Package evaluation of FeatureSelection on Julia 1.11.4 (a71dd056e0*) started at 2025-04-08T08:22:34.596 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 8.93s ################################################################################ # Installation # Installing FeatureSelection... Resolving package versions... Updating `~/.julia/environments/v1.11/Project.toml` [33837fe5] + FeatureSelection v0.2.2 Updating `~/.julia/environments/v1.11/Manifest.toml` [9a962f9c] + DataAPI v1.16.0 [e2d170a0] + DataValueInterfaces v1.0.0 [33837fe5] + FeatureSelection v0.2.2 [82899510] + IteratorInterfaceExtensions v1.0.0 [e80e1ace] + MLJModelInterface v1.11.0 [bac558e1] + OrderedCollections v1.8.0 [30f210dd] + ScientificTypesBase v3.0.0 [64bff920] + StatisticalTraits v3.4.0 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 Installation completed after 4.03s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 350.4s ################################################################################ # Testing # Testing FeatureSelection Status `/tmp/jl_yf8OSD/Project.toml` [4c88cf16] Aqua v0.8.11 [31c24e10] Distributions v0.25.118 [33837fe5] FeatureSelection v0.2.2 ⌃ [a7f614a8] MLJBase v1.7.0 [c6f25543] MLJDecisionTreeInterface v0.4.2 [e80e1ace] MLJModelInterface v1.11.0 ⌅ [5ae90465] MLJScikitLearnInterface v0.6.1 [03970b2e] MLJTuning v0.8.8 [30f210dd] ScientificTypesBase v3.0.0 [860ef19b] StableRNGs v1.0.2 ⌅ [a19d573c] StatisticalMeasures v0.1.7 [bd369af6] Tables v1.12.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_yf8OSD/Manifest.toml` [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.42 [79e6a3ab] Adapt v4.3.0 [66dad0bd] AliasTables v1.1.3 [4c88cf16] Aqua v0.8.11 [dce04be8] ArgCheck v2.5.0 [a9b6321e] Atomix v1.1.1 [198e06fe] BangBang v0.4.4 [9718e550] Baselet v0.1.1 [324d7699] CategoricalArrays v0.10.8 [af321ab8] CategoricalDistributions v0.1.15 [d360d2e6] ChainRulesCore v1.25.1 [3da002f7] ColorTypes v0.12.1 [34da2185] Compat v4.16.0 [a33af91c] CompositionsBase v0.1.2 [ed09eef8] ComputationalResources v0.3.2 [992eb4ea] CondaPkg v0.2.27 [187b0558] ConstructionBase v1.5.8 [6add18c4] ContextVariablesX v0.1.3 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [7806a523] DecisionTree v0.12.4 [244e2a9f] DefineSingletons v0.1.2 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.118 [ffbed154] DocStringExtensions v0.9.4 [cc61a311] FLoops v0.2.2 [b9860ae5] FLoopsBase v0.1.1 [33837fe5] FeatureSelection v0.2.2 [1a297f60] FillArrays v1.13.0 [53c48c17] FixedPointNumbers v0.8.5 [46192b85] GPUArraysCore v0.2.0 [076d061b] HashArrayMappedTries v0.2.0 [34004b35] HypergeometricFunctions v0.3.28 [22cec73e] InitialValues v0.3.1 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.4 [82899510] IteratorInterfaceExtensions v1.0.0 [692b3bcd] JLLWrappers v1.7.0 [0f8b85d8] JSON3 v1.14.2 [b14d175d] JuliaVariables v0.2.4 [63c18a36] KernelAbstractions v0.9.34 [b964fa9f] LaTeXStrings v1.4.0 [a5e1c1ea] LatinHypercubeSampling v1.9.0 ⌅ [92ad9a40] LearnAPI v0.1.0 [2ab3a3ac] LogExpFunctions v0.3.29 [c2834f40] MLCore v1.0.0 ⌃ [a7f614a8] MLJBase v1.7.0 [c6f25543] MLJDecisionTreeInterface v0.4.2 [e80e1ace] MLJModelInterface v1.11.0 ⌅ [5ae90465] MLJScikitLearnInterface v0.6.1 [03970b2e] MLJTuning v0.8.8 [d8e11817] MLStyle v0.4.17 [f1d291b0] MLUtils v0.4.8 [1914dd2f] MacroTools v0.5.15 [128add7d] MicroCollections v0.2.0 [0b3b1443] MicroMamba v0.1.14 [e1d29d7a] Missings v1.2.0 [872c559c] NNlib v0.9.30 [71a1bf82] NameResolution v0.1.5 [bac558e1] OrderedCollections v1.8.0 [90014a1f] PDMats v0.11.33 [d96e819e] Parameters v0.12.3 [69de0a69] Parsers v2.8.1 [fa939f87] Pidfile v1.3.0 ⌅ [aea7be01] PrecompileTools v1.2.1 [21216c6a] Preferences v1.4.3 [8162dcfd] PrettyPrint v0.2.0 [08abe8d2] PrettyTables v2.4.0 [92933f4c] ProgressMeter v1.10.4 [43287f4e] PtrArrays v1.3.0 [6099a3de] PythonCall v0.9.24 [1fd47b50] QuadGK v2.11.2 [3cdcf5f2] RecipesBase v1.3.4 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 [79098fc4] Rmath v0.8.0 [321657f4] ScientificTypes v3.1.0 [30f210dd] ScientificTypesBase v3.0.0 [6e75b9c4] ScikitLearnBase v0.5.0 [7e506255] ScopedValues v1.3.0 [6c6a2e73] Scratch v1.2.1 [efcf1570] Setfield v1.1.2 [605ecd9f] ShowCases v0.1.0 [699a6c99] SimpleTraits v0.9.4 [a2af1166] SortingAlgorithms v1.2.1 [276daf66] SpecialFunctions v2.5.0 [171d559e] SplittablesBase v0.1.15 [860ef19b] StableRNGs v1.0.2 [90137ffa] StaticArrays v1.9.13 [1e83bf80] StaticArraysCore v1.4.3 ⌅ [a19d573c] StatisticalMeasures v0.1.7 [c062fc1d] StatisticalMeasuresBase v0.1.2 [64bff920] StatisticalTraits v3.4.0 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.7.0 [2913bbd2] StatsBase v0.34.4 [4c63d2b9] StatsFuns v1.4.0 [892a3eda] StringManipulation v0.4.1 [856f2bd8] StructTypes v1.11.0 [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.0 [28d57a85] Transducers v0.4.84 [3a884ed6] UnPack v1.0.2 [013be700] UnsafeAtomics v0.3.0 [e17b2a0c] UnsafePointers v1.0.0 [efe28fd5] OpenSpecFun_jll v0.5.6+0 [f50d1b31] Rmath_jll v0.5.1+0 [f8abcde7] micromamba_jll v1.5.8+0 [4d7b5844] pixi_jll v0.41.3+0 [0dad84c5] ArgTools v1.1.2 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 v1.11.0 [ade2ca70] Dates v1.11.0 [8ba89e20] Distributed v1.11.0 [f43a241f] Downloads v1.6.0 [7b1f6079] FileWatching v1.11.0 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [4af54fe1] LazyArtifacts v1.11.0 [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.11.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.2.0 [44cfe95a] Pkg v1.11.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.11.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] LibCURL_jll v8.6.0+0 [e37daf67] LibGit2_jll v1.7.2+0 [29816b5a] LibSSH2_jll v1.11.0+1 [c8ffd9c3] MbedTLS_jll v2.28.6+0 [14a3606d] MozillaCACerts_jll v2023.12.12 [4536629a] OpenBLAS_jll v0.3.27+1 [05823500] OpenLibm_jll v0.8.5+0 [bea87d4a] SuiteSparse_jll v7.7.0+0 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.59.0+0 [3f19e933] p7zip_jll v17.4.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... Precompiling MLJBase... 3177.9 ms ✓ MLCore 7848.9 ms ✓ Transducers 22344.1 ms ✓ ScientificTypes 1476.2 ms ✓ Transducers → TransducersAdaptExt 15948.7 ms ✓ FLoops 7072.7 ms ✓ CategoricalDistributions 17956.1 ms ✓ MLUtils 16449.4 ms ✓ StatisticalMeasuresBase 25331.9 ms ✓ MLJBase 9 dependencies successfully precompiled in 119 seconds. 134 already precompiled. Precompiling MLJTuning... 1921.7 ms ✓ LatinHypercubeSampling 10924.9 ms ✓ MLJTuning 2 dependencies successfully precompiled in 14 seconds. 144 already precompiled. Precompiling MLJScikitLearnInterface... 3495.4 ms ✓ micromamba_jll 3453.7 ms ✓ pixi_jll 3519.7 ms ✓ MicroMamba 5743.5 ms ✓ CondaPkg 26445.3 ms ✓ PythonCall Info Given MLJScikitLearnInterface was explicitly requested, output will be shown live   CondaPkg Found dependencies: /home/pkgeval/.julia/packages/MLJScikitLearnInterface/oYeTs/CondaPkg.toml  CondaPkg Found dependencies: /home/pkgeval/.julia/packages/PythonCall/WMWY0/CondaPkg.toml  CondaPkg Resolving changes  + libstdcxx-ng  + python  + scikit-learn  CondaPkg Initialising pixi  │ /home/pkgeval/.julia/artifacts/cefba4912c2b400756d043a2563ef77a0088866b/bin/pixi  │ init  │ --format pixi  └ /tmp/jl_yf8OSD/.CondaPkg ✔ Created /tmp/jl_yf8OSD/.CondaPkg/pixi.toml  CondaPkg Wrote /tmp/jl_yf8OSD/.CondaPkg/pixi.toml  │ [dependencies]  │ libstdcxx-ng = ">=3.4,<13.0"  │  │ [dependencies.scikit-learn]  │ channel = "conda-forge"  │ version = ">=1.2, <1.4"  │  │ [dependencies.python]  │ channel = "conda-forge"  │ build = "*cpython*"  │ version = ">=3.8,<4"  │  │ [project]  │ name = ".CondaPkg"  │ platforms = ["linux-64"]  │ channels = ["conda-forge"]  │ channel-priority = "strict"  └ description = "automatically generated by CondaPkg.jl"  CondaPkg Installing packages  │ /home/pkgeval/.julia/artifacts/cefba4912c2b400756d043a2563ef77a0088866b/bin/pixi  │ install  └ --manifest-path /tmp/jl_yf8OSD/.CondaPkg/pixi.toml ✔ The default environment has been installed. 33317.6 ms ✓ MLJScikitLearnInterface 6 dependencies successfully precompiled in 76 seconds. 49 already precompiled. 1 dependency had output during precompilation: ┌ MLJScikitLearnInterface │ [Output was shown above] └ Precompiling CategoricalArraysStructTypesExt... 965.7 ms ✓ CategoricalArrays → CategoricalArraysStructTypesExt 1 dependency successfully precompiled in 1 seconds. 11 already precompiled. Precompiling StatisticalMeasures... 43474.4 ms ✓ StatisticalMeasures 6501.9 ms ✓ StatisticalMeasures → ScientificTypesExt 2 dependencies successfully precompiled in 51 seconds. 134 already precompiled. Precompiling DefaultMeasuresExt... 6742.9 ms ✓ MLJBase → DefaultMeasuresExt 1 dependency successfully precompiled in 7 seconds. 145 already precompiled. Test Summary: | Pass Total Time Aqua.jl | 11 11 1m02.4s Test Summary: | Pass Total Time Feat Selector | 12 12 9.1s [ Info: Training machine(DeterministicRecursiveFeatureElimination(model = RandomForestRegressor(max_depth = -1, …), …), …). [ Info: Fitting estimator with 10 features. [ Info: Fitting estimator with 9 features. [ Info: Fitting estimator with 8 features. [ Info: Fitting estimator with 7 features. [ Info: Fitting estimator with 6 features. [ Info: Fitting estimator with 5 features. [ Info: Training machine(ProbabilisticRecursiveFeatureElimination(model = RandomForestClassifier(max_depth = -1, …), …), …). [ Info: Fitting estimator with 10 features. [ Info: Fitting estimator with 9 features. [ Info: Fitting estimator with 8 features. [ Info: Fitting estimator with 7 features. [ Info: Fitting estimator with 6 features. [ Info: Fitting estimator with 5 features. [ Info: Training machine(DeterministicRecursiveFeatureElimination(model = RandomForestRegressor(max_depth = -1, …), …), …). [ Info: Fitting estimator with 10 features. [ Info: Fitting estimator with 9 features. [ Info: Fitting estimator with 8 features. [ Info: Fitting estimator with 7 features. [ Info: Fitting estimator with 6 features. [ Info: Fitting estimator with 5 features. Test Summary: | Pass Total Time RecursiveFeatureElimination | 27 27 50.0s [ Info: Training machine(DeterministicRecursiveFeatureElimination(model = SVMRegressor(kernel = linear, …), …), …). [ Info: Fitting estimator with 10 features. [ Info: Fitting estimator with 9 features. [ Info: Fitting estimator with 8 features. [ Info: Fitting estimator with 7 features. [ Info: Fitting estimator with 6 features. [ Info: Fitting estimator with 5 features. [ Info: Training machine(ProbabilisticTunedModel(model = DeterministicRecursiveFeatureElimination(model = SVMRegressor(kernel = linear, …), …), …), …). [ Info: Attempting to evaluate 10 models. Evaluating over 10 metamodels: 0%[> ] ETA: N/A Evaluating over 10 metamodels: 10%[==> ] ETA: 0:06:59 Evaluating over 10 metamodels: 20%[=====> ] ETA: 0:03:15 Evaluating over 10 metamodels: 30%[=======> ] ETA: 0:01:56 Evaluating over 10 metamodels: 40%[==========> ] ETA: 0:01:16 Evaluating over 10 metamodels: 50%[============> ] ETA: 0:00:52 Evaluating over 10 metamodels: 60%[===============> ] ETA: 0:00:35 Evaluating over 10 metamodels: 70%[=================> ] ETA: 0:00:23 Evaluating over 10 metamodels: 80%[====================> ] ETA: 0:00:14 Evaluating over 10 metamodels: 90%[======================> ] ETA: 0:00:06 Evaluating over 10 metamodels: 100%[=========================] Time: 0:00:57 Test Summary: | Pass Total Time Compare results for RFE with scikit-learn | 2 2 1m51.3s Test Summary: | Pass Total Time Serialization for atomic models with non-persistent fitresults | 1 1 20.7s Test Summary: | Pass Total Time is_wrapper | 2 2 0.1s Testing FeatureSelection tests passed Testing completed after 569.23s PkgEval succeeded after 945.72s