19
Training ensemble: 25%[============> ] ETA: 0:00:03[K
Training ensemble: 100%[==================================================] Time: 0:00:01[K
Test Summary: | Pass Total
networks | 37 37
[ Info: Training [34mNodalMachine{KNNRegressor} @ 2…35[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 1…50[39m.
[ Info: Training [34mNodalMachine{UnivariateBoxCoxTransformer} @ 6…22[39m.
[ Info: Training [34mNodalMachine{RidgeRegressor} @ 2…73[39m.
┌ Info: Not retraining [34mNodalMachine{Standardizer} @ 1…50[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
┌ Info: Not retraining [34mNodalMachine{UnivariateBoxCoxTransformer} @ 6…22[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
[ Info: Updating [34mNodalMachine{RidgeRegressor} @ 2…73[39m.
┌ Info: A model type "RidgeRegressor" is already loaded.
└ No new code loaded.
[ Info: Training [34mNodalMachine{Standardizer} @ 4…84[39m.
[ Info: Training [34mNodalMachine{PCA} @ 1…49[39m.
┌ Info: Not retraining [34mNodalMachine{Standardizer} @ 4…84[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
┌ Info: Not retraining [34mNodalMachine{PCA} @ 1…49[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
[ Info: Training [34mNodalMachine{RidgeRegressor} @ 2…68[39m.
┌ Info: A model type "PCA" is already loaded.
└ No new code loaded.
┌ Info: A model type "RidgeRegressor" is already loaded.
└ No new code loaded.
[ Info: Training [34mNodalMachine{Standardizer} @ 9…32[39m.
[ Info: Training [34mNodalMachine{PCA} @ 7…55[39m.
[ Info: Training [34mNodalMachine{RidgeRegressor} @ 2…91[39m.
┌ Info: A model type "PCA" is already loaded.
└ No new code loaded.
┌ Info: A model type "RidgeRegressor" is already loaded.
└ No new code loaded.
[ Info: Training [34mNodalMachine{Standardizer} @ 3…91[39m.
[ Info: Training [34mNodalMachine{PCA} @ 1…90[39m.
[ Info: Training [34mNodalMachine{UnivariateBoxCoxTransformer} @ 2…88[39m.
[ Info: Training [34mNodalMachine{RidgeRegressor} @ 8…86[39m.
[ Info: Training [34mNodalMachine{DecisionTreeRegressor} @ 4…08[39m.
[ Info: Training [34mNodalMachine{DecisionTreeRegressor} @ 7…09[39m.
Test Summary: | Pass Total
arrows | 12 12
[ Info: Training [34mNodalMachine{FeatureSelector} @ 5…98[39m.
[ Info: Training [34mNodalMachine{FooBarRegressor} @ 4…42[39m.
[ Info: Training [34mNodalMachine{SimpleDeterministicCompositeModel} @ 2…07[39m.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 2…30[39m.
[ Info: Training [34mNodalMachine{FooBarRegressor} @ 1…35[39m.
[ Info: Updating [34mNodalMachine{SimpleDeterministicCompositeModel} @ 2…07[39m.
[ Info: Updating [34mNodalMachine{FeatureSelector} @ 2…30[39m.
[ Info: Training [34mNodalMachine{FooBarRegressor} @ 1…35[39m.
[ Info: Training [34mNodalMachine{SimpleDeterministicCompositeModel} @ 2…07[39m.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 1…46[39m.
[ Info: Training [34mNodalMachine{FooBarRegressor} @ 5…61[39m.
[ Info: Training [34mMachine{WrappedRidge} @ 2…82[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 1…80[39m.
[ Info: Training [34mNodalMachine{UnivariateBoxCoxTransformer} @ 1…16[39m.
[ Info: Training [34mNodalMachine{FooBarRegressor} @ 1…59[39m.
[ Info: Updating [34mMachine{WrappedRidge} @ 2…82[39m.
┌ Info: Not retraining [34mNodalMachine{Standardizer} @ 1…80[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
┌ Info: Not retraining [34mNodalMachine{UnivariateBoxCoxTransformer} @ 1…16[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
[ Info: Updating [34mNodalMachine{FooBarRegressor} @ 1…59[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 8…06[39m.
[ Info: Spawning 3 sub-features to one-hot encode feature :x1.
[ Info: Training [34mNodalMachine{UnivariateStandardizer} @ 3…45[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 9…33[39m.
[ Info: Training [34mNodalMachine{DecisionTreeRegressor} @ 8…17[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 1…27[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 1…51[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 1…27[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 1…51[39m.
[ Info: Training [34mMachine{Composite3} @ 5…62[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 1…67[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 1…66[39m.
[ Info: Training [34mMachine{Composite3} @ 3…09[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 3…22[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 8…09[39m.
Test Summary: | Pass Total
composites | 71 71
[ Info: Training [34mNodalMachine{FeatureSelector} @ 1…50[39m.
[ Info: Training [34mNodalMachine{UnivariateStandardizer} @ 2…93[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 4…75[39m.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 1…58[39m.
[ Info: Training [34mNodalMachine{UnivariateStandardizer} @ 6…36[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 2…82[39m.
[ Info: Updating [34mNodalMachine{FeatureSelector} @ 1…50[39m.
┌ Info: Not retraining [34mNodalMachine{UnivariateStandardizer} @ 2…93[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 4…75[39m.
[ Info: Updating [34mNodalMachine{FeatureSelector} @ 1…58[39m.
┌ Info: Not retraining [34mNodalMachine{UnivariateStandardizer} @ 6…36[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 2…82[39m.
[ Info: Training [34mMachine{Pipe} @ 9…09[39m.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 7…41[39m.
[ Info: Training [34mNodalMachine{UnivariateStandardizer} @ 1…16[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 1…02[39m.
[ Info: Training [34mMachine{Pipe21} @ 2…32[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 1…05[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 1…58[39m.
[ Info: Training [34mMachine{Piper3} @ 6…54[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 1…38[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 8…20[39m.
[ Info: Training [34mMachine{Piper3} @ 1…79[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 4…08[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 7…49[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 5…96[39m.
[ Info: Spawning 3 sub-features to one-hot encode feature :x3.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 1…48[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 2…28[39m.
[ Info: Training [34mMachine{Pipe4} @ 3…53[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 7…17[39m.
[ Info: Spawning 3 sub-features to one-hot encode feature :x3.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 7…48[39m.
[ Info: Training [34mNodalMachine{StaticTransformer} @ 3…39[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 1…12[39m.
[ Info: Training [34mNodalMachine{StaticTransformer} @ 3…17[39m.
[ Info: Training [34mMachine{Pipe9} @ 5…28[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 9…02[39m.
[ Info: Spawning 2 sub-features to one-hot encode feature :gender.
[ Info: Training [34mNodalMachine{UnivariateStandardizer} @ 7…12[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 9…03[39m.
Test Summary: | Pass Total
pipelines | 133 133
Evaluating over 5 folds: 17%[====> ] ETA: 0:00:00[K
Evaluating over 5 folds: 33%[========> ] ETA: 0:00:02[K
Evaluating over 5 folds: 50%[============> ] ETA: 0:00:02[K
Evaluating over 5 folds: 67%[================> ] ETA: 0:00:01[K
Evaluating over 5 folds: 83%[====================> ] ETA: 0:00:00[K
Evaluating over 5 folds: 100%[=========================] Time: 0:00:01[K
┌─────────┬─────────────────────┐
│ measure │ measurement │
├─────────┼─────────────────────┤
│ my_rms │ 0.6 │
│ my_mav │ 0.6 │
│ rmslp1 │ 0.25095859542946747 │
└─────────┴─────────────────────┘
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:00[K
┌─────────┬─────────────────────┐
│ measure │ measurement │
├─────────┼─────────────────────┤
│ rms │ 0.6666666666666667 │
│ rmslp1 │ 0.25131442828090633 │
└─────────┴─────────────────────┘
Evaluating over 5 folds: 17%[====> ] ETA: 0:00:00[K
Evaluating over 5 folds: 33%[========> ] ETA: 0:00:00[K
Evaluating over 5 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 5 folds: 67%[================> ] ETA: 0:00:00[K
Evaluating over 5 folds: 83%[====================> ] ETA: 0:00:00[K
Evaluating over 5 folds: 100%[=========================] Time: 0:00:00[K
┌─────────┬─────────────────────┐
│ measure │ measurement │
├─────────┼─────────────────────┤
│ rms │ 0.6123724356957945 │
│ rmslp1 │ 0.25095859542946747 │
└─────────┴─────────────────────┘
Evaluating over 6 folds: 14%[===> ] ETA: 0:00:00[K
Evaluating over 6 folds: 43%[==========> ] ETA: 0:00:00[K
Evaluating over 6 folds: 71%[=================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 100%[=========================] Time: 0:00:00[K
[ Info: Updating [34mMachine{Resampler} @ 1…98[39m.
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:00[K
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:02[K
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:01[K
[ Info: Passing machine sample weights to any supported measures.
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:00[K
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:00[K
[ Info: Passing machine sample weights to any supported measures.
Evaluating over 6 folds: 14%[===> ] ETA: 0:00:00[K
Evaluating over 6 folds: 29%[=======> ] ETA: 0:00:08[K
Evaluating over 6 folds: 43%[==========> ] ETA: 0:00:04[K
Evaluating over 6 folds: 57%[==============> ] ETA: 0:00:03[K
Evaluating over 6 folds: 71%[=================> ] ETA: 0:00:01[K
Evaluating over 6 folds: 86%[=====================> ] ETA: 0:00:01[K
Evaluating over 6 folds: 100%[=========================] Time: 0:00:03[K
[ Info: Passing machine sample weights to any supported measures.
[ Info: Creating subsamples from a subset of all rows.
Evaluating over 6 folds: 14%[===> ] ETA: 0:00:00[K
Evaluating over 6 folds: 29%[=======> ] ETA: 0:00:00[K
Evaluating over 6 folds: 43%[==========> ] ETA: 0:00:00[K
Evaluating over 6 folds: 57%[==============> ] ETA: 0:00:00[K
Evaluating over 6 folds: 71%[=================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 86%[=====================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 100%[=========================] Time: 0:00:00[K
[ Info: Training [34mMachine{Resampler} @ 1…68[39m.
[ Info: Passing machine sample weights to any supported measures.
[ Info: Passing machine sample weights to any supported measures.
Evaluating over 6 folds: 14%[===> ] ETA: 0:00:00[K
Evaluating over 6 folds: 29%[=======> ] ETA: 0:00:00[K
Evaluating over 6 folds: 43%[==========> ] ETA: 0:00:00[K
Evaluating over 6 folds: 57%[==============> ] ETA: 0:00:00[K
Evaluating over 6 folds: 71%[=================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 86%[=====================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 100%[=========================] Time: 0:00:00[K
[ Info: Training [34mMachine{Resampler} @ 9…13[39m.
Evaluating over 6 folds: 14%[===> ] ETA: 0:00:00[K
Evaluating over 6 folds: 29%[=======> ] ETA: 0:00:00[K
Evaluating over 6 folds: 43%[==========> ] ETA: 0:00:00[K
Evaluating over 6 folds: 57%[==============> ] ETA: 0:00:00[K
Evaluating over 6 folds: 71%[=================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 86%[=====================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 100%[=========================] Time: 0:00:00[K
Test Summary: | Pass Total
resampling | 52 52
[ Info: Training [34mMachine{DeterministicTunedModel} @ 2…75[39m.
[ Info: Mimimizing rms.
Iterating over a 40-point grid: 2%[> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 7%[=> ] ETA: 0:00:47[K
Iterating over a 40-point grid: 10%[==> ] ETA: 0:00:44[K
Iterating over a 40-point grid: 12%[===> ] ETA: 0:00:38[K
Iterating over a 40-point grid: 15%[===> ] ETA: 0:00:31[K
Iterating over a 40-point grid: 17%[====> ] ETA: 0:00:26[K
Iterating over a 40-point grid: 20%[====> ] ETA: 0:00:22[K
Iterating over a 40-point grid: 22%[=====> ] ETA: 0:00:19[K
Iterating over a 40-point grid: 24%[======> ] ETA: 0:00:17[K
Iterating over a 40-point grid: 27%[======> ] ETA: 0:00:15[K
Iterating over a 40-point grid: 29%[=======> ] ETA: 0:00:13[K
Iterating over a 40-point grid: 32%[=======> ] ETA: 0:00:12[K
Iterating over a 40-point grid: 34%[========> ] ETA: 0:00:10[K
Iterating over a 40-point grid: 37%[=========> ] ETA: 0:00:09[K
Iterating over a 40-point grid: 39%[=========> ] ETA: 0:00:08[K
Iterating over a 40-point grid: 41%[==========> ] ETA: 0:00:08[K
Iterating over a 40-point grid: 44%[==========> ] ETA: 0:00:07[K
Iterating over a 40-point grid: 46%[===========> ] ETA: 0:00:06[K
Iterating over a 40-point grid: 49%[============> ] ETA: 0:00:06[K
Iterating over a 40-point grid: 51%[============> ] ETA: 0:00:05[K
Iterating over a 40-point grid: 54%[=============> ] ETA: 0:00:05[K
Iterating over a 40-point grid: 56%[==============> ] ETA: 0:00:04[K
Iterating over a 40-point grid: 59%[==============> ] ETA: 0:00:04[K
Iterating over a 40-point grid: 61%[===============> ] ETA: 0:00:03[K
Iterating over a 40-point grid: 63%[===============> ] ETA: 0:00:03[K
Iterating over a 40-point grid: 66%[================> ] ETA: 0:00:03[K
Iterating over a 40-point grid: 68%[=================> ] ETA: 0:00:03[K
Iterating over a 40-point grid: 71%[=================> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 73%[==================> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 76%[==================> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 78%[===================> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 80%[====================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 83%[====================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 85%[=====================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 88%[=====================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 90%[======================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 93%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 95%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 98%[========================>] ETA: 0:00:00[K
Iterating over a 40-point grid: 100%[=========================] Time: 0:00:05[K
[ Info: Training best model on all supplied data.
[ Info: Updating [34mMachine{DeterministicTunedModel} @ 2…75[39m.
[ Info: Mimimizing rms.
Iterating over a 40-point grid: 2%[> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 7%[=> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 10%[==> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 12%[===> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 15%[===> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 17%[====> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 20%[====> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 22%[=====> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 24%[======> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 27%[======> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 29%[=======> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 32%[=======> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 34%[========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 37%[=========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 39%[=========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 41%[==========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 44%[==========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 46%[===========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 49%[============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 51%[============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 54%[=============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 56%[==============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 59%[==============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 61%[===============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 63%[===============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 66%[================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 68%[=================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 71%[=================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 73%[==================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 76%[==================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 78%[===================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 80%[====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 83%[====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 85%[=====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 88%[=====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 90%[======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 93%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 95%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 98%[========================>] ETA: 0:00:00[K
Iterating over a 40-point grid: 100%[=========================] Time: 0:00:00[K
[ Info: Training best model on all supplied data.
[ Info: Updating [34mMachine{DeterministicTunedModel} @ 2…75[39m.
[ Info: Mimimizing rms.
Iterating over a 40-point grid: 2%[> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 7%[=> ] ETA: 0:00:05[K
Iterating over a 40-point grid: 10%[==> ] ETA: 0:00:03[K
Iterating over a 40-point grid: 12%[===> ] ETA: 0:00:03[K
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[ Info: Training best model on all supplied data.
┌ Info: A model type "KNNRegressor" is already loaded.
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[ Info: Training [34mMachine{DeterministicTunedModel} @ 7…26[39m.
[ Info: Mimimizing rms.
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[ Info: Training best model on all supplied data.
[ Info: Updating [34mMachine{DeterministicTunedModel} @ 7…26[39m.
[ Info: Mimimizing rms.
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[ Info: Training best model on all supplied data.
[ Info: Updating [34mMachine{DeterministicTunedModel} @ 7…26[39m.
┌ Warning: No resolution specified for forest.bagging_fraction. Will use a value of 5.
└ @ MLJ ~/.julia/packages/MLJ/LDDzK/src/tuning.jl:189
[ Info: Mimimizing rms.
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[ Info: Training best model on all supplied data.
┌ Warning: No measure specified. Setting measure=rms.
└ @ MLJ ~/.julia/packages/MLJ/LDDzK/src/machines.jl:148
[ Info: Training [34mMachine{DeterministicTunedModel} @ 6…66[39m.
[ Info: Mimimizing rms.
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[ Info: Training best model on all supplied data.
[ Info: Training [34mMachine{DeterministicTunedModel} @ 1…57[39m.
[ Info: Mimimizing rms.
atom.K=3 bagging_fraction=0.4 measurement=0.2973770670340338
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[ Info: Training best model on all supplied data.
[ Info: Training [34mMachine{DeterministicEnsembleModel{KNNRegressor}} @ 7…08[39m.
[ Info: Training [34mMachine{ProbabilisticTunedModel} @ 1…99[39m.
[ Info: Maximizing BrierScore(UnivariateFinite).
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[ Info: Training best model on all supplied data.
[ Info: Training [34mMachine{ProbabilisticTunedModel} @ 1…22[39m.
[ Info: Maximizing BrierScore(UnivariateFinite).
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[ Info: Training best model on all supplied data.
[ Info: Training [34mMachine{ProbabilisticTunedModel} @ 2…71[39m.
[ Info: Maximizing BrierScore(UnivariateFinite).
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[ Info: Training best model on all supplied data.
[ Info: Training [34mMachine{DeterministicTunedModel} @ 1…43[39m.
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Iterating over a 30-point grid: 97%[========================>] ETA: 0:00:00[K
Iterating over a 30-point grid: 100%[=========================] Time: 0:00:04[K
┌ Info: Training of best model suppressed.
└ To train tuning machine `mach` on all supplied data, call `fit!(mach.fitresult)`.
[ Info: Training [34mMachine{DeterministicTunedModel} @ 5…60[39m.
Iterating over a 30-point grid: 3%[> ] ETA: 0:00:00[K
Iterating over a 30-point grid: 10%[==> ] ETA: 0:00:00[K
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Iterating over a 30-point grid: 42%[==========> ] ETA: 0:00:00[K
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Iterating over a 30-point grid: 71%[=================> ] ETA: 0:00:00[K
Iterating over a 30-point grid: 74%[==================> ] ETA: 0:00:00[K
Iterating over a 30-point grid: 77%[===================> ] ETA: 0:00:00[K
Iterating over a 30-point grid: 81%[====================> ] ETA: 0:00:00[K
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Iterating over a 30-point grid: 87%[=====================> ] ETA: 0:00:00[K
Iterating over a 30-point grid: 90%[======================> ] ETA: 0:00:00[K
Iterating over a 30-point grid: 94%[=======================> ] ETA: 0:00:00[K
Iterating over a 30-point grid: 97%[========================>] ETA: 0:00:00[K
Iterating over a 30-point grid: 100%[=========================] Time: 0:00:00[K
┌ Info: Training of best model suppressed.
└ To train tuning machine `mach` on all supplied data, call `fit!(mach.fitresult)`.
Test Summary: | Pass Total
tuning | 18 18
Training ensemble: 50%[=========================> ] ETA: 0:00:01[K
Training ensemble: 100%[==================================================] Time: 0:00:00[K
Training ensemble: 20%[==========> ] ETA: 0:00:02[K
Training ensemble: 100%[==================================================] Time: 0:00:00[K
[ Info: Training [34mMachine{DeterministicEnsembleModel{KNNRegressor}} @ 1…26[39m.
Test Summary: | Pass Total
ensembles | 41 41
Test Summary: | Pass Total
matching models to data | 11 11
┌ Info:
│ is_probabilistic = true
│ input_scitype = ScientificTypes.Table{Union{AbstractArray{Count,1}, AbstractArray{Unknown,1}}}
└ target_scitype = AbstractArray{Count,1}
┌ Warning: Missing values encountered coercing scitype to Count.
│ Coerced to Union{Missing,Count} instead.
└ @ ScientificTypes ~/.julia/packages/ScientificTypes/XsivS/src/conventions/mlj/mlj.jl:5
┌ Info:
│ is_probabilistic = false
│ input_scitype = ScientificTypes.Table{Union{AbstractArray{Continuous,1}, AbstractArray{Multiclass{4},1}, AbstractArray{Union{Missing, Count},1}}}
└ target_scitype = AbstractArray{Count,1}
Test Summary: | Pass Total
tasks | 20 20
Test Summary: | Pass Total
scitypes | 3 3
Testing MLJ tests passed
Results with Julia v1.3.1-pre-7704df0a5a
Testing was successful .
Last evaluation was ago and took 12 minutes, 46 seconds.
Click here to download the log file.
Click here to show the log contents.
Resolving package versions...
Installed Tables ────────────────────── v0.2.11
Installed Formatting ────────────────── v0.4.1
Installed ColorTypes ────────────────── v0.8.0
Installed Compat ────────────────────── v2.2.0
Installed QuadGK ────────────────────── v2.1.1
Installed DataStructures ────────────── v0.17.6
Installed SpecialFunctions ──────────── v0.8.0
Installed MLJ ───────────────────────── v0.5.5
Installed BinDeps ───────────────────── v0.8.10
Installed LossFunctions ─────────────── v0.5.1
Installed URIParser ─────────────────── v0.4.0
Installed MLJBase ───────────────────── v0.8.4
Installed StatsBase ─────────────────── v0.32.0
Installed FixedPointNumbers ─────────── v0.6.1
Installed Missings ──────────────────── v0.4.3
Installed ComputationalResources ────── v0.3.0
Installed DocStringExtensions ───────── v0.8.1
Installed TableTraits ───────────────── v1.0.0
Installed BinaryProvider ────────────── v0.5.8
Installed StatsFuns ─────────────────── v0.9.0
Installed ScientificTypes ───────────── v0.2.6
Installed Arpack ────────────────────── v0.3.1
Installed Rmath ─────────────────────── v0.5.1
Installed ProgressMeter ─────────────── v1.2.0
Installed InvertedIndices ───────────── v1.0.0
Installed PrettyTables ──────────────── v0.6.0
Installed Requires ──────────────────── v0.5.2
Installed Parameters ────────────────── v0.12.0
Installed Crayons ───────────────────── v4.0.1
Installed Distances ─────────────────── v0.8.2
Installed MLJModels ─────────────────── v0.5.9
Installed DataValueInterfaces ───────── v1.0.0
Installed CategoricalArrays ─────────── v0.7.3
Installed Reexport ──────────────────── v0.2.0
Installed MultivariateStats ─────────── v0.7.0
Installed LearnBase ─────────────────── v0.2.2
Installed Distributions ─────────────── v0.21.9
Installed IteratorInterfaceExtensions ─ v1.0.0
Installed DataAPI ───────────────────── v1.1.0
Installed JSON ──────────────────────── v0.21.0
Installed RecipesBase ───────────────── v0.7.0
Installed PDMats ────────────────────── v0.9.10
Installed OrderedCollections ────────── v1.1.0
Installed Parsers ───────────────────── v0.3.10
Installed SortingAlgorithms ─────────── v0.3.1
Updating `~/.julia/environments/v1.3/Project.toml`
[add582a8] + MLJ v0.5.5
Updating `~/.julia/environments/v1.3/Manifest.toml`
[7d9fca2a] + Arpack v0.3.1
[9e28174c] + BinDeps v0.8.10
[b99e7846] + BinaryProvider v0.5.8
[324d7699] + CategoricalArrays v0.7.3
[3da002f7] + ColorTypes v0.8.0
[34da2185] + Compat v2.2.0
[ed09eef8] + ComputationalResources v0.3.0
[a8cc5b0e] + Crayons v4.0.1
[9a962f9c] + DataAPI v1.1.0
[864edb3b] + DataStructures v0.17.6
[e2d170a0] + DataValueInterfaces v1.0.0
[b4f34e82] + Distances v0.8.2
[31c24e10] + Distributions v0.21.9
[ffbed154] + DocStringExtensions v0.8.1
[53c48c17] + FixedPointNumbers v0.6.1
[59287772] + Formatting v0.4.1
[41ab1584] + InvertedIndices v1.0.0
[82899510] + IteratorInterfaceExtensions v1.0.0
[682c06a0] + JSON v0.21.0
[7f8f8fb0] + LearnBase v0.2.2
[30fc2ffe] + LossFunctions v0.5.1
[add582a8] + MLJ v0.5.5
[a7f614a8] + MLJBase v0.8.4
[d491faf4] + MLJModels v0.5.9
[e1d29d7a] + Missings v0.4.3
[6f286f6a] + MultivariateStats v0.7.0
[bac558e1] + OrderedCollections v1.1.0
[90014a1f] + PDMats v0.9.10
[d96e819e] + Parameters v0.12.0
[69de0a69] + Parsers v0.3.10
[08abe8d2] + PrettyTables v0.6.0
[92933f4c] + ProgressMeter v1.2.0
[1fd47b50] + QuadGK v2.1.1
[3cdcf5f2] + RecipesBase v0.7.0
[189a3867] + Reexport v0.2.0
[ae029012] + Requires v0.5.2
[79098fc4] + Rmath v0.5.1
[321657f4] + ScientificTypes v0.2.6
[a2af1166] + SortingAlgorithms v0.3.1
[276daf66] + SpecialFunctions v0.8.0
[2913bbd2] + StatsBase v0.32.0
[4c63d2b9] + StatsFuns v0.9.0
[3783bdb8] + TableTraits v1.0.0
[bd369af6] + Tables v0.2.11
[30578b45] + URIParser v0.4.0
[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 SpecialFunctions → `~/.julia/packages/SpecialFunctions/ne2iw/deps/build.log`
Building Arpack ──────────→ `~/.julia/packages/Arpack/cu5By/deps/build.log`
Building Rmath ───────────→ `~/.julia/packages/Rmath/4wt82/deps/build.log`
Testing MLJ
Resolving package versions...
Installed WeakRefStrings ───── v0.6.1
Installed NearestNeighbors ─── v0.4.4
Installed RData ────────────── v0.6.3
Installed UnicodePlots ─────── v1.1.0
Installed TimeZones ────────── v0.10.3
Installed ArrayLayouts ─────── v0.1.5
Installed DecisionTree ─────── v0.9.1
Installed LazyArrays ───────── v0.14.10
Installed MacroTools ───────── v0.5.2
Installed StaticArrays ─────── v0.12.1
Installed DataFrames ───────── v0.19.4
Installed FillArrays ───────── v0.8.2
Installed Mocking ──────────── v0.7.0
Installed CodecZlib ────────── v0.6.0
Installed PooledArrays ─────── v0.5.2
Installed FileIO ───────────── v1.1.0
Installed RDatasets ────────── v0.6.5
Installed TranscodingStreams ─ v0.9.5
Installed FilePathsBase ────── v0.7.0
Installed EzXML ────────────── v0.9.5
Installed ScikitLearnBase ──── v0.5.0
Installed CSV ──────────────── v0.5.18
Building EzXML ────→ `~/.julia/packages/EzXML/QtGgF/deps/build.log`
Building TimeZones → `~/.julia/packages/TimeZones/pjvlM/deps/build.log`
Building CodecZlib → `~/.julia/packages/CodecZlib/5t9zO/deps/build.log`
Status `/tmp/jl_3ESQCx/Manifest.toml`
[7d9fca2a] Arpack v0.3.1
[4c555306] ArrayLayouts v0.1.5
[9e28174c] BinDeps v0.8.10
[b99e7846] BinaryProvider v0.5.8
[336ed68f] CSV v0.5.18
[324d7699] CategoricalArrays v0.7.3
[944b1d66] CodecZlib v0.6.0
[3da002f7] ColorTypes v0.8.0
[34da2185] Compat v2.2.0
[ed09eef8] ComputationalResources v0.3.0
[a8cc5b0e] Crayons v4.0.1
[9a962f9c] DataAPI v1.1.0
[a93c6f00] DataFrames v0.19.4
[864edb3b] DataStructures v0.17.6
[e2d170a0] DataValueInterfaces v1.0.0
[7806a523] DecisionTree v0.9.1
[b4f34e82] Distances v0.8.2
[31c24e10] Distributions v0.21.9
[ffbed154] DocStringExtensions v0.8.1
[8f5d6c58] EzXML v0.9.5
[5789e2e9] FileIO v1.1.0
[48062228] FilePathsBase v0.7.0
[1a297f60] FillArrays v0.8.2
[53c48c17] FixedPointNumbers v0.6.1
[59287772] Formatting v0.4.1
[41ab1584] InvertedIndices v1.0.0
[82899510] IteratorInterfaceExtensions v1.0.0
[682c06a0] JSON v0.21.0
[5078a376] LazyArrays v0.14.10
[7f8f8fb0] LearnBase v0.2.2
[30fc2ffe] LossFunctions v0.5.1
[add582a8] MLJ v0.5.5
[a7f614a8] MLJBase v0.8.4
[d491faf4] MLJModels v0.5.9
[1914dd2f] MacroTools v0.5.2
[e1d29d7a] Missings v0.4.3
[78c3b35d] Mocking v0.7.0
[6f286f6a] MultivariateStats v0.7.0
[b8a86587] NearestNeighbors v0.4.4
[bac558e1] OrderedCollections v1.1.0
[90014a1f] PDMats v0.9.10
[d96e819e] Parameters v0.12.0
[69de0a69] Parsers v0.3.10
[2dfb63ee] PooledArrays v0.5.2
[08abe8d2] PrettyTables v0.6.0
[92933f4c] ProgressMeter v1.2.0
[1fd47b50] QuadGK v2.1.1
[df47a6cb] RData v0.6.3
[ce6b1742] RDatasets v0.6.5
[3cdcf5f2] RecipesBase v0.7.0
[189a3867] Reexport v0.2.0
[ae029012] Requires v0.5.2
[79098fc4] Rmath v0.5.1
[321657f4] ScientificTypes v0.2.6
[6e75b9c4] ScikitLearnBase v0.5.0
[a2af1166] SortingAlgorithms v0.3.1
[276daf66] SpecialFunctions v0.8.0
[90137ffa] StaticArrays v0.12.1
[2913bbd2] StatsBase v0.32.0
[4c63d2b9] StatsFuns v0.9.0
[3783bdb8] TableTraits v1.0.0
[bd369af6] Tables v0.2.11
[f269a46b] TimeZones v0.10.3
[3bb67fe8] TranscodingStreams v0.9.5
[30578b45] URIParser v0.4.0
[b8865327] UnicodePlots v1.1.0
[ea10d353] WeakRefStrings v0.6.1
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[9fa8497b] Future [`@stdlib/Future`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[4607b0f0] SuiteSparse [`@stdlib/SuiteSparse`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
[ Info: Model metadata loaded from registry.
Test Summary: | Pass Total
utilities | 12 12
Test Summary: | Pass Total
parameters | 17 17
┌ Info: MLJModels.DecisionTree_.DecisionTreeRegressor does not support sample weights and the supplied weights will be ignored in training.
└ However, supplied weights will be passed to weight-supporting measures on calls to `evaluate!` and in tuning.
┌ Info: MLJModels.DecisionTree_.DecisionTreeRegressor does not support sample weights and the supplied weights will be ignored in training.
└ However, supplied weights will be passed to weight-supporting measures on calls to `evaluate!` and in tuning.
[ Info: Training [34mMachine{ConstantClassifier} @ 8…91[39m.
[ Info: Training [34mMachine{ConstantClassifier} @ 7…09[39m.
Test Summary: | Pass Total
Machines | 19 19
Training ensemble: 25%[============> ] ETA: 0:00:03[K
Training ensemble: 100%[==================================================] Time: 0:00:01[K
Test Summary: | Pass Total
networks | 37 37
[ Info: Training [34mNodalMachine{KNNRegressor} @ 4…72[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 8…33[39m.
[ Info: Training [34mNodalMachine{UnivariateBoxCoxTransformer} @ 3…29[39m.
[ Info: Training [34mNodalMachine{RidgeRegressor} @ 6…53[39m.
┌ Info: Not retraining [34mNodalMachine{Standardizer} @ 8…33[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
┌ Info: Not retraining [34mNodalMachine{UnivariateBoxCoxTransformer} @ 3…29[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
[ Info: Updating [34mNodalMachine{RidgeRegressor} @ 6…53[39m.
┌ Info: A model type "RidgeRegressor" is already loaded.
└ No new code loaded.
[ Info: Training [34mNodalMachine{Standardizer} @ 1…68[39m.
[ Info: Training [34mNodalMachine{PCA} @ 1…73[39m.
┌ Info: Not retraining [34mNodalMachine{Standardizer} @ 1…68[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
┌ Info: Not retraining [34mNodalMachine{PCA} @ 1…73[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
[ Info: Training [34mNodalMachine{RidgeRegressor} @ 1…69[39m.
┌ Info: A model type "PCA" is already loaded.
└ No new code loaded.
┌ Info: A model type "RidgeRegressor" is already loaded.
└ No new code loaded.
[ Info: Training [34mNodalMachine{Standardizer} @ 1…19[39m.
[ Info: Training [34mNodalMachine{PCA} @ 7…84[39m.
[ Info: Training [34mNodalMachine{RidgeRegressor} @ 4…82[39m.
┌ Info: A model type "PCA" is already loaded.
└ No new code loaded.
┌ Info: A model type "RidgeRegressor" is already loaded.
└ No new code loaded.
[ Info: Training [34mNodalMachine{Standardizer} @ 1…20[39m.
[ Info: Training [34mNodalMachine{PCA} @ 6…30[39m.
[ Info: Training [34mNodalMachine{UnivariateBoxCoxTransformer} @ 1…68[39m.
[ Info: Training [34mNodalMachine{RidgeRegressor} @ 1…24[39m.
[ Info: Training [34mNodalMachine{DecisionTreeRegressor} @ 1…07[39m.
[ Info: Training [34mNodalMachine{DecisionTreeRegressor} @ 1…46[39m.
Test Summary: | Pass Total
arrows | 12 12
[ Info: Training [34mNodalMachine{FeatureSelector} @ 1…69[39m.
[ Info: Training [34mNodalMachine{FooBarRegressor} @ 1…08[39m.
[ Info: Training [34mNodalMachine{SimpleDeterministicCompositeModel} @ 8…46[39m.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 1…92[39m.
[ Info: Training [34mNodalMachine{FooBarRegressor} @ 6…18[39m.
[ Info: Updating [34mNodalMachine{SimpleDeterministicCompositeModel} @ 8…46[39m.
[ Info: Updating [34mNodalMachine{FeatureSelector} @ 1…92[39m.
[ Info: Training [34mNodalMachine{FooBarRegressor} @ 6…18[39m.
[ Info: Training [34mNodalMachine{SimpleDeterministicCompositeModel} @ 8…46[39m.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 8…56[39m.
[ Info: Training [34mNodalMachine{FooBarRegressor} @ 7…36[39m.
[ Info: Training [34mMachine{WrappedRidge} @ 1…79[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 8…77[39m.
[ Info: Training [34mNodalMachine{UnivariateBoxCoxTransformer} @ 9…48[39m.
[ Info: Training [34mNodalMachine{FooBarRegressor} @ 1…57[39m.
[ Info: Updating [34mMachine{WrappedRidge} @ 1…79[39m.
┌ Info: Not retraining [34mNodalMachine{Standardizer} @ 8…77[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
┌ Info: Not retraining [34mNodalMachine{UnivariateBoxCoxTransformer} @ 9…48[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
[ Info: Updating [34mNodalMachine{FooBarRegressor} @ 1…57[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 1…46[39m.
[ Info: Spawning 3 sub-features to one-hot encode feature :x1.
[ Info: Training [34mNodalMachine{UnivariateStandardizer} @ 6…55[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 4…08[39m.
[ Info: Training [34mNodalMachine{DecisionTreeRegressor} @ 1…85[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 8…56[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 1…34[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 8…56[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 1…34[39m.
[ Info: Training [34mMachine{Composite3} @ 4…05[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 8…59[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 1…13[39m.
[ Info: Training [34mMachine{Composite3} @ 1…50[39m.
[ Info: Training [34mNodalMachine{Standardizer} @ 4…41[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 1…77[39m.
Test Summary: | Pass Total
composites | 71 71
[ Info: Training [34mNodalMachine{FeatureSelector} @ 1…48[39m.
[ Info: Training [34mNodalMachine{UnivariateStandardizer} @ 1…15[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 7…96[39m.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 1…80[39m.
[ Info: Training [34mNodalMachine{UnivariateStandardizer} @ 1…93[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 1…98[39m.
[ Info: Updating [34mNodalMachine{FeatureSelector} @ 1…48[39m.
┌ Info: Not retraining [34mNodalMachine{UnivariateStandardizer} @ 1…15[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 7…96[39m.
[ Info: Updating [34mNodalMachine{FeatureSelector} @ 1…80[39m.
┌ Info: Not retraining [34mNodalMachine{UnivariateStandardizer} @ 1…93[39m.
└ It appears up-to-date. Use `force=true` to force retraining.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 1…98[39m.
[ Info: Training [34mMachine{Pipe} @ 1…14[39m.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 1…63[39m.
[ Info: Training [34mNodalMachine{UnivariateStandardizer} @ 6…19[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 7…77[39m.
[ Info: Training [34mMachine{Pipe21} @ 1…70[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 3…12[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 1…09[39m.
[ Info: Training [34mMachine{Piper3} @ 1…05[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 8…85[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 1…81[39m.
[ Info: Training [34mMachine{Piper3} @ 4…85[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 1…82[39m.
[ Info: Training [34mNodalMachine{ConstantClassifier} @ 1…55[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 1…79[39m.
[ Info: Spawning 3 sub-features to one-hot encode feature :x3.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 1…10[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 1…75[39m.
[ Info: Training [34mMachine{Pipe4} @ 1…48[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 1…53[39m.
[ Info: Spawning 3 sub-features to one-hot encode feature :x3.
[ Info: Training [34mNodalMachine{FeatureSelector} @ 5…61[39m.
[ Info: Training [34mNodalMachine{StaticTransformer} @ 6…28[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 3…19[39m.
[ Info: Training [34mNodalMachine{StaticTransformer} @ 1…09[39m.
[ Info: Training [34mMachine{Pipe9} @ 7…03[39m.
[ Info: Training [34mNodalMachine{OneHotEncoder} @ 7…56[39m.
[ Info: Spawning 2 sub-features to one-hot encode feature :gender.
[ Info: Training [34mNodalMachine{UnivariateStandardizer} @ 3…68[39m.
[ Info: Training [34mNodalMachine{KNNRegressor} @ 2…46[39m.
Test Summary: | Pass Total
pipelines | 133 133
Evaluating over 5 folds: 17%[====> ] ETA: 0:00:00[K
Evaluating over 5 folds: 33%[========> ] ETA: 0:00:03[K
Evaluating over 5 folds: 50%[============> ] ETA: 0:00:02[K
Evaluating over 5 folds: 67%[================> ] ETA: 0:00:01[K
Evaluating over 5 folds: 83%[====================> ] ETA: 0:00:00[K
Evaluating over 5 folds: 100%[=========================] Time: 0:00:01[K
┌─────────┬─────────────────────┐
│ measure │ measurement │
├─────────┼─────────────────────┤
│ my_rms │ 0.6 │
│ my_mav │ 0.6 │
│ rmslp1 │ 0.25095859542946747 │
└─────────┴─────────────────────┘
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:00[K
┌─────────┬─────────────────────┐
│ measure │ measurement │
├─────────┼─────────────────────┤
│ rms │ 0.6666666666666667 │
│ rmslp1 │ 0.25131442828090633 │
└─────────┴─────────────────────┘
Evaluating over 5 folds: 17%[====> ] ETA: 0:00:00[K
Evaluating over 5 folds: 33%[========> ] ETA: 0:00:00[K
Evaluating over 5 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 5 folds: 67%[================> ] ETA: 0:00:00[K
Evaluating over 5 folds: 83%[====================> ] ETA: 0:00:00[K
Evaluating over 5 folds: 100%[=========================] Time: 0:00:00[K
┌─────────┬─────────────────────┐
│ measure │ measurement │
├─────────┼─────────────────────┤
│ rms │ 0.6123724356957945 │
│ rmslp1 │ 0.25095859542946747 │
└─────────┴─────────────────────┘
Evaluating over 6 folds: 14%[===> ] ETA: 0:00:00[K
Evaluating over 6 folds: 29%[=======> ] ETA: 0:00:00[K
Evaluating over 6 folds: 43%[==========> ] ETA: 0:00:00[K
Evaluating over 6 folds: 57%[==============> ] ETA: 0:00:00[K
Evaluating over 6 folds: 71%[=================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 86%[=====================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 100%[=========================] Time: 0:00:00[K
[ Info: Updating [34mMachine{Resampler} @ 8…78[39m.
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:00[K
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:02[K
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:01[K
[ Info: Passing machine sample weights to any supported measures.
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:00[K
Evaluating over 1 folds: 50%[============> ] ETA: 0:00:00[K
Evaluating over 1 folds: 100%[=========================] Time: 0:00:00[K
[ Info: Passing machine sample weights to any supported measures.
Evaluating over 6 folds: 14%[===> ] ETA: 0:00:00[K
Evaluating over 6 folds: 29%[=======> ] ETA: 0:00:08[K
Evaluating over 6 folds: 43%[==========> ] ETA: 0:00:04[K
Evaluating over 6 folds: 57%[==============> ] ETA: 0:00:02[K
Evaluating over 6 folds: 71%[=================> ] ETA: 0:00:01[K
Evaluating over 6 folds: 86%[=====================> ] ETA: 0:00:01[K
Evaluating over 6 folds: 100%[=========================] Time: 0:00:03[K
[ Info: Passing machine sample weights to any supported measures.
[ Info: Creating subsamples from a subset of all rows.
Evaluating over 6 folds: 14%[===> ] ETA: 0:00:00[K
Evaluating over 6 folds: 29%[=======> ] ETA: 0:00:00[K
Evaluating over 6 folds: 43%[==========> ] ETA: 0:00:00[K
Evaluating over 6 folds: 57%[==============> ] ETA: 0:00:00[K
Evaluating over 6 folds: 71%[=================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 86%[=====================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 100%[=========================] Time: 0:00:00[K
[ Info: Training [34mMachine{Resampler} @ 7…06[39m.
[ Info: Passing machine sample weights to any supported measures.
[ Info: Passing machine sample weights to any supported measures.
Evaluating over 6 folds: 14%[===> ] ETA: 0:00:00[K
Evaluating over 6 folds: 29%[=======> ] ETA: 0:00:00[K
Evaluating over 6 folds: 43%[==========> ] ETA: 0:00:00[K
Evaluating over 6 folds: 57%[==============> ] ETA: 0:00:00[K
Evaluating over 6 folds: 71%[=================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 86%[=====================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 100%[=========================] Time: 0:00:00[K
[ Info: Training [34mMachine{Resampler} @ 2…75[39m.
Evaluating over 6 folds: 14%[===> ] ETA: 0:00:00[K
Evaluating over 6 folds: 29%[=======> ] ETA: 0:00:00[K
Evaluating over 6 folds: 43%[==========> ] ETA: 0:00:00[K
Evaluating over 6 folds: 57%[==============> ] ETA: 0:00:00[K
Evaluating over 6 folds: 71%[=================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 86%[=====================> ] ETA: 0:00:00[K
Evaluating over 6 folds: 100%[=========================] Time: 0:00:00[K
Test Summary: | Pass Total
resampling | 52 52
[ Info: Training [34mMachine{DeterministicTunedModel} @ 5…02[39m.
[ Info: Mimimizing rms.
Iterating over a 40-point grid: 2%[> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 7%[=> ] ETA: 0:00:52[K
Iterating over a 40-point grid: 10%[==> ] ETA: 0:00:45[K
Iterating over a 40-point grid: 12%[===> ] ETA: 0:00:39[K
Iterating over a 40-point grid: 15%[===> ] ETA: 0:00:32[K
Iterating over a 40-point grid: 17%[====> ] ETA: 0:00:27[K
Iterating over a 40-point grid: 20%[====> ] ETA: 0:00:23[K
Iterating over a 40-point grid: 22%[=====> ] ETA: 0:00:20[K
Iterating over a 40-point grid: 24%[======> ] ETA: 0:00:17[K
Iterating over a 40-point grid: 27%[======> ] ETA: 0:00:15[K
Iterating over a 40-point grid: 29%[=======> ] ETA: 0:00:13[K
Iterating over a 40-point grid: 32%[=======> ] ETA: 0:00:12[K
Iterating over a 40-point grid: 34%[========> ] ETA: 0:00:11[K
Iterating over a 40-point grid: 37%[=========> ] ETA: 0:00:10[K
Iterating over a 40-point grid: 39%[=========> ] ETA: 0:00:09[K
Iterating over a 40-point grid: 41%[==========> ] ETA: 0:00:08[K
Iterating over a 40-point grid: 44%[==========> ] ETA: 0:00:07[K
Iterating over a 40-point grid: 46%[===========> ] ETA: 0:00:06[K
Iterating over a 40-point grid: 49%[============> ] ETA: 0:00:06[K
Iterating over a 40-point grid: 51%[============> ] ETA: 0:00:05[K
Iterating over a 40-point grid: 54%[=============> ] ETA: 0:00:05[K
Iterating over a 40-point grid: 56%[==============> ] ETA: 0:00:04[K
Iterating over a 40-point grid: 59%[==============> ] ETA: 0:00:04[K
Iterating over a 40-point grid: 61%[===============> ] ETA: 0:00:04[K
Iterating over a 40-point grid: 63%[===============> ] ETA: 0:00:03[K
Iterating over a 40-point grid: 66%[================> ] ETA: 0:00:03[K
Iterating over a 40-point grid: 68%[=================> ] ETA: 0:00:03[K
Iterating over a 40-point grid: 71%[=================> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 73%[==================> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 76%[==================> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 78%[===================> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 80%[====================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 83%[====================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 85%[=====================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 88%[=====================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 90%[======================> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 93%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 95%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 98%[========================>] ETA: 0:00:00[K
Iterating over a 40-point grid: 100%[=========================] Time: 0:00:05[K
[ Info: Training best model on all supplied data.
[ Info: Updating [34mMachine{DeterministicTunedModel} @ 5…02[39m.
[ Info: Mimimizing rms.
Iterating over a 40-point grid: 2%[> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 7%[=> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 10%[==> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 12%[===> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 15%[===> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 17%[====> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 20%[====> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 22%[=====> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 24%[======> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 27%[======> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 29%[=======> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 32%[=======> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 34%[========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 37%[=========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 39%[=========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 41%[==========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 44%[==========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 46%[===========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 49%[============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 51%[============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 54%[=============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 56%[==============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 59%[==============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 61%[===============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 63%[===============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 66%[================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 68%[=================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 71%[=================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 73%[==================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 76%[==================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 78%[===================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 80%[====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 83%[====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 85%[=====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 88%[=====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 90%[======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 93%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 95%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 98%[========================>] ETA: 0:00:00[K
Iterating over a 40-point grid: 100%[=========================] Time: 0:00:00[K
[ Info: Training best model on all supplied data.
[ Info: Updating [34mMachine{DeterministicTunedModel} @ 5…02[39m.
[ Info: Mimimizing rms.
Iterating over a 40-point grid: 2%[> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 7%[=> ] ETA: 0:00:05[K
Iterating over a 40-point grid: 10%[==> ] ETA: 0:00:04[K
Iterating over a 40-point grid: 12%[===> ] ETA: 0:00:03[K
Iterating over a 40-point grid: 15%[===> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 17%[====> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 20%[====> ] ETA: 0:00:02[K
Iterating over a 40-point grid: 22%[=====> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 24%[======> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 27%[======> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 29%[=======> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 32%[=======> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 34%[========> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 37%[=========> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 39%[=========> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 41%[==========> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 44%[==========> ] ETA: 0:00:01[K
Iterating over a 40-point grid: 46%[===========> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 49%[============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 51%[============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 54%[=============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 56%[==============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 59%[==============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 61%[===============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 63%[===============> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 66%[================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 68%[=================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 71%[=================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 73%[==================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 76%[==================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 78%[===================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 80%[====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 83%[====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 85%[=====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 88%[=====================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 90%[======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 93%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 95%[=======================> ] ETA: 0:00:00[K
Iterating over a 40-point grid: 98%[========================>] ETA: 0:00:00[K
Iterating over a 40-point grid: 100%[=========================] Time: 0:00:00[K
[ Info: Training best model on all supplied data.
┌ Info: A model type "KNNRegressor" is already loaded.
└ No new code loaded.
[ Info: Training [34mMachine{DeterministicTunedModel} @ 8…59[39m.
[ Info: Mimimizing rms.
Iterating over a 72-point grid: 1%[> ] ETA: 0:00:00[K
Iterating over a 72-point grid: 4%[=> ] ETA: 0:02:56[K
Iterating over a 72-point grid: 5%[=> ] ETA: 0:02:45[K
Iterating over a 72-point grid: 7%[=> ] ETA: 0:02:11[K
Iterating over a 72-point grid: 8%[==> ] ETA: 0:01:48[K
Iterating over a 72-point grid: 10%[==> ] ETA: 0:01:32[K
Iterating over a 72-point grid: 11%[==> ] ETA: 0:01:19[K
Iterating over a 72-point grid: 12%[===> ] ETA: 0:01:10[K
Iterating over a 72-point grid: 14%[===> ] ETA: 0:01:02[K
Iterating over a 72-point grid: 15%[===> ] ETA: 0:00:56[K
Iterating over a 72-point grid: 16%[====> ] ETA: 0:00:50[K
Iterating over a 72-point grid: 18%[====> ] ETA: 0:00:46[K
Iterating over a 72-point grid: 19%[====> ] ETA: 0:00:42[K
Iterating over a 72-point grid: 21%[=====> ] ETA: 0:00:39[K
Iterating over a 72-point grid: 22%[=====> ] ETA: 0:00:36[K
Iterating over a 72-point grid: 23%[=====> ] ETA: 0:00:33[K
Iterating over a 72-point grid: 25%[======> ] ETA: 0:00:31[K
Iterating over a 72-point grid: 26%[======> ] ETA: 0:00:29[K
Iterating over a 72-point grid: 27%[======> ] ETA: 0:00:27[K
Iterating over a 72-point grid: 29%[=======> ] ETA: 0:00:25[K
Iterating over a 72-point grid: 30%[=======> ] ETA: 0:00:24[K
Iterating over a 72-point grid: 32%[=======> ] ETA: 0:00:23[K
Iterating over a 72-point grid: 33%[========> ] ETA: 0:00:21[K
Iterating over a 72-point grid: 34%[========> ] ETA: 0:00:20[K
Iterating over a 72-point grid: 36%[========> ] ETA: 0:00:19[K
Iterating over a 72-point grid: 37%[=========> ] ETA: 0:00:18[K
Iterating over a 72-point grid: 38%[=========> ] ETA: 0:00:17[K
Iterating over a 72-point grid: 40%[=========> ] ETA: 0:00:16[K
Iterating over a 72-point grid: 41%[==========> ] ETA: 0:00:15[K
Iterating over a 72-point grid: 42%[==========> ] ETA: 0:00:14[K
Iterating over a 72-point grid: 44%[==========> ] ETA: 0:00:14[K
Iterating over a 72-point grid: 45%[===========> ] ETA: 0:00:13[K
Iterating over a 72-point grid: 47%[===========> ] ETA: 0:00:12[K
Iterating over a 72-point grid: 48%[===========> ] ETA: 0:00:12[K
Iterating over a 72-point grid: 49%[============> ] ETA: 0:00:11[K
Iterating over a 72-point grid: 51%[============> ] ETA: 0:00:11[K
Iterating over a 72-point grid: 52%[=============> ] ETA: 0:00:10[K
Iterating over a 72-point grid: 53%[=============> ] ETA: 0:00:10[K
Iterating over a 72-point grid: 55%[=============> ] ETA: 0:00:09[K
Iterating over a 72-point grid: 56%[==============> ] ETA: 0:00:09[K
Iterating over a 72-point grid: 58%[==============> ] ETA: 0:00:08[K
Iterating over a 72-point grid: 59%[==============> ] ETA: 0:00:08[K
Iterating over a 72-point grid: 60%[===============> ] ETA: 0:00:07[K
Iterating over a 72-point grid: 62%[===============> ] ETA: 0:00:07[K
Iterating over a 72-point grid: 63%[===============> ] ETA: 0:00:07[K
Iterating over a 72-point grid: 64%[================> ] ETA: 0:00:06[K
Iterating over a 72-point grid: 66%[================> ] ETA: 0:00:06[K
Iterating over a 72-point grid: 67%[================> ] ETA: 0:00:06[K
Iterating over a 72-point grid: 68%[=================> ] ETA: 0:00:05[K
Iterating over a 72-point grid: 70%[=================> ] ETA: 0:00:05[K
Iterating over a 72-point grid: 71%[=================> ] ETA: 0:00:05[K
Iterating over a 72-point grid: 73%[==================> ] ETA: 0:00:04[K
Iterating over a 72-point grid: 74%[==================> ] ETA: 0:00:04[K
Iterating over a 72-point grid: 75%[==================> ] ETA: 0:00:04[K
Iterating over a 72-point grid: 77%[===================> ] ETA: 0:00:04[K
Iterating over a 72-point grid: 78%[===================> ] ETA: 0:00:03[K
Iterating over a 72-point grid: 79%[===================> ] ETA: 0:00:03[K
Iterating over a 72-point grid: 81%[====================> ] ETA: 0:00:03[K
Iterating over a 72-point grid: 82%[====================> ] ETA: 0:00:03[K
Iterating over a 72-point grid: 84%[====================> ] ETA: 0:00:02[K
Iterating over a 72-point grid: 85%[=====================> ] ETA: 0:00:02[K
Iterating over a 72-point grid: 86%[=====================> ] ETA: 0:00:02[K
Iterating over a 72-point grid: 88%[=====================> ] ETA: 0:00:02[K
Iterating over a 72-point grid: 89%[======================> ] ETA: 0:00:02[K
Iterating over a 72-point grid: 90%[======================> ] ETA: 0:00:01[K
Iterating over a 72-point grid: 92%[======================> ] ETA: 0:00:01[K
Iterating over a 72-point grid: 93%[=======================> ] ETA: 0:00:01[K
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[ Info: Training best model on all supplied data.
[ Info: Updating [34mMachine{DeterministicTunedModel} @ 8…59[39m.
[ Info: Mimimizing rms.
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[ Info: Training best model on all supplied data.
[ Info: Updating [34mMachine{DeterministicTunedModel} @ 8…59[39m.
┌ Warning: No resolution specified for forest.bagging_fraction. Will use a value of 5.
└ @ MLJ ~/.julia/packages/MLJ/LDDzK/src/tuning.jl:189
[ Info: Mimimizing rms.
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[ Info: Training best model on all supplied data.
┌ Warning: No measure specified. Setting measure=rms.
└ @ MLJ ~/.julia/packages/MLJ/LDDzK/src/machines.jl:148
[ Info: Training [34mMachine{DeterministicTunedModel} @ 1…05[39m.
[ Info: Mimimizing rms.
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[ Info: Training best model on all supplied data.
[ Info: Training [34mMachine{DeterministicTunedModel} @ 1…90[39m.
[ Info: Mimimizing rms.
atom.K=3 bagging_fraction=0.4 measurement=0.2973770670340338
atom.K=4 bagging_fraction=0.4 measurement=0.29487431347616055
atom.K=3 bagging_fraction=0.7 measurement=0.3053084971552806
atom.K=4 bagging_fraction=0.7 measurement=0.3007352876810562
atom.K=3 bagging_fraction=1.0 measurement=0.322315125056536
atom.K=4 bagging_fraction=1.0 measurement=0.31259942522260187
[ Info: Training best model on all supplied data.
[ Info: Training [34mMachine{DeterministicEnsembleModel{KNNRegressor}} @ 1…86[39m.
[ Info: Training [34mMachine{ProbabilisticTunedModel} @ 1…86[39m.
[ Info: Maximizing BrierScore(UnivariateFinite).
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[ Info: Training best model on all supplied data.
[ Info: Training [34mMachine{ProbabilisticTunedModel} @ 3…44[39m.
[ Info: Maximizing BrierScore(UnivariateFinite).
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[ Info: Training best model on all supplied data.
[ Info: Training [34mMachine{ProbabilisticTunedModel} @ 7…23[39m.
[ Info: Maximizing BrierScore(UnivariateFinite).
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[ Info: Training best model on all supplied data.
[ Info: Training [34mMachine{DeterministicTunedModel} @ 1…27[39m.
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┌ Info: Training of best model suppressed.
└ To train tuning machine `mach` on all supplied data, call `fit!(mach.fitresult)`.
[ Info: Training [34mMachine{DeterministicTunedModel} @ 1…33[39m.
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┌ Info: Training of best model suppressed.
└ To train tuning machine `mach` on all supplied data, call `fit!(mach.fitresult)`.
Test Summary: | Pass Total
tuning | 18 18
Training ensemble: 20%[==========> ] ETA: 0:00:02[K
Training ensemble: 100%[==================================================] Time: 0:00:00[K
Training ensemble: 50%[=========================> ] ETA: 0:00:01[K
Training ensemble: 100%[==================================================] Time: 0:00:00[K
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Training ensemble: 100%[==================================================] Time: 0:00:00[K
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Training ensemble: 100%[==================================================] Time: 0:00:00[K
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Training ensemble: 100%[==================================================] Time: 0:00:00[K
Training ensemble: 40%[====================> ] ETA: 0:00:01[K
Training ensemble: 100%[==================================================] Time: 0:00:00[K
[ Info: Training [34mMachine{DeterministicEnsembleModel{KNNRegressor}} @ 3…95[39m.
Test Summary: | Pass Total
ensembles | 41 41
Test Summary: | Pass Total
matching models to data | 11 11
┌ Info:
│ is_probabilistic = true
│ input_scitype = ScientificTypes.Table{Union{AbstractArray{Count,1}, AbstractArray{Unknown,1}}}
└ target_scitype = AbstractArray{Count,1}
┌ Warning: Missing values encountered coercing scitype to Count.
│ Coerced to Union{Missing,Count} instead.
└ @ ScientificTypes ~/.julia/packages/ScientificTypes/XsivS/src/conventions/mlj/mlj.jl:5
┌ Info:
│ is_probabilistic = false
│ input_scitype = ScientificTypes.Table{Union{AbstractArray{Continuous,1}, AbstractArray{Multiclass{4},1}, AbstractArray{Union{Missing, Count},1}}}
└ target_scitype = AbstractArray{Count,1}
Test Summary: | Pass Total
tasks | 20 20
Test Summary: | Pass Total
scitypes | 3 3
Testing MLJ tests passed