Package evaluation to test Imbalance on Julia 1.14.0-DEV.3055 (7e75a8061a*) started at 2026-08-28T04:03:28.160 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.14` Set-up completed after 14.44s ################################################################################ # Installation # Installing Imbalance... Resolving package versions... Installed Rmath_jll ─────────────────── v0.5.2+0 Installed CommonSolve ───────────────── v0.2.14 Installed FillArrays ────────────────── v1.17.0 Installed DataStructures ────────────── v0.19.6 Installed CompositionsBase ──────────── v0.1.2 Installed MacroTools ────────────────── v0.5.16 Installed Measurements ──────────────── v2.14.1 Installed Adapt ─────────────────────── v4.7.0 Installed HypergeometricFunctions ───── v0.3.30 Installed ScientificTypes ───────────── v3.3.0 Installed StatsBase ─────────────────── v0.34.13 Installed DelimitedFiles ────────────── v1.9.1 Installed AbstractTrees ─────────────── v0.4.5 Installed AliasTables ───────────────── v1.1.3 Installed Calculus ──────────────────── v0.5.2 Installed ConstructionBase ──────────── v1.6.0 Installed OrderedCollections ────────── v2.0.1 Installed NearestNeighbors ──────────── v0.4.29 Installed NNlib ─────────────────────── v0.9.44 Installed TableDistances ────────────── v1.2.0 Installed HashArrayMappedTries ──────── v0.2.0 Installed Compat ────────────────────── v4.18.1 Installed NelderMead ────────────────── v0.4.0 Installed IteratorInterfaceExtensions ─ v1.0.0 Installed RecipesBase ───────────────── v1.3.4 Installed InvertedIndices ───────────── v1.3.1 Installed DataAPI ───────────────────── v1.16.0 Installed Statistics ────────────────── v1.11.1 Installed PrecompileTools ───────────── v1.3.4 Installed SentinelArrays ────────────── v1.4.10 Installed Clustering ────────────────── v0.15.8 Installed StatsAPI ──────────────────── v1.8.0 Installed DataValueInterfaces ───────── v1.0.0 Installed Atomix ────────────────────── v1.1.3 Installed MLCore ────────────────────── v1.1.0 Installed TransformsBase ────────────── v1.6.0 Installed ProgressMeter ─────────────── v1.11.0 Installed StaticArrays ──────────────── v1.9.19 Installed MLUtils ───────────────────── v0.4.13 Installed StructUtils ───────────────── v2.8.5 Installed CodeTracking ──────────────── v3.0.2 Installed StaticArraysCore ──────────── v1.4.4 Installed ChainRulesCore ────────────── v1.26.1 Installed IrrationalConstants ───────── v0.2.6 Installed StringManipulation ────────── v0.5.0 Installed GPUArraysCore ─────────────── v0.2.0 Installed Distances ─────────────────── v0.10.12 Installed QuadGK ────────────────────── v2.11.3 Installed Requires ──────────────────── v1.3.1 Installed ColorTypes ────────────────── v0.12.1 Installed SimpleTraits ──────────────── v0.9.6 Installed Imbalance ─────────────────── v0.2.0 Installed LogExpFunctions ───────────── v1.0.1 Installed StatisticalMeasuresBase ───── v0.1.4 Installed OpenSpecFun_jll ───────────── v0.5.6+0 Installed Roots ─────────────────────── v3.0.7 Installed ComputationalResources ────── v0.3.2 Installed JSON ──────────────────────── v1.7.1 Installed KernelAbstractions ────────── v0.9.42 Installed PrettyTables ──────────────── v3.4.8 Installed Distributions ─────────────── v0.25.131 Installed CategoricalDistributions ──── v0.2.2 Installed Parsers ───────────────────── v2.8.7 Installed SpectralIndices ───────────── v0.2.20 Installed Gamma ─────────────────────── v1.2.0 Installed MLJBase ───────────────────── v1.14.1 Installed Parameters ────────────────── v0.13.1 Installed UnsafeAtomics ─────────────── v0.3.2 Installed StatsFuns ─────────────────── v2.2.1 Installed Tables ────────────────────── v1.14.0 Installed MLJTestInterface ──────────── v0.2.9 Installed Rmath ─────────────────────── v0.9.0 Installed ScopedValues ──────────────── v1.6.2 Installed SpecialFunctions ──────────── v2.9.0 Installed Unitful ───────────────────── v1.28.0 Installed PtrArrays ─────────────────── v1.4.0 Installed Reexport ──────────────────── v1.2.2 Installed InverseFunctions ──────────── v0.1.17 Installed Missings ──────────────────── v1.2.0 Installed FixedPointNumbers ─────────── v0.8.6 Installed IntervalSets ──────────────── v0.7.14 Installed Preferences ───────────────── v1.5.2 Installed TableTransforms ───────────── v1.38.10 Installed SortingAlgorithms ─────────── v1.2.3 Installed ScientificTypesBase ───────── v3.1.0 Installed ShowCases ─────────────────── v0.1.0 Installed LaTeXStrings ──────────────── v1.4.1 Installed JLLWrappers ───────────────── v1.8.0 Installed TableTraits ───────────────── v1.0.1 Installed ColumnSelectors ───────────── v1.0.0 Installed CoDa ──────────────────────── v1.5.1 Installed DataScienceTraits ─────────── v1.2.2 Installed Crayons ───────────────────── v4.2.0 Installed PDMats ────────────────────── v0.11.41 Installed BFloat16s ─────────────────── v0.6.1 Installed AxisArrays ────────────────── v0.4.8 Installed CategoricalArrays ─────────── v1.1.1 Installed IterTools ─────────────────── v1.10.0 Installed RangeArrays ───────────────── v0.3.2 Installed UnPack ────────────────────── v1.0.2 Installed StatisticalTraits ─────────── v3.5.0 Installed TableOperations ───────────── v1.2.0 Installed LearnAPI ──────────────────── v2.0.1 Installed Accessors ─────────────────── v0.1.45 Installed DocStringExtensions ───────── v0.9.5 Installed MLJModelInterface ─────────── v1.12.1 Installing 2 artifacts Installed artifact Rmath 111.3 KiB Installed artifact OpenSpecFun 105.4 KiB Updating `~/.julia/environments/v1.14/Project.toml` [c709b415] + Imbalance v0.2.0 Updating `~/.julia/environments/v1.14/Manifest.toml` [1520ce14] + AbstractTrees v0.4.5 [7d9f7c33] + Accessors v0.1.45 [79e6a3ab] + Adapt v4.7.0 [66dad0bd] + AliasTables v1.1.3 [a9b6321e] + Atomix v1.1.3 [39de3d68] + AxisArrays v0.4.8 [ab4f0b2a] + BFloat16s v0.6.1 [49dc2e85] + Calculus v0.5.2 [324d7699] + CategoricalArrays v1.1.1 [af321ab8] + CategoricalDistributions v0.2.2 [d360d2e6] + ChainRulesCore v1.26.1 [aaaa29a8] + Clustering v0.15.8 [5900dafe] + CoDa v1.5.1 [da1fd8a2] + CodeTracking v3.0.2 [3da002f7] + ColorTypes v0.12.1 [9cc86067] + ColumnSelectors v1.0.0 [38540f10] + CommonSolve v0.2.14 [34da2185] + Compat v4.18.1 [a33af91c] + CompositionsBase v0.1.2 [ed09eef8] + ComputationalResources v0.3.2 [187b0558] + ConstructionBase v1.6.0 [a8cc5b0e] + Crayons v4.2.0 [9a962f9c] + DataAPI v1.16.0 [6cb2f572] + DataScienceTraits v1.2.2 [864edb3b] + DataStructures v0.19.6 [e2d170a0] + DataValueInterfaces v1.0.0 [8bb1440f] + DelimitedFiles v1.9.1 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.131 [ffbed154] + DocStringExtensions v0.9.5 [1a297f60] + FillArrays v1.17.0 ⌅ [53c48c17] + FixedPointNumbers v0.8.6 [46192b85] + GPUArraysCore v0.2.0 [a0844989] + Gamma v1.2.0 [076d061b] + HashArrayMappedTries v0.2.0 [34004b35] + HypergeometricFunctions v0.3.30 [c709b415] + Imbalance v0.2.0 [8197267c] + IntervalSets v0.7.14 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.6 [c8e1da08] + IterTools v1.10.0 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.8.0 [682c06a0] + JSON v1.7.1 [63c18a36] + KernelAbstractions v0.9.42 [b964fa9f] + LaTeXStrings v1.4.1 [92ad9a40] + LearnAPI v2.0.1 [2ab3a3ac] + LogExpFunctions v1.0.1 [c2834f40] + MLCore v1.1.0 [a7f614a8] + MLJBase v1.14.1 [e80e1ace] + MLJModelInterface v1.12.1 [72560011] + MLJTestInterface v0.2.9 [f1d291b0] + MLUtils v0.4.13 [1914dd2f] + MacroTools v0.5.16 [eff96d63] + Measurements v2.14.1 [e1d29d7a] + Missings v1.2.0 [872c559c] + NNlib v0.9.44 [b8a86587] + NearestNeighbors v0.4.29 [2f6b4ddb] + NelderMead v0.4.0 [bac558e1] + OrderedCollections v2.0.1 [90014a1f] + PDMats v0.11.41 [d96e819e] + Parameters v0.13.1 ⌅ [69de0a69] + Parsers v2.8.7 [aea7be01] + PrecompileTools v1.3.4 [21216c6a] + Preferences v1.5.2 [08abe8d2] + PrettyTables v3.4.8 [92933f4c] + ProgressMeter v1.11.0 [43287f4e] + PtrArrays v1.4.0 [1fd47b50] + QuadGK v2.11.3 [b3c3ace0] + RangeArrays v0.3.2 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [79098fc4] + Rmath v0.9.0 [f2b01f46] + Roots v3.0.7 [321657f4] + ScientificTypes v3.3.0 [30f210dd] + ScientificTypesBase v3.1.0 [7e506255] + ScopedValues v1.6.2 [91c51154] + SentinelArrays v1.4.10 [605ecd9f] + ShowCases v0.1.0 [699a6c99] + SimpleTraits v0.9.6 [a2af1166] + SortingAlgorithms v1.2.3 [276daf66] + SpecialFunctions v2.9.0 [df0093a1] + SpectralIndices v0.2.20 [90137ffa] + StaticArrays v1.9.19 [1e83bf80] + StaticArraysCore v1.4.4 [c062fc1d] + StatisticalMeasuresBase v0.1.4 [64bff920] + StatisticalTraits v3.5.0 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.8.0 [2913bbd2] + StatsBase v0.34.13 [4c63d2b9] + StatsFuns v2.2.1 [892a3eda] + StringManipulation v0.5.0 [ec057cc2] + StructUtils v2.8.5 [e5d66e97] + TableDistances v1.2.0 [ab02a1b2] + TableOperations v1.2.0 [3783bdb8] + TableTraits v1.0.1 [0d432bfd] + TableTransforms v1.38.10 [bd369af6] + Tables v1.14.0 [28dd2a49] + TransformsBase v1.6.0 [3a884ed6] + UnPack v1.0.2 [1986cc42] + Unitful v1.28.0 [013be700] + UnsafeAtomics v0.3.2 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.2+0 [0dad84c5] + ArgTools v1.2.0 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.12.0 [f43a241f] + Downloads v1.7.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.13.0 [b27032c2] + LibCURL v1.0.0 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.14.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.14.0 [de0858da] + Printf v1.11.0 [3fa0cd96] + REPL v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v1.13.0 [9e88b42a] + Serialization v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.13.0 [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 v1.5.7+0 [deac9b47] + LibCURL_jll v8.21.0+0 [e37daf67] + LibGit2_jll v1.9.7+0 [29816b5a] + LibSSH2_jll v1.11.104+0 [14a3606d] + MozillaCACerts_jll v2026.8.13 [4536629a] + OpenBLAS_jll v0.3.34+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.8+0 [efcefdf7] + PCRE2_jll v10.47.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.2+0 [3161d3a3] + Zstd_jll v1.5.7+1 [8e850b90] + libblastrampoline_jll v5.15.0+0 [8e850ede] + nghttp2_jll v1.70.0+0 [3f19e933] + p7zip_jll v17.8.2+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Installation completed after 11.56s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling project... 4.4 s ✓ TestEnv 1 dependency successfully precompiled in 5 seconds. 27 already precompiled. Precompiling package dependencies... Precompiling project... 3.5 s ✓ MacroTools 1.4 s ✓ InlineStrings 0.5 s ✓ Reexport 0.7 s ✓ ConstructionBase 2.0 s ✓ IrrationalConstants 0.5 s ✓ DataValueInterfaces 0.6 s ✓ StatsAPI 0.9 s ✓ NelderMead 1.1 s ✓ Calculus 0.8 s ✓ IterTools 0.8 s ✓ IntervalSets 0.5 s ✓ LaTeXStrings 0.7 s ✓ ChunkCodecCore 0.8 s ✓ Statistics 0.6 s ✓ StaticArraysCore 0.7 s ✓ StableRNGs 0.6 s ✓ PtrArrays 0.6 s ✓ Adapt 0.6 s ✓ DataAPI 3.0 s ✓ UnsafeAtomics 1.1 s ✓ ShowCases 0.6 s ✓ InvertedIndices 0.6 s ✓ InverseFunctions 0.5 s ✓ CompositionsBase 0.9 s ✓ AbstractTrees 0.7 s ✓ ComputationalResources 2.1 s ✓ FillArrays 0.5 s ✓ ColumnSelectors 0.5 s ✓ UnPack 0.6 s ✓ RangeArrays 0.9 s ✓ OrderedCollections 0.6 s ✓ HashArrayMappedTries 0.7 s ✓ DocStringExtensions 0.4 s ✓ IteratorInterfaceExtensions 1.6 s ✓ Crayons 0.5 s ✓ IOCapture 0.7 s ✓ Requires 0.8 s ✓ BFloat16s 2.1 s ✓ ProgressMeter 0.7 s ✓ DelimitedFiles 0.9 s ✓ DataScienceTraits 29.5 s ✓ Unitful 1.8 s ✓ SentinelArrays 2.0 s ✓ PDMats 0.8 s ✓ Compat 1.0 s ✓ ScientificTypesBase 1.5 s ✓ JLLWrappers 1.7 s ✓ LearnAPI 2.8 s ✓ CodeTracking 5.4 s ✓ StringManipulation 1.5 s ✓ CommonSolve 3.0 s ✓ RecipesBase 2.2 s ✓ SimpleTraits 9.6 s ✓ PyCall 0.9 s ✓ InlineStrings → ParsersExt 0.5 s ✓ ConstructionBase → ConstructionBaseLinearAlgebraExt 0.9 s ✓ Measurements 0.5 s ✓ IntervalSets → IntervalSetsRandomExt 0.6 s ✓ ConstructionBase → ConstructionBaseIntervalSetsExt 0.6 s ✓ ChunkCodecLibZlib 0.6 s ✓ ChunkCodecLibZstd 1.5 s ✓ Statistics → SparseArraysExt 2.2 s ✓ FixedPointNumbers 1.1 s ✓ Distances 0.5 s ✓ IntervalSets → IntervalSetsStatisticsExt 0.9 s ✓ StructUtils → StructUtilsStaticArraysCoreExt 12.9 s ✓ StaticArrays 0.8 s ✓ AliasTables 1.5 s ✓ Adapt → AdaptSparseArraysExt 0.8 s ✓ GPUArraysCore 0.7 s ✓ Missings 0.8 s ✓ PooledArrays 0.7 s ✓ Atomix 0.9 s ✓ InverseFunctions → InverseFunctionsDatesExt 1.7 s ✓ InverseFunctions → InverseFunctionsTestExt 1.1 s ✓ CompositionsBase → CompositionsBaseInverseFunctionsExt 0.6 s ✓ TransformsBase 2.0 s ✓ FillArrays → FillArraysSparseArraysExt 1.3 s ✓ FillArrays → FillArraysStatisticsExt 0.9 s ✓ Parameters 3.4 s ✓ DataStructures 0.7 s ✓ ScopedValues 2.2 s ✓ LogExpFunctions 0.5 s ✓ TableTraits 7.1 s ✓ FileIO 1.4 s ✓ Unitful → ConstructionBaseUnitfulExt 1.7 s ✓ Unitful → PrintfExt 1.5 s ✓ Unitful → InverseFunctionsUnitfulExt 1.6 s ✓ DataScienceTraits → DataScienceTraitsUnitfulExt 1.8 s ✓ FillArrays → FillArraysPDMatsExt 0.5 s ✓ Compat → CompatLinearAlgebraExt 1.0 s ✓ StatisticalTraits 1.4 s ✓ Rmath_jll 1.0 s ✓ OpenSpecFun_jll 1.1 s ✓ IntervalSets → IntervalSetsRecipesBaseExt 0.9 s ✓ Measurements → MeasurementsRecipesBaseExt 1.6 s ✓ Measurements → MeasurementsUnitfulExt 0.9 s ✓ StructUtils → StructUtilsMeasurementsExt 1.6 s ✓ AxisArrays 2.4 s ✓ ColorTypes 1.5 s ✓ Distances → DistancesSparseArraysExt 1.3 s ✓ StaticArrays → StaticArraysStatisticsExt 1.3 s ✓ ConstructionBase → ConstructionBaseStaticArraysExt 1.3 s ✓ Adapt → AdaptStaticArraysExt 1.9 s ✓ FillArrays → FillArraysStaticArraysExt 4.0 s ✓ Accessors 0.7 s ✓ SortingAlgorithms 1.6 s ✓ QuadGK 0.6 s ✓ LogExpFunctions → LogExpFunctionsInverseFunctionsExt 1.4 s ✓ Gamma 1.6 s ✓ Tables 41.7 s ✓ JLD2 1.9 s ✓ ChainRulesCore 16.7 s ✓ SpectralIndices 3.5 s ✓ CategoricalArrays 3.2 s ✓ MLJModelInterface 1.8 s ✓ Rmath 5.2 s ✓ SpecialFunctions 1.2 s ✓ ColorTypes → StyledStringsExt 1.3 s ✓ DataScienceTraits → DataScienceTraitsColorTypesExt 7.5 s ✓ NearestNeighbors 6.9 s ✓ KernelAbstractions 1.4 s ✓ Accessors → StaticArraysExt 1.7 s ✓ Accessors → TestExt 2.0 s ✓ Accessors → IntervalSetsExt 1.5 s ✓ Accessors → LinearAlgebraExt 1.6 s ✓ Accessors → UnitfulExt 4.8 s ✓ StatsBase 2.0 s ✓ HypergeometricFunctions 0.9 s ✓ StructUtils → StructUtilsTablesExt 1.3 s ✓ TableOperations 41.1 s ✓ PrettyTables 2.4 s ✓ MLCore 5.3 s ✓ JLD2 → UnPackExt 1.6 s ✓ ChainRulesCore → ChainRulesCoreSparseArraysExt 0.6 s ✓ Distances → DistancesChainRulesCoreExt 1.4 s ✓ StaticArrays → StaticArraysChainRulesCoreExt 2.6 s ✓ LogExpFunctions → LogExpFunctionsChainRulesCoreExt 2.1 s ✓ CategoricalArrays → CategoricalArraysRecipesBaseExt 2.3 s ✓ CategoricalArrays → CategoricalArraysSentinelArraysExt 2.2 s ✓ CategoricalArrays → CategoricalArraysJSONExt 1.6 s ✓ DataScienceTraits → DataScienceTraitsCategoricalArraysExt 3.2 s ✓ SpecialFunctions → SpecialFunctionsChainRulesCoreExt 2.0 s ✓ Measurements → MeasurementsSpecialFunctionsExt 2.9 s ✓ KernelAbstractions → SparseArraysExt 2.0 s ✓ KernelAbstractions → LinearAlgebraExt 5.5 s ✓ Roots 1.7 s ✓ PDMats → StatsBaseExt 2.5 s ✓ CategoricalArrays → CategoricalArraysStatsBaseExt 4.7 s ✓ Clustering 2.1 s ✓ StatsFuns 76.6 s ✓ DataFrames 2.4 s ✓ TableDistances 11.8 s ✓ NNlib 1.0 s ✓ Roots → RootsChainRulesCoreExt 1.5 s ✓ Roots → RootsUnitfulExt 3.0 s ✓ StatsFuns → StatsFunsChainRulesCoreExt 0.9 s ✓ StatsFuns → StatsFunsInverseFunctionsExt 6.9 s ✓ SpectralIndices → SpectralIndicesDataFramesExt 2.4 s ✓ NNlib → NNlibSpecialFunctionsExt 11.8 s ✓ MLUtils 10.3 s ✓ Distributions 17.7 s ✓ StatisticalMeasuresBase 4.4 s ✓ Distributions → DistributionsTestExt 4.0 s ✓ Distributions → DistributionsChainRulesCoreExt 3.3 s ✓ DataScienceTraits → DataScienceTraitsDistributionsExt 4.7 s ✓ CoDa 8.6 s ✓ ScientificTypes 4.0 s ✓ DataScienceTraits → DataScienceTraitsCoDaExt 7.8 s ✓ CategoricalDistributions 12.4 s ✓ TableTransforms 17.9 s ✓ MLJBase 17.5 s ✓ MLJTestInterface 11.8 s ✓ Imbalance 174 dependencies successfully precompiled in 632 seconds. 46 already precompiled. Precompilation completed after 698.6s ################################################################################ # Testing # Testing Imbalance Status `/tmp/jl_WiIcJJ/Project.toml` [324d7699] CategoricalArrays v1.1.1 [af321ab8] CategoricalDistributions v0.2.2 [aaaa29a8] Clustering v0.15.8 [8f4d0f93] Conda v1.10.3 [a93c6f00] DataFrames v1.8.2 [b4f34e82] Distances v0.10.12 [b5f81e59] IOCapture v1.0.0 [c709b415] Imbalance v0.2.0 [033835bb] JLD2 v0.6.6 [a7f614a8] MLJBase v1.14.1 [e80e1ace] MLJModelInterface v1.12.1 [72560011] MLJTestInterface v0.2.9 [b8a86587] NearestNeighbors v0.4.29 [bac558e1] OrderedCollections v2.0.1 [92933f4c] ProgressMeter v1.11.0 [438e738f] PyCall v1.96.4 [321657f4] ScientificTypes v3.3.0 [860ef19b] StableRNGs v1.0.4 [10745b16] Statistics v1.11.1 [2913bbd2] StatsBase v0.34.13 [ab02a1b2] TableOperations v1.2.0 [0d432bfd] TableTransforms v1.38.10 [bd369af6] Tables v1.14.0 [28dd2a49] TransformsBase v1.6.0 [37e2e46d] LinearAlgebra v1.14.0 [44cfe95a] Pkg v1.14.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_WiIcJJ/Manifest.toml` [1520ce14] AbstractTrees v0.4.5 [7d9f7c33] Accessors v0.1.45 [79e6a3ab] Adapt v4.7.0 [66dad0bd] AliasTables v1.1.3 [a9b6321e] Atomix v1.1.3 [39de3d68] AxisArrays v0.4.8 [ab4f0b2a] BFloat16s v0.6.1 [49dc2e85] Calculus v0.5.2 [324d7699] CategoricalArrays v1.1.1 [af321ab8] CategoricalDistributions v0.2.2 [d360d2e6] ChainRulesCore v1.26.1 [0b6fb165] ChunkCodecCore v1.0.2 [4c0bbee4] ChunkCodecLibZlib v1.1.0 [55437552] ChunkCodecLibZstd v1.0.0 [aaaa29a8] Clustering v0.15.8 [5900dafe] CoDa v1.5.1 [da1fd8a2] CodeTracking v3.0.2 [3da002f7] ColorTypes v0.12.1 [9cc86067] ColumnSelectors v1.0.0 [38540f10] CommonSolve v0.2.14 [34da2185] Compat v4.18.1 [a33af91c] CompositionsBase v0.1.2 [ed09eef8] ComputationalResources v0.3.2 [8f4d0f93] Conda v1.10.3 [187b0558] ConstructionBase v1.6.0 [a8cc5b0e] Crayons v4.2.0 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.8.2 [6cb2f572] DataScienceTraits v1.2.2 [864edb3b] DataStructures v0.19.6 [e2d170a0] DataValueInterfaces v1.0.0 [8bb1440f] DelimitedFiles v1.9.1 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.131 [ffbed154] DocStringExtensions v0.9.5 [5789e2e9] FileIO v1.20.0 [1a297f60] FillArrays v1.17.0 ⌅ [53c48c17] FixedPointNumbers v0.8.6 [46192b85] GPUArraysCore v0.2.0 [a0844989] Gamma v1.2.0 [076d061b] HashArrayMappedTries v0.2.0 [34004b35] HypergeometricFunctions v0.3.30 [b5f81e59] IOCapture v1.0.0 [c709b415] Imbalance v0.2.0 [842dd82b] InlineStrings v1.4.5 [8197267c] IntervalSets v0.7.14 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.6 [c8e1da08] IterTools v1.10.0 [82899510] IteratorInterfaceExtensions v1.0.0 [033835bb] JLD2 v0.6.6 [692b3bcd] JLLWrappers v1.8.0 [682c06a0] JSON v1.7.1 [63c18a36] KernelAbstractions v0.9.42 [b964fa9f] LaTeXStrings v1.4.1 [92ad9a40] LearnAPI v2.0.1 [2ab3a3ac] LogExpFunctions v1.0.1 [c2834f40] MLCore v1.1.0 [a7f614a8] MLJBase v1.14.1 [e80e1ace] 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v0.8.7+0 [458c3c95] OpenSSL_jll v3.5.8+0 [efcefdf7] PCRE2_jll v10.47.0+0 [bea87d4a] SuiteSparse_jll v7.10.1+0 [83775a58] Zlib_jll v1.3.2+0 [3161d3a3] Zstd_jll v1.5.7+1 [8e850b90] libblastrampoline_jll v5.15.0+0 [8e850ede] nghttp2_jll v1.70.0+0 [3f19e933] p7zip_jll v17.8.2+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... Building Conda ─→ `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/8f06b0cfa4c514c7b9546756dbae91fcfbc92dc9/build.log` Building PyCall → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/9816a3826b0ebf49ab4926e2b18842ad8b5c8f04/build.log` ┌ Warning: `disable_sigint` no longer defers Ctrl-C: SIGINT is delivered as a cancellation of the current ^C scope and observed at cancellation points regardless of the sigatomic region this establishes. Shield a region from cancellation by scoping `Base.CANCEL_TOKEN => nothing` over it instead. │ caller = pyeval_(s::String, globals::PyCall.PyDict{String, PyCall.PyObject, true}, locals::PyCall.PyDict{String, PyCall.PyObject, true}, input_type::Int64, fname::String) at pyeval.jl:37 └ @ PyCall ~/.julia/packages/PyCall/1gn3u/src/pyeval.jl:37 [ Info: Comparing with saved python model outcomes in some tests. For live comparisons, set `offline_python_test=false` in runtests.jl. Test Summary: | Pass Total Time class_counts | 12 12 10.9s Test Summary: | Pass Total Time table_wrappers | 6 6 14.6s Progress: 67%|███████████████████████████▍ | ETA: 0:00:01 class: 0  Progress: 67%|███████████████████████████▍ | ETA: 0:00:00 class: 2 Test Summary: | Pass Total Time generic_resample | 14 14 25.5s Test Summary: | Pass Total Time extras | 1 1 2.9s Test Summary: | Pass Total Time distance metrics | 3 3 1m16.7s Progress: 67%|███████████████████████████▍ | ETA: 0:00:00 class: 0   Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 class: 1 ROSE MLJ: Error During Test at /home/pkgeval/.julia/packages/Imbalance/mQpnn/test/interfaces/mlj_interface.jl:57 Test threw exception Expression: transform(mach, X, y) == rose(X, y; s = 0.01, ratios = Dict(0 => 1.2, 1 => 1.2, 2 => 1.2), rng = 42) MethodError: no method matching reducedim1(::Matrix{Float64}, ::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}) The function `reducedim1` exists, but no method is defined for this combination of argument types. Closest candidates are: reducedim1(::Any) @ Base reducedim.jl:1229 Stacktrace: [1] centralize_sumabs2!(R::Matrix{Float64}, A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, means::Matrix{Float64}) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:282 [2] varm!(R::Matrix{Float64}, A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, m::Matrix{Float64}; corrected::Bool) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:309 [3] _varm(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, m::Matrix{Float64}, corrected::Bool, region::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:341 [4] varm(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, m::Matrix{Float64}; corrected::Bool, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:339 [inlined] [5] _var(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:386 [inlined] [6] var(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}; corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:380 [inlined] [7] _std(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:469 [8] std(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}; corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:461 [inlined] [9] rose_per_class(X::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, n::Int64; s::Float64, rng::Xoshiro) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/rose/rose.jl:25 [10] generic_oversample(::Matrix{Float64}, ::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}, ::typeof(Imbalance.rose_per_class); ratios::Dict{Int64, Float64}, pass_inds::Bool, is_transposed::Bool, kwargs::@Kwargs{s::Float64, rng::Xoshiro}) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/generic_resample.jl:50 [11] rose(X::Matrix{Float64}, y::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}; s::Float64, ratios::Dict{Int64, Float64}, rng::Int64, try_preserve_type::Bool) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/rose/rose.jl:174 [inlined] [12] tablify(matrix_func::typeof(rose), X::DataFrame, y::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}; try_preserve_type::Bool, encode_func::Imbalance.var"#tablify##4#tablify##5", decode_func::Imbalance.var"#tablify##6#tablify##7", kwargs::@Kwargs{s::Float64, ratios::Dict{Int64, Float64}, rng::Int64}) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/table_wrappers.jl:73 [13] rose(X::DataFrame, y::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}; s::Float64, ratios::Dict{Int64, Float64}, rng::Int64, try_preserve_type::Bool) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/rose/rose.jl:187 [14] transform(r::Imbalance.MLJ.ROSE{Dict{Int64, Float64}, Int64, Float64}, ::Any, X::DataFrame, y::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}) @ Imbalance.MLJ ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/rose/interface_mlj.jl:40 [inlined] [15] transform(mach::MLJBase.Machine{Imbalance.MLJ.ROSE{Dict{Int64, Float64}, Int64, Float64}, Imbalance.MLJ.ROSE{Dict{Int64, Float64}, Int64, Float64}, false}, Xraw::DataFrame, Xraw_more::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}) @ MLJBase ~/.julia/packages/MLJBase/ip7cA/src/operations.jl:143 [16] top-level scope @ ~/.julia/packages/Imbalance/mQpnn/test/interfaces/mlj_interface.jl:40 [17] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [18] macro expansion @ ~/.julia/packages/Imbalance/mQpnn/test/interfaces/mlj_interface.jl:57 [inlined] [19] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined] ROSE MLJ: Error During Test at /home/pkgeval/.julia/packages/Imbalance/mQpnn/test/interfaces/mlj_interface.jl:62 Test threw exception Expression: transform(mach, X, y) == rose(X, y; s = 0.01, ratios = Dict(0 => 1.2, 1 => 1.2, 2 => 1.2), rng = 42) MethodError: no method matching reducedim1(::Matrix{Float64}, ::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}) The function `reducedim1` exists, but no method is defined for this combination of argument types. Closest candidates are: reducedim1(::Any) @ Base reducedim.jl:1229 Stacktrace: [1] centralize_sumabs2!(R::Matrix{Float64}, A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, means::Matrix{Float64}) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:282 [2] varm!(R::Matrix{Float64}, A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, m::Matrix{Float64}; corrected::Bool) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:309 [3] _varm(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, m::Matrix{Float64}, corrected::Bool, region::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:341 [4] varm(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, m::Matrix{Float64}; corrected::Bool, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:339 [inlined] [5] _var(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:386 [inlined] [6] var(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}; corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:380 [inlined] [7] _std(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:469 [8] std(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}; corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:461 [inlined] [9] rose_per_class(X::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, n::Int64; s::Float64, rng::Xoshiro) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/rose/rose.jl:25 [10] generic_oversample(::Matrix{Float64}, ::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}, ::typeof(Imbalance.rose_per_class); ratios::Dict{Int64, Float64}, pass_inds::Bool, is_transposed::Bool, kwargs::@Kwargs{s::Float64, rng::Xoshiro}) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/generic_resample.jl:50 [11] rose(X::Matrix{Float64}, y::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}; s::Float64, ratios::Dict{Int64, Float64}, rng::Int64, try_preserve_type::Bool) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/rose/rose.jl:174 [inlined] [12] transform(r::Imbalance.MLJ.ROSE{Dict{Int64, Float64}, Int64, Float64}, ::Any, X::Matrix{Float64}, y::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}) @ Imbalance.MLJ ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/rose/interface_mlj.jl:44 [inlined] [13] transform(mach::MLJBase.Machine{Imbalance.MLJ.ROSE{Dict{Int64, Float64}, Int64, Float64}, Imbalance.MLJ.ROSE{Dict{Int64, Float64}, Int64, Float64}, false}, Xraw::Matrix{Float64}, Xraw_more::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}) @ MLJBase ~/.julia/packages/MLJBase/ip7cA/src/operations.jl:143 [14] top-level scope @ ~/.julia/packages/Imbalance/mQpnn/test/interfaces/mlj_interface.jl:40 [15] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [16] macro expansion @ ~/.julia/packages/Imbalance/mQpnn/test/interfaces/mlj_interface.jl:62 [inlined] [17] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined] Progress: 67%|███████████████████████████▍ | ETA: 0:00:03 class: 0   Progress: 100%|█████████████████████████████████████████| Time: 0:00:05 class: 1 [ Info: After filtering, the mapping from each class to number of borderline points is (CategoricalValue(CategoricalArrays.CategoricalPool{Int64, UInt32}([0, 1, 2]). Progress: 67%|███████████████████████████▍ | ETA: 0:00:01 class: 1   Progress: 100%|█████████████████████████████████████████| Time: 0:00:02 class: 2 [ Info: After filtering, the mapping from each class to number of borderline points is (CategoricalValue(CategoricalArrays.CategoricalPool{Int64, UInt32}([0, 1, 2]). [ Info: After filtering, the mapping from each class to number of borderline points is (CategoricalValue(CategoricalArrays.CategoricalPool{Int64, UInt32}([0, 1, 2]). [ Info: After filtering, the mapping from each class to number of borderline points is (CategoricalValue(CategoricalArrays.CategoricalPool{Int64, UInt32}([0, 1, 2]). SMOTENC MLJ: Error During Test at /home/pkgeval/.julia/packages/Imbalance/mQpnn/test/interfaces/mlj_interface.jl:146 Test threw exception Expression: transform(mach, X, y) == smotenc(X, y; k = 5, ratios = Dict(0 => 1.2, 1 => 1.2, 2 => 1.2), rng = 42) MethodError: no method matching reducedim1(::Matrix{Float64}, ::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Vector{Int64}, Vector{Int64}}, false}) The function `reducedim1` exists, but no method is defined for this combination of argument types. Closest candidates are: reducedim1(::Any) @ Base reducedim.jl:1229 Stacktrace: [1] centralize_sumabs2!(R::Matrix{Float64}, A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Vector{Int64}, Vector{Int64}}, false}, means::Matrix{Float64}) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:282 [2] varm!(R::Matrix{Float64}, A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Vector{Int64}, Vector{Int64}}, false}, m::Matrix{Float64}; corrected::Bool) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:309 [3] _varm(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Vector{Int64}, Vector{Int64}}, false}, m::Matrix{Float64}, corrected::Bool, region::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:341 [4] varm(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Vector{Int64}, Vector{Int64}}, false}, m::Matrix{Float64}; corrected::Bool, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:339 [inlined] [5] _var(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Vector{Int64}, Vector{Int64}}, false}, corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:386 [inlined] [6] var(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Vector{Int64}, Vector{Int64}}, false}; corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:380 [inlined] [7] _std(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Vector{Int64}, Vector{Int64}}, false}, corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:469 [8] std(A::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Vector{Int64}, Vector{Int64}}, false}; corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:461 [inlined] [9] get_penalty(X::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, cont_inds::Vector{Int64}) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/smotenc/smotenc.jl:45 [10] smotenc_per_class(X::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, n::Int64, cont_inds::Vector{Int64}, cat_inds::Vector{Int64}; k::Int64, knn_tree::String, rng::Xoshiro) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/smotenc/smotenc.jl:135 [11] generic_oversample(::Matrix{Float64}, ::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}, ::typeof(Imbalance.smotenc_per_class), ::Vector{Int64}, ::Vararg{Vector{Int64}}; ratios::Dict{Int64, Float64}, pass_inds::Bool, is_transposed::Bool, kwargs::@Kwargs{k::Int64, knn_tree::String, rng::Xoshiro}) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/generic_resample.jl:50 [12] smotenc(X::Matrix{Float64}, y::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}, cat_inds::Vector{Int64}; k::Int64, ratios::Dict{Int64, Float64}, knn_tree::String, rng::Int64, try_preserve_type::Bool) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/smotenc/smotenc.jl:309 [inlined] [13] tablify(matrix_func::typeof(smotenc), X::DataFrame, y::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}; try_preserve_type::Bool, encode_func::typeof(Imbalance.smotenc_encoder), decode_func::typeof(Imbalance.smotenc_decoder), kwargs::@Kwargs{k::Int64, ratios::Dict{Int64, Float64}, knn_tree::String, rng::Int64}) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/table_wrappers.jl:73 [14] smotenc(X::DataFrame, y::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}; k::Int64, ratios::Dict{Int64, Float64}, knn_tree::String, rng::Int64, try_preserve_type::Bool) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/smotenc/smotenc.jl:324 [inlined] [15] transform(s::Imbalance.MLJ.SMOTENC{Dict{Int64, Float64}, Int64, String, Int64}, ::Any, X::DataFrame, y::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}) @ Imbalance.MLJ ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/smotenc/interface_mlj.jl:46 [inlined] [16] transform(mach::MLJBase.Machine{Imbalance.MLJ.SMOTENC{Dict{Int64, Float64}, Int64, String, Int64}, Imbalance.MLJ.SMOTENC{Dict{Int64, Float64}, Int64, String, Int64}, false}, Xraw::DataFrame, Xraw_more::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}) @ MLJBase ~/.julia/packages/MLJBase/ip7cA/src/operations.jl:143 [17] top-level scope @ ~/.julia/packages/Imbalance/mQpnn/test/interfaces/mlj_interface.jl:124 [18] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [19] macro expansion @ ~/.julia/packages/Imbalance/mQpnn/test/interfaces/mlj_interface.jl:146 [inlined] [20] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined] RandomWalkOversampler MLJ: Error During Test at /home/pkgeval/.julia/packages/Imbalance/mQpnn/test/interfaces/mlj_interface.jl:174 Test threw exception Expression: transform(mach, X, y) == random_walk_oversample(X, y; ratios = Dict(0 => 1.2, 1 => 1.2, 2 => 1.2), rng = 42) MethodError: no method matching reducedim1(::Matrix{Float64}, ::Matrix{Float64}) The function `reducedim1` exists, but no method is defined for this combination of argument types. Closest candidates are: reducedim1(::Any) @ Base reducedim.jl:1229 Stacktrace: [1] centralize_sumabs2!(R::Matrix{Float64}, A::Matrix{Float64}, means::Matrix{Float64}) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:282 [2] varm!(R::Matrix{Float64}, A::Matrix{Float64}, m::Matrix{Float64}; corrected::Bool) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:309 [3] _varm(A::Matrix{Float64}, m::Matrix{Float64}, corrected::Bool, region::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:341 [4] varm(A::Matrix{Float64}, m::Matrix{Float64}; corrected::Bool, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:339 [inlined] [5] _var(A::Matrix{Float64}, corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:386 [inlined] [6] var(A::Matrix{Float64}; corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:380 [inlined] [7] _std(A::Matrix{Float64}, corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:469 [8] std(A::Matrix{Float64}; corrected::Bool, mean::Nothing, dims::Int64) @ Statistics ~/.julia/packages/Statistics/gbcbG/src/Statistics.jl:461 [inlined] [9] random_walk_per_class(X::SubArray{Float64, 2, LinearAlgebra.Transpose{Float64, Matrix{Float64}}, Tuple{Base.Slice{Base.OneTo{Int64}}, Vector{Int64}}, false}, n::Int64, cont_inds::Vector{Int64}, cat_inds::Vector{Int64}; rng::Xoshiro) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/random_walk/random_walk.jl:101 [10] generic_oversample(::Matrix{Float64}, ::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}, ::typeof(Imbalance.random_walk_per_class), ::Vector{Int64}, ::Vararg{Vector{Int64}}; ratios::Dict{Int64, Float64}, pass_inds::Bool, is_transposed::Bool, kwargs::@Kwargs{rng::Xoshiro}) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/generic_resample.jl:50 [11] random_walk_oversample(X::Matrix{Float64}, y::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}, cat_inds::Vector{Int64}; ratios::Dict{Int64, Float64}, rng::Int64, try_preserve_type::Bool) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/random_walk/random_walk.jl:258 [inlined] [12] tablify(matrix_func::typeof(random_walk_oversample), X::DataFrame, y::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}; try_preserve_type::Bool, encode_func::typeof(Imbalance.random_walk_encoder), decode_func::typeof(Imbalance.random_walk_decoder), kwargs::@Kwargs{ratios::Dict{Int64, Float64}, rng::Int64}) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/table_wrappers.jl:73 [13] random_walk_oversample(X::DataFrame, y::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}; ratios::Dict{Int64, Float64}, rng::Int64, try_preserve_type::Bool) @ Imbalance ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/random_walk/random_walk.jl:271 [inlined] [14] transform(s::Imbalance.MLJ.RandomWalkOversampler{Dict{Int64, Float64}, Int64}, ::Any, X::DataFrame, y::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}) @ Imbalance.MLJ ~/.julia/packages/Imbalance/mQpnn/src/oversampling_methods/random_walk/interface_mlj.jl:31 [inlined] [15] transform(mach::MLJBase.Machine{Imbalance.MLJ.RandomWalkOversampler{Dict{Int64, Float64}, Int64}, Imbalance.MLJ.RandomWalkOversampler{Dict{Int64, Float64}, Int64}, false}, Xraw::DataFrame, Xraw_more::CategoricalArrays.CategoricalVector{Int64, UInt32, Int64, CategoricalArrays.CategoricalValue{Int64, UInt32}, Union{}}) @ MLJBase ~/.julia/packages/MLJBase/ip7cA/src/operations.jl:143 [16] top-level scope @ ~/.julia/packages/Imbalance/mQpnn/test/interfaces/mlj_interface.jl:152 [17] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2247 [inlined] [18] macro expansion @ ~/.julia/packages/Imbalance/mQpnn/test/interfaces/mlj_interface.jl:174 [inlined] [19] macro expansion @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:781 [inlined] Progress: 67%|███████████████████████████▍ | ETA: 0:00:02 class: 0   Progress: 100%|█████████████████████████████████████████| Time: 0:00:03 class: 1 Progress: 67%|███████████████████████████▍ | ETA: 0:00:00 class: 0   Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 class: 1 Progress: 67%|███████████████████████████▍ | ETA: 0:00:00 class: 0   Progress: 100%|█████████████████████████████████████████| Time: 0:00:00 class: 1 Progress: 67%|███████████████████████████▍ | ETA: 0:00:09 class: 0   Progress: 100%|█████████████████████████████████████████| Time: 0:00:18 class: 1 Progress: 67%|███████████████████████████▍ | ETA: 0:00:01 class: 0   Progress: 100%|█████████████████████████████████████████| Time: 0:00:01 class: 1 Test Summary: | Pass Error Total Time MLJ Interface | 26 4 30 3m28.2s Random Oversampler MLJ | 3 3 57.3s ROSE MLJ | 1 2 3 17.7s SMOTE MLJ | 3 3 12.0s BorderlineSMOTE1 MLJ | 3 3 17.7s SMOTENC MLJ | 1 1 2 27.9s RandomWalkOversampler MLJ | 1 1 2 7.8s SMOTEN MLJ | 2 2 21.1s Random Undersampler MLJ | 3 3 5.6s Cluster Undersampler MLJ | 3 3 26.9s ENN Undersampler MLJ | 3 3 9.9s Tomek Undersampler MLJ | 3 3 4.2s RNG of the outermost testset: Xoshiro(0x4672cec76ee82464, 0xe7d5d4e10470dad7, 0x394da79f433f46f4, 0x46e0eef656897302, 0x0c9f1c432a393cbb) ERROR: LoadError: Some tests did not pass: 26 passed, 0 failed, 4 errored, 0 broken. in expression starting at /home/pkgeval/.julia/packages/Imbalance/mQpnn/test/runtests.jl:59 Testing failed after 516.71s ERROR: LoadError: Package Imbalance errored during testing Stacktrace: [1] pkgerror(msg::String) @ Pkg.Types /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Types.jl:68 [2] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, julia_args::Cmd, test_args::Cmd, test_fn::Nothing, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool) @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Operations.jl:3283 [3] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, test_fn::Nothing, julia_args::Cmd, test_args::Cmd, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool, kwargs::@Kwargs{io::IOContext{IO}}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:587 [4] test(pkgs::Vector{PackageSpec}; io::IOContext{IO}, kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:172 [5] test(pkgs::Vector{String}; kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:160 [6] test(pkg::String; kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:159 [inlined] [7] top-level scope @ /PkgEval.jl/scripts/evaluate.jl:223 in expression starting at /PkgEval.jl/scripts/evaluate.jl:214 PkgEval failed after 1310.48s: package tests unexpectedly errored