Package evaluation to test TextClassification on Julia 1.14.0-DEV.24 (d5fb6bbb43*) started at 2025-11-02T21:40:05.934 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 8.95s ################################################################################ # Installation # Installing TextClassification... Resolving package versions... Updating `~/.julia/environments/v1.14/Project.toml` [8e067cb0] + TextClassification v0.6.1 Updating `~/.julia/environments/v1.14/Manifest.toml` [7d9f7c33] + Accessors v0.1.42 [79e6a3ab] + Adapt v4.4.0 [dce04be8] + ArgCheck v2.5.0 [4fba245c] + ArrayInterface v7.22.0 [a9b6321e] + Atomix v1.1.2 [198e06fe] + BangBang v0.4.6 [9718e550] + Baselet v0.1.1 [62783981] + BitTwiddlingConvenienceFunctions v0.1.6 [2a0fbf3d] + CPUSummary v0.2.7 [d360d2e6] + ChainRulesCore v1.26.0 [fb6a15b2] + CloseOpenIntervals v0.1.13 [f70d9fcc] + CommonWorldInvalidations v1.0.0 [34da2185] + Compat v4.18.1 [a33af91c] + CompositionsBase v0.1.2 [187b0558] + ConstructionBase v1.6.0 [6add18c4] + ContextVariablesX v0.1.3 [adafc99b] + CpuId v0.3.1 [9a962f9c] + DataAPI v1.16.0 ⌅ [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [8bb1440f] + DelimitedFiles v1.9.1 [b4f34e82] + Distances v0.10.12 [ffbed154] + DocStringExtensions v0.9.5 [cc61a311] + FLoops v0.2.2 [b9860ae5] + FLoopsBase v0.1.1 [5789e2e9] + FileIO v1.17.1 [46192b85] + GPUArraysCore v0.2.0 [076d061b] + HashArrayMappedTries v0.2.0 [615f187c] + IfElse v0.1.1 [22cec73e] + InitialValues v0.3.1 [6d0fbc77] + Intersections v0.4.0 [3587e190] + InverseFunctions v0.1.17 ⌃ [b20bd276] + InvertedFiles v0.8.0 [92d709cd] + IrrationalConstants v0.2.6 [c8e1da08] + IterTools v1.10.0 [82899510] + IteratorInterfaceExtensions v1.0.0 ⌅ [033835bb] + JLD2 v0.4.55 [692b3bcd] + JLLWrappers v1.7.1 [b14d175d] + JuliaVariables v0.2.4 [63c18a36] + KernelAbstractions v0.9.38 [2d691ee1] + LIBLINEAR v0.7.1 [b1bec4e5] + LIBSVM v0.8.1 [10f19ff3] + LayoutPointers v0.1.17 [2ab3a3ac] + LogExpFunctions v0.3.29 [c2834f40] + MLCore v1.0.0 [d8e11817] + MLStyle v0.4.17 [f1d291b0] + MLUtils v0.4.8 [1914dd2f] + MacroTools v0.5.16 [d125e4d3] + ManualMemory v0.1.8 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [872c559c] + NNlib v0.9.31 [71a1bf82] + NameResolution v0.1.5 [bac558e1] + OrderedCollections v1.8.1 [d96e819e] + Parameters v0.12.3 [f517fe37] + Polyester v0.7.18 [1d0040c9] + PolyesterWeave v0.2.2 [aea7be01] + PrecompileTools v1.3.3 [21216c6a] + Preferences v1.5.0 [8162dcfd] + PrettyPrint v0.2.0 [92933f4c] + ProgressMeter v1.11.0 [ae029012] + Requires v1.3.1 [94e857df] + SIMDTypes v0.1.0 [431bcebd] + SciMLPublic v1.0.0 [6e75b9c4] + ScikitLearnBase v0.5.0 [7e506255] + ScopedValues v1.5.0 [0e966ebe] + SearchModels v0.4.1 [efcf1570] + Setfield v1.1.2 [605ecd9f] + ShowCases v0.1.0 ⌅ [053f045d] + SimilaritySearch v0.12.3 [699a6c99] + SimpleTraits v0.9.5 [a2af1166] + SortingAlgorithms v1.2.2 [171d559e] + SplittablesBase v0.1.15 [aedffcd0] + Static v1.3.1 [0d7ed370] + StaticArrayInterface v1.8.0 [90137ffa] + StaticArrays v1.9.15 [1e83bf80] + StaticArraysCore v1.4.4 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.1 ⌅ [2913bbd2] + StatsBase v0.33.21 [7792a7ef] + StrideArraysCore v0.5.8 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [8e067cb0] + TextClassification v0.6.1 ⌃ [7f6f6c8a] + TextSearch v0.19.5 [8290d209] + ThreadingUtilities v0.5.5 [3bb67fe8] + TranscodingStreams v0.11.3 [28d57a85] + Transducers v0.4.85 [3a884ed6] + UnPack v1.0.2 [013be700] + UnsafeAtomics v0.3.0 [1d63c593] + LLVMOpenMP_jll v18.1.8+0 [275f1f90] + liblinear_jll v2.47.0+0 [08558c22] + libsvm_jll v3.25.0+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 v1.7.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [b27032c2] + LibCURL v1.0.0 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.13.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.13.0 [de0858da] + Printf v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v1.0.0 [9e88b42a] + Serialization v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.11.0 [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 v1.3.0+1 [deac9b47] + LibCURL_jll v8.16.0+0 [e37daf67] + LibGit2_jll v1.9.1+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.9.9 [4536629a] + OpenBLAS_jll v0.3.29+0 [458c3c95] + OpenSSL_jll v3.5.4+0 [efcefdf7] + PCRE2_jll v10.47.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.1+2 [3161d3a3] + Zstd_jll v1.5.7+1 [8e850b90] + libblastrampoline_jll v5.15.0+0 [8e850ede] + nghttp2_jll v1.67.1+0 [3f19e933] + p7zip_jll v17.6.0+0 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Installation completed after 5.13s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... ┌ Error: Failed to use TestEnv.jl; test dependencies will not be precompiled │ exception = │ UndefVarError: `project_rel_path` not defined in `TestEnv` │ Suggestion: this global was defined as `Pkg.Operations.project_rel_path` but not assigned a value. │ Stacktrace: │ [1] get_test_dir(ctx::Pkg.Types.Context, pkgspec::PackageSpec) │ @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/common.jl:75 │ [2] test_dir_has_project_file │ @ ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/common.jl:52 [inlined] │ [3] maybe_gen_project_override! │ @ ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/common.jl:83 [inlined] │ [4] activate(pkg::String; allow_reresolve::Bool) │ @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/activate_set.jl:12 │ [5] activate(pkg::String) │ @ TestEnv ~/.julia/packages/TestEnv/nGMfF/src/julia-1.11/activate_set.jl:9 │ [6] top-level scope │ @ /PkgEval.jl/scripts/precompile.jl:24 │ [7] include(mod::Module, _path::String) │ @ Base ./Base.jl:309 │ [8] exec_options(opts::Base.JLOptions) │ @ Base ./client.jl:344 │ [9] _start() │ @ Base ./client.jl:577 └ @ Main /PkgEval.jl/scripts/precompile.jl:26 Precompiling package dependencies... Precompiling packages... 1237.9 ms ✓ Intersections 9034.7 ms ✓ StatsBase 1384.6 ms ✓ MicroCollections 5617.3 ms ✓ SearchModels 6386.5 ms ✓ Transducers 17314.7 ms ✓ SimilaritySearch 2009.6 ms ✓ Transducers → TransducersAdaptExt 14293.8 ms ✓ FLoops 10203.5 ms ✓ InvertedFiles 21850.3 ms ✓ MLUtils 12535.8 ms ✓ TextSearch 20351.1 ms ✓ TextClassification 12 dependencies successfully precompiled in 123 seconds. 147 already precompiled. Precompilation completed after 132.81s ################################################################################ # Testing # Testing TextClassification Status `/tmp/jl_ZzGho4/Project.toml` [4c88cf16] Aqua v0.8.14 [336ed68f] CSV v0.10.15 [944b1d66] CodecZlib v0.7.8 ⌃ [b20bd276] InvertedFiles v0.8.0 [c8e1da08] IterTools v1.10.0 [682c06a0] JSON v1.2.0 [b1bec4e5] LIBSVM v0.8.1 [f1d291b0] MLUtils v0.4.8 [d96e819e] Parameters v0.12.3 [0e966ebe] SearchModels v0.4.1 ⌅ [053f045d] SimilaritySearch v0.12.3 [82ae8749] StatsAPI v1.7.1 ⌅ [2913bbd2] StatsBase v0.33.21 [8e067cb0] TextClassification v0.6.1 ⌃ [7f6f6c8a] TextSearch v0.19.5 [8ba89e20] Distributed v1.11.0 [f43a241f] Downloads v1.7.0 [37e2e46d] LinearAlgebra v1.13.0 [9a3f8284] Random v1.11.0 [2f01184e] SparseArrays v1.13.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_ZzGho4/Manifest.toml` [7d9f7c33] Accessors v0.1.42 [79e6a3ab] Adapt v4.4.0 [4c88cf16] Aqua v0.8.14 [dce04be8] ArgCheck v2.5.0 [4fba245c] ArrayInterface v7.22.0 [a9b6321e] Atomix v1.1.2 [198e06fe] BangBang v0.4.6 [9718e550] Baselet v0.1.1 [62783981] BitTwiddlingConvenienceFunctions v0.1.6 [2a0fbf3d] CPUSummary v0.2.7 [336ed68f] CSV v0.10.15 [d360d2e6] ChainRulesCore v1.26.0 [fb6a15b2] CloseOpenIntervals v0.1.13 [944b1d66] CodecZlib v0.7.8 [f70d9fcc] CommonWorldInvalidations v1.0.0 [34da2185] Compat v4.18.1 [a33af91c] CompositionsBase v0.1.2 [187b0558] ConstructionBase v1.6.0 [6add18c4] ContextVariablesX v0.1.3 [adafc99b] CpuId v0.3.1 [9a962f9c] DataAPI v1.16.0 ⌅ [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [244e2a9f] DefineSingletons v0.1.2 [8bb1440f] DelimitedFiles v1.9.1 [b4f34e82] Distances v0.10.12 [ffbed154] DocStringExtensions v0.9.5 [cc61a311] FLoops v0.2.2 [b9860ae5] FLoopsBase v0.1.1 [5789e2e9] FileIO v1.17.1 [48062228] FilePathsBase v0.9.24 [46192b85] GPUArraysCore v0.2.0 [076d061b] HashArrayMappedTries v0.2.0 [615f187c] IfElse v0.1.1 [22cec73e] InitialValues v0.3.1 [842dd82b] InlineStrings v1.4.5 [6d0fbc77] Intersections v0.4.0 [3587e190] InverseFunctions v0.1.17 ⌃ [b20bd276] InvertedFiles v0.8.0 [92d709cd] IrrationalConstants v0.2.6 [c8e1da08] IterTools v1.10.0 [82899510] IteratorInterfaceExtensions v1.0.0 ⌅ [033835bb] JLD2 v0.4.55 [692b3bcd] JLLWrappers v1.7.1 [682c06a0] JSON v1.2.0 [b14d175d] JuliaVariables v0.2.4 [63c18a36] KernelAbstractions v0.9.38 [2d691ee1] LIBLINEAR v0.7.1 [b1bec4e5] LIBSVM v0.8.1 [10f19ff3] LayoutPointers v0.1.17 [2ab3a3ac] LogExpFunctions v0.3.29 [c2834f40] MLCore v1.0.0 [d8e11817] MLStyle v0.4.17 [f1d291b0] MLUtils v0.4.8 [1914dd2f] MacroTools v0.5.16 [d125e4d3] ManualMemory v0.1.8 [128add7d] MicroCollections v0.2.0 [e1d29d7a] Missings v1.2.0 [872c559c] NNlib v0.9.31 [71a1bf82] NameResolution v0.1.5 [bac558e1] OrderedCollections v1.8.1 [d96e819e] Parameters v0.12.3 [69de0a69] Parsers v2.8.3 [f517fe37] Polyester v0.7.18 [1d0040c9] PolyesterWeave v0.2.2 [2dfb63ee] PooledArrays v1.4.3 [aea7be01] PrecompileTools v1.3.3 [21216c6a] Preferences v1.5.0 [8162dcfd] PrettyPrint v0.2.0 [92933f4c] ProgressMeter v1.11.0 [ae029012] Requires v1.3.1 [94e857df] SIMDTypes v0.1.0 [431bcebd] SciMLPublic v1.0.0 [6e75b9c4] ScikitLearnBase v0.5.0 [7e506255] ScopedValues v1.5.0 [0e966ebe] SearchModels v0.4.1 [91c51154] SentinelArrays v1.4.8 [efcf1570] Setfield v1.1.2 [605ecd9f] ShowCases v0.1.0 ⌅ [053f045d] SimilaritySearch v0.12.3 [699a6c99] SimpleTraits v0.9.5 [a2af1166] SortingAlgorithms v1.2.2 [171d559e] SplittablesBase v0.1.15 [aedffcd0] Static v1.3.1 [0d7ed370] StaticArrayInterface v1.8.0 [90137ffa] StaticArrays v1.9.15 [1e83bf80] StaticArraysCore v1.4.4 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.7.1 ⌅ [2913bbd2] StatsBase v0.33.21 [7792a7ef] StrideArraysCore v0.5.8 [ec057cc2] StructUtils v2.5.1 [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.1 [8e067cb0] TextClassification v0.6.1 ⌃ [7f6f6c8a] TextSearch v0.19.5 [8290d209] ThreadingUtilities v0.5.5 [3bb67fe8] TranscodingStreams v0.11.3 [28d57a85] Transducers v0.4.85 [3a884ed6] UnPack v1.0.2 [013be700] UnsafeAtomics v0.3.0 [ea10d353] WeakRefStrings v1.4.2 [76eceee3] WorkerUtilities v1.6.1 [1d63c593] LLVMOpenMP_jll v18.1.8+0 [275f1f90] liblinear_jll v2.47.0+0 [08558c22] libsvm_jll v3.25.0+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 v1.7.0 [7b1f6079] FileWatching v1.11.0 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [ac6e5ff7] JuliaSyntaxHighlighting v1.12.0 [b27032c2] LibCURL v1.0.0 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.13.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.3.0 [44cfe95a] Pkg v1.13.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v1.0.0 [9e88b42a] Serialization v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.13.0 [f489334b] StyledStrings v1.11.0 [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 v1.3.0+1 [deac9b47] LibCURL_jll v8.16.0+0 [e37daf67] LibGit2_jll v1.9.1+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2025.9.9 [4536629a] OpenBLAS_jll v0.3.29+0 [458c3c95] OpenSSL_jll v3.5.4+0 [efcefdf7] PCRE2_jll v10.47.0+0 [bea87d4a] SuiteSparse_jll v7.10.1+0 [83775a58] Zlib_jll v1.3.1+2 [3161d3a3] Zstd_jll v1.5.7+1 [8e850b90] libblastrampoline_jll v5.15.0+0 [8e850ede] nghttp2_jll v1.67.1+0 [3f19e933] p7zip_jll v17.6.0+0 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... t = "Es muy triste que exista esto y no poder ir. _emo _url" t = "No mamen, me llego un sentimiento de tristeza bien cabrón. _emo _emo _emo _emo _emo" t = "Háblame si vieras cuanto bien me haría oír tu voz... _emo" t = "Mas que be decido. ¡les amo!. _emo _emo _emo _emo _url" t = "Tu saltas, yo salto, no importa si es de paracaídas o bungee. Te amo demasiado brouu. #YoMeRifoConJuanpa _emo _num" t = "Justo en el cora _emo" t = "_usr _usr _usr _usr _usr _usr _num,_num votos para mis héroes _usr .:*･ _emo VAMOS CODERS #CD_num #AlonsoVillalpandoTrendy #Coders #KCAMexico" t = "_usr Felicidades bebé _emo" t = "CDMX te veo en marzo _emo _emo" t = "Desde hoyyyyyyy Hugo ya no es mi primo fav _emo _emo _emo _emo" countmap(trainlabels) = Dict{Any, Int64}("♡" => 536, "💔" => 557) countmap(testlabels) = Dict{Any, Int64}("♡" => 243, "💔" => 226) SearchModels> search params iter=0, initialpopulation=32, maxpopulation=8, bsize=2, mutbsize=8, crossbsize=8 .* optimization finished, #iter = 1087 obj = -276.685080, rho = -0.189250 nSV = 579, nBSV = 259 Total nSV = 579 *.* optimization finished, #iter = 1017 obj = -284.655950, rho = -0.118444 nSV = 487, nBSV = 296 Total nSV = 487 *.* optimization finished, #iter = 1050 obj = -266.441195, rho = -0.074289 nSV = 405, nBSV = 266 Total nSV = 405 *.* optimization finished, #iter = 822 obj = -280.622069, rho = -0.107101 nSV = 413, nBSV = 277 Total nSV = 413 .* optimization finished, #iter = 1086 obj = -243.949055, rho = -0.049156 nSV = 436, nBSV = 236 Total nSV = 436 *.* optimization finished, #iter = 995 obj = -256.454081, rho = -0.156192 nSV = 414, nBSV = 252 Total nSV = 414 .* optimization finished, #iter = 1072 obj = -293.044247, rho = -0.187509 nSV = 484, nBSV = 298 Total nSV = 484 *.* optimization finished, #iter = 975 obj = -320.550587, rho = -0.123982 nSV = 487, nBSV = 333 Total nSV = 487 .* optimization finished, #iter = 1193 obj = -265.300680, rho = -0.163878 nSV = 576, nBSV = 241 Total nSV = 576 .* optimization finished, #iter = 1144 obj = -268.444204, rho = -0.051457 nSV = 555, nBSV = 257 Total nSV = 555 .* optimization finished, #iter = 1138 obj = -301.784723, rho = -0.239033 nSV = 469, nBSV = 315 Total nSV = 469 .* optimization finished, #iter = 1109 obj = -264.690060, rho = -0.112627 nSV = 577, nBSV = 248 Total nSV = 577 .* optimization finished, #iter = 1194 obj = -312.572797, rho = -0.190700 nSV = 477, nBSV = 315 Total nSV = 477 *.* optimization finished, #iter = 998 obj = -278.782380, rho = -0.058203 nSV = 420, nBSV = 284 Total nSV = 420 *.* optimization finished, #iter = 955 obj = -286.787770, rho = 0.024541 nSV = 423, nBSV = 292 Total nSV = 423 .* optimization finished, #iter = 1003 obj = -293.330298, rho = -0.120803 nSV = 525, nBSV = 292 Total nSV = 525 .* optimization finished, #iter = 1011 obj = -278.001779, rho = -0.095461 nSV = 531, nBSV = 269 Total nSV = 531 *.* optimization finished, #iter = 1006 obj = -271.518596, rho = -0.070780 nSV = 414, nBSV = 273 Total nSV = 414 *.* optimization finished, #iter = 911 obj = -231.733927, rho = -0.072256 nSV = 429, nBSV = 230 Total nSV = 429 .* optimization finished, #iter = 1002 obj = -302.504877, rho = -0.160287 nSV = 490, nBSV = 317 Total nSV = 490 .* optimization finished, #iter = 1135 obj = -284.555470, rho = -0.185809 nSV = 483, nBSV = 295 Total nSV = 483 *.* optimization finished, #iter = 971 obj = -320.550587, rho = -0.123901 nSV = 488, nBSV = 333 Total nSV = 488 *.* optimization finished, #iter = 975 obj = -305.728509, rho = -0.092104 nSV = 492, nBSV = 325 Total nSV = 492 *.* optimization finished, #iter = 1033 obj = -244.842798, rho = -0.218537 nSV = 447, nBSV = 236 Total nSV = 447 *.* optimization finished, #iter = 1003 obj = -208.835130, rho = -0.068605 nSV = 433, nBSV = 195 Total nSV = 433 *.* optimization finished, #iter = 815 obj = -286.231168, rho = -0.059661 nSV = 414, nBSV = 294 Total nSV = 414 .* optimization finished, #iter = 1281 obj = -297.676275, rho = -0.205253 nSV = 503, nBSV = 298 Total nSV = 503 *.* optimization finished, #iter = 997 obj = -289.220389, rho = -0.092799 nSV = 419, nBSV = 298 Total nSV = 419 *.* optimization finished, #iter = 966 obj = -283.299626, rho = -0.110879 nSV = 498, nBSV = 296 Total nSV = 498 *.* optimization finished, #iter = 979 obj = -311.318744, rho = -0.071053 nSV = 488, nBSV = 329 Total nSV = 488 .* optimization finished, #iter = 1268 obj = -170.777799, rho = -0.073884 nSV = 475, nBSV = 122 Total nSV = 475 *.* optimization finished, #iter = 1039 obj = -245.239710, rho = -0.196234 nSV = 444, nBSV = 229 Total nSV = 444 SearchModels iteration 1> population: 8, bsize: 2, queue: 8, observed: 40, best-error: 0.1992088371398717 worst-error: 0.21820420958351994 ┌ Warning: using 715 of 765 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[2], TextSearch.Skipgram[TextSearch.Skipgram(3, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5384615384615384 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 637 obj = -144.547356, rho = -0.263721 nSV = 567, nBSV = 491 Total nSV = 567 ┌ Warning: using 763 of 765 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 979 obj = -332.300540, rho = -0.271196 nSV = 460, nBSV = 355 Total nSV = 460 .*.* optimization finished, #iter = 2210 obj = -422.266384, rho = -0.135749 nSV = 498, nBSV = 62 Total nSV = 498 .* optimization finished, #iter = 1139 obj = -265.716163, rho = -0.166809 nSV = 581, nBSV = 243 Total nSV = 581 * optimization finished, #iter = 730 obj = -142.731701, rho = -0.162422 nSV = 624, nBSV = 503 Total nSV = 624 * optimization finished, #iter = 709 obj = -140.954840, rho = -0.094242 nSV = 614, nBSV = 503 Total nSV = 614 [ Info: ignoring configuration due to exception empty vocabulary(VectorModelConfig = TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() min_token_ndocs: Int64 3 max_token_pdocs: Float64 0.0 , TextConfig = TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()))[ Info: ignoring configuration due to exception empty vocabulary(VectorModelConfig = TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.BinaryGlobalWeighting} global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() min_token_ndocs: Int64 3 max_token_pdocs: Float64 0.0 , TextConfig = TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()))SearchModels iteration 2> population: 8, bsize: 2, queue: 10, observed: 50, best-error: 0.1992088371398717 worst-error: 0.2173085535154501 ┌ Warning: using 716 of 765 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[2], TextSearch.Skipgram[TextSearch.Skipgram(3, 1)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.9099999999999999 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 663 obj = -144.898434, rho = -0.245283 nSV = 569, nBSV = 489 Total nSV = 569 .*.* optimization finished, #iter = 1845 obj = -439.941478, rho = -0.203800 nSV = 513, nBSV = 69 Total nSV = 513 * optimization finished, #iter = 704 obj = -139.432210, rho = -0.097250 nSV = 606, nBSV = 492 Total nSV = 606 .*.* optimization finished, #iter = 1844 obj = -447.822830, rho = -0.031674 nSV = 513, nBSV = 79 Total nSV = 513 ┌ Warning: using 764 of 765 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 494 nu = 0.714710 obj = -142.150094, rho = -0.185046 nSV = 595, nBSV = 483 Total nSV = 595 ┌ Warning: using 764 of 765 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .*.* optimization finished, #iter = 2052 nu = 0.408712 obj = -684.316858, rho = -0.166828 nSV = 432, nBSV = 198 Total nSV = 432 *.* optimization finished, #iter = 973 obj = -296.645118, rho = -0.023870 nSV = 511, nBSV = 316 Total nSV = 511 .* optimization finished, #iter = 1215 obj = -270.690109, rho = -0.070900 nSV = 545, nBSV = 264 Total nSV = 545 [ Info: ignoring configuration due to exception empty vocabulary(VectorModelConfig = TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.BinaryGlobalWeighting} global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() min_token_ndocs: Int64 3 max_token_pdocs: Float64 0.0 , TextConfig = TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3, 5], Int8[1, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()))[ Info: ignoring configuration due to exception empty vocabulary(VectorModelConfig = TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() min_token_ndocs: Int64 3 max_token_pdocs: Float64 0.0 , TextConfig = TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3, 5], Int8[1, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()))SearchModels iteration 3> population: 8, bsize: 2, queue: 8, observed: 58, best-error: 0.1992088371398717 worst-error: 0.21544260337363785 * optimization finished, #iter = 704 obj = -139.432211, rho = -0.097250 nSV = 606, nBSV = 492 Total nSV = 606 *.* optimization finished, #iter = 974 obj = -298.188880, rho = -0.074949 nSV = 498, nBSV = 312 Total nSV = 498 .* optimization finished, #iter = 1214 obj = -270.690105, rho = -0.070908 nSV = 545, nBSV = 264 Total nSV = 545 .* optimization finished, #iter = 1011 obj = -278.001779, rho = -0.095461 nSV = 531, nBSV = 269 Total nSV = 531 .* optimization finished, #iter = 1132 obj = -270.035662, rho = -0.055791 nSV = 547, nBSV = 268 Total nSV = 547 ┌ Warning: using 763 of 765 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.65 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 705 obj = -137.709066, rho = -0.194451 nSV = 518, nBSV = 446 Total nSV = 518 * optimization finished, #iter = 710 obj = -138.680178, rho = -0.129780 nSV = 594, nBSV = 484 Total nSV = 594 * optimization finished, #iter = 622 obj = -144.695068, rho = -0.420139 nSV = 553, nBSV = 499 Total nSV = 553 SearchModels iteration 4> population: 8, bsize: 2, queue: 5, observed: 63, best-error: 0.1992088371398717 worst-error: 0.21081504702194365 ┌ Warning: using 715 of 765 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.65 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .*.* optimization finished, #iter = 1925 obj = -799.512752, rho = -0.187611 nSV = 428, nBSV = 232 Total nSV = 428 .*.* optimization finished, #iter = 1704 obj = -511.624998, rho = -0.110517 nSV = 515, nBSV = 121 Total nSV = 515 .*.* optimization finished, #iter = 1854 obj = -367.387821, rho = -0.166813 nSV = 545, nBSV = 33 Total nSV = 545 ┌ Warning: using 763 of 765 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 1042 obj = -308.194239, rho = -0.153091 nSV = 480, nBSV = 314 Total nSV = 480 [ Info: ignoring configuration due to exception empty vocabulary(VectorModelConfig = TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() min_token_ndocs: Int64 3 max_token_pdocs: Float64 0.0 , TextConfig = TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[5], Int8[1, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()))SearchModels> stop by convergence error={MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() min_token_ndocs: Int64 4 max_token_pdocs: Float64 0.9099999999999999 LIBSVMConfig } => 0.21081504702194365, iter=5 (of 16) .* optimization finished, #iter = 1336 obj = -387.205553, rho = -0.187481 nSV = 717, nBSV = 388 Total nSV = 717 ┌ Info: ("-- perf best_lists[1]:", 1, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[5], Int8[1, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.65 └ LIBSVMConfig }, 0.1992088371398717) ┌ Info: ("-- perf best_lists[2]:", 2, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[5], Int8[1, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5 └ LIBSVMConfig }, 0.1992088371398717) ┌ Info: ("-- perf best_lists[3]:", 3, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1, 2], TextSearch.Skipgram[TextSearch.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.FreqWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.9099999999999999 └ LIBSVMConfig }, 0.20596357665323173) ┌ Info: ("-- perf best_lists[4]:", 4, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.7 └ LIBSVMConfig }, 0.20670995670995662) ┌ Info: ("-- perf best_lists[5]:", 5, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TfWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.7 └ LIBSVMConfig }, 0.20670995670995662) ┌ Info: ("-- perf best_lists[6]:", 6, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 └ LIBSVMConfig }, 0.20958351992834756) ┌ Info: ("-- perf best_lists[7]:", 7, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.FreqWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.7 └ LIBSVMConfig }, 0.21081504702194365) ┌ Info: ("-- perf best_lists[8]:", 8, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.9099999999999999 └ LIBSVMConfig }, 0.21081504702194365) ┌ Info: *** Performance on test: └ sc = (microf1 = 0.7718550106609808, precision = 0.7718550106609808, macroprecision = 0.7735984972827078, recall = 0.7718550106609808, macrorecall = 0.7731800138388143, macrof1 = 0.7718384141489918, accuracy = 0.7718550106609808, classf1 = Dict{Any, Any}("♡" => 0.7698924731182796, "💔" => 0.773784355179704), classprecision = Dict{Any, Any}("♡" => 0.8063063063063063, "💔" => 0.7408906882591093), classrecall = Dict{Any, Any}("♡" => 0.7366255144032922, "💔" => 0.8097345132743363)) SearchModels> search params iter=0, initialpopulation=32, maxpopulation=8, bsize=2, mutbsize=8, crossbsize=8 ┌ Warning: using 727 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[5], Int8[], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TfWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ min_token_ndocs: Int64 11 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 912 obj = -313.189176, rho = 0.161227 nSV = 463, nBSV = 324 Total nSV = 463 .* optimization finished, #iter = 1011 obj = -270.544701, rho = 0.045373 nSV = 493, nBSV = 272 Total nSV = 493 *.* optimization finished, #iter = 944 obj = -280.863983, rho = 0.038520 nSV = 486, nBSV = 291 Total nSV = 486 .* optimization finished, #iter = 994 obj = -264.951706, rho = 0.122104 nSV = 494, nBSV = 276 Total nSV = 494 *.* optimization finished, #iter = 974 obj = -266.360346, rho = 0.037547 nSV = 512, nBSV = 269 Total nSV = 512 *.* optimization finished, #iter = 912 obj = -313.189178, rho = 0.161227 nSV = 463, nBSV = 324 Total nSV = 463 .* optimization finished, #iter = 1006 obj = -333.340790, rho = 0.236450 nSV = 468, nBSV = 343 Total nSV = 468 vectorizing corpus 0%|▏ | ETA: 0:07:44vectorizing corpus 0%|▏ | ETA: 0:08:08vectorizing corpus 0%|▏ | ETA: 0:09:39vectorizing corpus 0%|▏ | ETA: 0:10:01vectorizing corpus 0%|▏ | ETA: 0:10:24 *.* optimization finished, #iter = 885 obj = -262.572390, rho = 0.059837 nSV = 398, nBSV = 265 Total nSV = 398 *.* optimization finished, #iter = 994 obj = -207.598017, rho = 0.083342 nSV = 412, nBSV = 198 Total nSV = 412 *.* optimization finished, #iter = 910 obj = -253.590674, rho = 0.083098 nSV = 398, nBSV = 247 Total nSV = 398 *.* optimization finished, #iter = 940 obj = -257.148994, rho = -0.310851 nSV = 397, nBSV = 251 Total nSV = 397 *.* optimization finished, #iter = 859 obj = -250.754971, rho = -0.159082 nSV = 405, nBSV = 255 Total nSV = 405 *.* optimization finished, #iter = 984 obj = -197.307902, rho = -0.039004 nSV = 401, nBSV = 187 Total nSV = 401 *.* optimization finished, #iter = 887 obj = -247.655693, rho = -0.026401 nSV = 394, nBSV = 250 Total nSV = 394 *.* optimization finished, #iter = 896 obj = -240.211346, rho = -0.044167 nSV = 405, nBSV = 245 Total nSV = 405 .* optimization finished, #iter = 1046 obj = -182.414134, rho = 0.076469 nSV = 426, nBSV = 169 Total nSV = 426 .* optimization finished, #iter = 1086 obj = -234.109205, rho = 0.142004 nSV = 391, nBSV = 223 Total nSV = 391 *.* optimization finished, #iter = 1018 obj = -263.236449, rho = -0.189394 nSV = 396, nBSV = 267 Total nSV = 396 *.* optimization finished, #iter = 876 obj = -219.342619, rho = 0.021246 nSV = 411, nBSV = 215 Total nSV = 411   vectorizing corpus 0%|▏ | ETA: 0:10:10vectorizing corpus 0%|▏ | ETA: 0:10:01vectorizing corpus 1%|▏ | ETA: 0:04:39vectorizing corpus 1%|▏ | ETA: 0:04:02vectorizing corpus 0%|▏ | ETA: 0:07:58vectorizing corpus 1%|▏ | ETA: 0:03:19vectorizing corpus 0%|▏ | ETA: 0:06:31vectorizing corpus 0%|▏ | ETA: 0:06:22vectorizing corpus 65%|█████████████████████▍ | ETA: 0:00:01vectorizing corpus 38%|████████████▋ | ETA: 0:00:02vectorizing corpus 45%|██████████████▉ | ETA: 0:00:01 vectorizing corpus 1%|▏ | ETA: 0:05:25vectorizing corpus 1%|▏ | ETA: 0:04:58vectorizing corpus 1%|▏ | ETA: 0:04:45vectorizing corpus 1%|▏ | ETA: 0:04:19vectorizing corpus 1%|▏ | ETA: 0:03:50vectorizing corpus 1%|▏ | ETA: 0:03:22  *.* optimization finished, #iter = 903 obj = -329.521690, rho = -0.357762 nSV = 459, nBSV = 341 Total nSV = 459 vectorizing corpus 20%|██████▋ | ETA: 0:00:04vectorizing corpus 1%|▌ | ETA: 0:01:12vectorizing corpus 0%|▏ | ETA: 0:04:02vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:09vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:09vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:09    vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06  .* optimization finished, #iter = 1053 obj = -250.498597, rho = -0.142331 nSV = 409, nBSV = 247 Total nSV = 409 *.* optimization finished, #iter = 826 obj = -241.925848, rho = -0.158304 nSV = 413, nBSV = 245 Total nSV = 413 .* optimization finished, #iter = 1079 obj = -186.511035, rho = -0.190864 nSV = 437, nBSV = 164 Total nSV = 437 *.* optimization finished, #iter = 835 obj = -242.134799, rho = -0.160457 nSV = 388, nBSV = 245 Total nSV = 388 *.* optimization finished, #iter = 1017 obj = -266.954869, rho = -0.091815 nSV = 408, nBSV = 269 Total nSV = 408 *.* optimization finished, #iter = 903 obj = -224.700150, rho = -0.168549 nSV = 410, nBSV = 219 Total nSV = 410  vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04 .* optimization finished, #iter = 995 obj = -290.254887, rho = 0.197661 nSV = 480, nBSV = 301 Total nSV = 480 .* optimization finished, #iter = 1048 obj = -272.373496, rho = 0.143945 nSV = 483, nBSV = 280 Total nSV = 483 .* optimization finished, #iter = 999 obj = -295.615525, rho = 0.156222 nSV = 467, nBSV = 310 Total nSV = 467 .* optimization finished, #iter = 1032 obj = -292.004972, rho = 0.179689 nSV = 486, nBSV = 298 Total nSV = 486 .* optimization finished, #iter = 1039 obj = -259.525290, rho = 0.120310 nSV = 546, nBSV = 241 Total nSV = 546 *.* optimization finished, #iter = 969 obj = -336.486258, rho = -0.464957 nSV = 462, nBSV = 345 Total nSV = 462 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04 .* optimization finished, #iter = 1103 obj = -170.018762, rho = 0.053436 nSV = 433, nBSV = 136 Total nSV = 433 .* optimization finished, #iter = 1061 obj = -268.503984, rho = -0.021513 nSV = 394, nBSV = 258 Total nSV = 394 *.* optimization finished, #iter = 942 obj = -264.118609, rho = 0.098114 nSV = 395, nBSV = 263 Total nSV = 395 *.* optimization finished, #iter = 881 obj = -197.306893, rho = -0.038911 nSV = 402, nBSV = 188 Total nSV = 402 *.* optimization finished, #iter = 801 obj = -259.073680, rho = -0.101409 nSV = 384, nBSV = 270 Total nSV = 384  *.* optimization finished, #iter = 883 obj = -239.698430, rho = 0.056148 nSV = 401, nBSV = 245 Total nSV = 401 *.* optimization finished, #iter = 885 obj = -257.237139, rho = -0.098052 nSV = 393, nBSV = 264 Total nSV = 393 *.* optimization finished, #iter = 886 obj = -241.436726, rho = -0.073693 nSV = 386, nBSV = 250 Total nSV = 386 vectorizing corpus 1%|▏ | ETA: 0:03:51vectorizing corpus 1%|▏ | ETA: 0:03:17  vectorizing corpus 1%|▏ | ETA: 0:06:34vectorizing corpus 1%|▏ | ETA: 0:06:21vectorizing corpus 1%|▏ | ETA: 0:05:16vectorizing corpus 1%|▏ | ETA: 0:04:11vectorizing corpus 1%|▏ | ETA: 0:03:42vectorizing corpus 1%|▏ | ETA: 0:04:08vectorizing corpus 1%|▏ | ETA: 0:03:07  .* optimization finished, #iter = 1049 obj = -263.454110, rho = -0.287447 nSV = 492, nBSV = 258 Total nSV = 492 .* optimization finished, #iter = 1043 obj = -265.905085, rho = -0.230874 nSV = 527, nBSV = 266 Total nSV = 527 *.* optimization finished, #iter = 869 obj = -301.897175, rho = -0.395372 nSV = 452, nBSV = 312 Total nSV = 452 *.* optimization finished, #iter = 936 obj = -246.107720, rho = -0.249367 nSV = 396, nBSV = 250 Total nSV = 396 *.* optimization finished, #iter = 1012 obj = -236.714894, rho = -0.198261 nSV = 410, nBSV = 237 Total nSV = 410 .* optimization finished, #iter = 1079 obj = -175.801924, rho = -0.092268 nSV = 417, nBSV = 150 Total nSV = 417 *.* optimization finished, #iter = 880 obj = -242.813565, rho = -0.231738 nSV = 398, nBSV = 236 Total nSV = 398 *.* optimization finished, #iter = 944 obj = -260.125947, rho = -0.089205 nSV = 400, nBSV = 262 Total nSV = 400 *.* optimization finished, #iter = 964 obj = -226.106765, rho = -0.072823 nSV = 421, nBSV = 226 Total nSV = 421 *.* optimization finished, #iter = 919 obj = -292.339097, rho = -0.342521 nSV = 463, nBSV = 316 Total nSV = 463 .* optimization finished, #iter = 1108 obj = -279.195970, rho = -0.259373 nSV = 476, nBSV = 274 Total nSV = 476 *.* optimization finished, #iter = 952 obj = -254.220327, rho = -0.241908 nSV = 551, nBSV = 248 Total nSV = 551 vectorizing corpus 0%|▏ | ETA: 0:10:06vectorizing corpus 0%|▏ | ETA: 0:10:06vectorizing corpus 0%|▏ | ETA: 0:10:06 vectorizing corpus 1%|▏ | ETA: 0:05:30vectorizing corpus 1%|▏ | ETA: 0:04:51vectorizing corpus 1%|▏ | ETA: 0:04:28vectorizing corpus 1%|▏ | ETA: 0:04:44vectorizing corpus 1%|▏ | ETA: 0:04:09vectorizing corpus 1%|▏ | ETA: 0:03:34vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:09vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:09vectorizing corpus 1%|▏ | ETA: 0:03:26vectorizing corpus 93%|██████████████████████████████▌ | ETA: 0:00:00   *.* optimization finished, #iter = 978 obj = -250.025877, rho = -0.090396 nSV = 396, nBSV = 245 Total nSV = 396 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:09vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:09vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 0%|▏ | ETA: 0:09:42vectorizing corpus 0%|▏ | ETA: 0:09:34vectorizing corpus 0%|▏ | ETA: 0:09:15vectorizing corpus 0%|▏ | ETA: 0:09:09vectorizing corpus 0%|▏ | ETA: 0:09:03vectorizing corpus 0%|▏ | ETA: 0:08:57 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07  *.* optimization finished, #iter = 901 obj = -285.323782, rho = -0.281448 nSV = 461, nBSV = 301 Total nSV = 461 .* optimization finished, #iter = 998 obj = -281.634156, rho = -0.302213 nSV = 481, nBSV = 280 Total nSV = 481 .* optimization finished, #iter = 994 obj = -251.568478, rho = -0.133553 nSV = 548, nBSV = 236 Total nSV = 548 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07  vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04  vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04 *.* optimization finished, #iter = 869 obj = -301.897180, rho = -0.395371 nSV = 452, nBSV = 312 Total nSV = 452 .* optimization finished, #iter = 1026 obj = -268.356612, rho = -0.283307 nSV = 494, nBSV = 270 Total nSV = 494 *.* optimization finished, #iter = 887 obj = -275.288947, rho = -0.262559 nSV = 474, nBSV = 284 Total nSV = 474 .* optimization finished, #iter = 1041 obj = -278.214884, rho = -0.272311 nSV = 469, nBSV = 276 Total nSV = 469 .* optimization finished, #iter = 1022 obj = -272.501420, rho = -0.357334 nSV = 476, nBSV = 281 Total nSV = 476 *.* optimization finished, #iter = 918 obj = -235.301227, rho = -0.179769 nSV = 375, nBSV = 240 Total nSV = 375 vectorizing corpus 1%|▏ | ETA: 0:03:04  *.* optimization finished, #iter = 823 obj = -273.243994, rho = -0.123268 nSV = 400, nBSV = 282 Total nSV = 400 *.* optimization finished, #iter = 889 obj = -216.300897, rho = -0.155485 nSV = 410, nBSV = 213 Total nSV = 410 *.* optimization finished, #iter = 923 obj = -266.264698, rho = -0.193355 nSV = 399, nBSV = 270 Total nSV = 399 *.* optimization finished, #iter = 907 obj = -265.338024, rho = 0.023613 nSV = 404, nBSV = 266 Total nSV = 404 .* optimization finished, #iter = 1087 obj = -251.561512, rho = -0.118938 nSV = 413, nBSV = 252 Total nSV = 413 *.* optimization finished, #iter = 1075 obj = -202.503192, rho = -0.150804 nSV = 416, nBSV = 195 Total nSV = 416 *.* optimization finished, #iter = 855 obj = -248.800315, rho = -0.162266 nSV = 395, nBSV = 244 Total nSV = 395 *.* optimization finished, #iter = 1010 obj = -260.932851, rho = -0.144163 nSV = 409, nBSV = 265 Total nSV = 409 vectorizing corpus 0%|▏ | ETA: 0:18:41vectorizing corpus 0%|▏ | ETA: 0:18:32vectorizing corpus 0%|▏ | ETA: 0:17:19vectorizing corpus 0%|▏ | ETA: 0:12:46vectorizing corpus 0%|▏ | ETA: 0:12:39   .* optimization finished, #iter = 1083 obj = -281.895584, rho = -0.297603 nSV = 477, nBSV = 284 Total nSV = 477 *.* optimization finished, #iter = 1006 obj = -264.408591, rho = -0.326762 nSV = 471, nBSV = 275 Total nSV = 471 vectorizing corpus 0%|▏ | ETA: 0:06:52vectorizing corpus 0%|▏ | ETA: 0:06:52vectorizing corpus 0%|▏ | ETA: 0:06:52vectorizing corpus 0%|▏ | ETA: 0:06:51vectorizing corpus 0%|▏ | ETA: 0:06:51vectorizing corpus 1%|▏ | ETA: 0:03:08 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07  vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06   *.* optimization finished, #iter = 896 obj = -213.465331, rho = -0.135082 nSV = 397, nBSV = 208 Total nSV = 397 *.* optimization finished, #iter = 829 obj = -270.165871, rho = -0.223592 nSV = 402, nBSV = 272 Total nSV = 402 *.* optimization finished, #iter = 1015 obj = -259.115819, rho = 0.056841 nSV = 404, nBSV = 265 Total nSV = 404 *.* optimization finished, #iter = 1036 obj = -250.947236, rho = -0.066567 nSV = 423, nBSV = 251 Total nSV = 423 .* optimization finished, #iter = 1061 obj = -200.301631, rho = -0.004772 nSV = 413, nBSV = 189 Total nSV = 413 *.* optimization finished, #iter = 840 obj = -242.115740, rho = -0.143225 nSV = 394, nBSV = 252 Total nSV = 394 *.* optimization finished, #iter = 873 obj = -257.869319, rho = -0.180444 nSV = 393, nBSV = 270 Total nSV = 393 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04 .* optimization finished, #iter = 1163 obj = -169.322587, rho = -0.148122 nSV = 452, nBSV = 127 Total nSV = 452 *.* optimization finished, #iter = 925 obj = -280.299732, rho = -0.076442 nSV = 406, nBSV = 276 Total nSV = 406 *.* optimization finished, #iter = 914 obj = -275.242771, rho = -0.081361 nSV = 404, nBSV = 280 Total nSV = 404 .* optimization finished, #iter = 1121 obj = -202.503030, rho = -0.150859 nSV = 415, nBSV = 195 Total nSV = 415 *.* optimization finished, #iter = 950 obj = -264.284686, rho = -0.150470 nSV = 401, nBSV = 272 Total nSV = 401 *.* optimization finished, #iter = 956 obj = -298.015304, rho = -0.321566 nSV = 440, nBSV = 303 Total nSV = 440 .* optimization finished, #iter = 1015 obj = -268.574397, rho = -0.193578 nSV = 500, nBSV = 279 Total nSV = 500 *.* optimization finished, #iter = 932 obj = -266.953547, rho = -0.200636 nSV = 470, nBSV = 283 Total nSV = 470 *.* optimization finished, #iter = 967 obj = -258.010604, rho = -0.262910 nSV = 488, nBSV = 263 Total nSV = 488 *.* optimization finished, #iter = 982 obj = -264.320582, rho = -0.086030 nSV = 511, nBSV = 262 Total nSV = 511 *.* optimization finished, #iter = 956 obj = -298.015306, rho = -0.321566 nSV = 440, nBSV = 303 Total nSV = 440 vectorizing corpus 1%|▏ | ETA: 0:05:05vectorizing corpus 1%|▏ | ETA: 0:04:25vectorizing corpus 1%|▏ | ETA: 0:03:32.* optimization finished, #iter = 1073 obj = -167.216612, rho = -0.003380 nSV = 435, nBSV = 131 Total nSV = 435 .* optimization finished, #iter = 1069 obj = -291.356777, rho = -0.210578 nSV = 413, nBSV = 284 Total nSV = 413 *.* optimization finished, #iter = 737 obj = -278.155082, rho = -0.160367 nSV = 393, nBSV = 287 Total nSV = 393 .* optimization finished, #iter = 1075 obj = -200.489024, rho = -0.004688 nSV = 413, nBSV = 190 Total nSV = 413 *.* optimization finished, #iter = 811 obj = -258.135371, rho = -0.197197 nSV = 392, nBSV = 260 Total nSV = 392 *.* optimization finished, #iter = 793 obj = -278.677507, rho = -0.155518 nSV = 392, nBSV = 279 Total nSV = 392 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:03vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:03 SearchModels iteration 1> population: 8, bsize: 2, queue: 10, observed: 42, best-error: 0.2113048985438727 worst-error: 0.2177203075580615 [ Info: TaskFailedException(Task (failed) @0x0000711e555d7df0) [ Info: ignoring configuration due to exception *.* optimization finished, #iter = 892 obj = -303.951532, rho = 0.034317 nSV = 472, nBSV = 317 Total nSV = 472 * optimization finished, #iter = 629 obj = -132.991648, rho = -0.001794 nSV = 515, nBSV = 450 Total nSV = 515 *.* optimization finished, #iter = 869 obj = -293.684229, rho = 0.069818 nSV = 469, nBSV = 302 Total nSV = 469 .*.* optimization finished, #iter = 1759 obj = -493.326462, rho = 0.105886 nSV = 468, nBSV = 115 Total nSV = 468 *.* optimization finished, #iter = 945 obj = -295.496537, rho = 0.152313 nSV = 470, nBSV = 304 Total nSV = 470 * optimization finished, #iter = 711 obj = -136.988156, rho = 0.124194 nSV = 609, nBSV = 486 Total nSV = 609 [ Info: TaskFailedException(Task (failed) @0x0000711e555d7ee0) [ Info: ignoring configuration due to exception vectorizing corpus 1%|▏ | ETA: 0:03:10┌ Warning: using 726 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 5], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 6 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 626 obj = -137.231983, rho = 0.262696 nSV = 494, nBSV = 449 Total nSV = 494 ┌ Warning: using 726 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[TextSearch.Skipgram(3, 1)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.FreqWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 6 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 1240 obj = -330.251147, rho = 0.298821 nSV = 430, nBSV = 328 Total nSV = 430 * optimization finished, #iter = 680 obj = -132.313402, rho = -0.298134 nSV = 530, nBSV = 443 Total nSV = 530 *.* optimization finished, #iter = 898 obj = -289.420853, rho = -0.268504 nSV = 481, nBSV = 307 Total nSV = 481 .*.* optimization finished, #iter = 1771 obj = -471.528961, rho = -0.280859 nSV = 458, nBSV = 111 Total nSV = 458 *.* optimization finished, #iter = 852 obj = -291.868766, rho = -0.317346 nSV = 466, nBSV = 313 Total nSV = 466 * optimization finished, #iter = 712 obj = -137.821889, rho = -0.219672 nSV = 607, nBSV = 488 Total nSV = 607 vectorizing corpus 26%|████████▍ | ETA: 0:00:03vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04vectorizing corpus 1%|▏ | ETA: 0:03:05 vectorizing corpus 0%|▏ | ETA: 0:06:42*.* optimization finished, #iter = 868 obj = -282.928614, rho = -0.150017 nSV = 467, nBSV = 295 Total nSV = 467 .*.* optimization finished, #iter = 1650 obj = -474.593675, rho = -0.171150 nSV = 460, nBSV = 104 Total nSV = 460 *.* optimization finished, #iter = 873 obj = -284.740049, rho = -0.236036 nSV = 461, nBSV = 298 Total nSV = 461 * optimization finished, #iter = 639 obj = -134.980336, rho = -0.167220 nSV = 596, nBSV = 493 Total nSV = 596 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:03vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:03 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04.* optimization finished, #iter = 1004 obj = -299.853409, rho = -0.270737 nSV = 490, nBSV = 318 Total nSV = 490  ┌ Warning: using 726 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 5], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 6 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *. WARNING: using -h 0 may be faster * optimization finished, #iter = 731 obj = -135.544840, rho = -0.361632 nSV = 498, nBSV = 441 Total nSV = 498 ┌ Warning: using 726 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[TextSearch.Skipgram(3, 1)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.FreqWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 6 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 850 obj = -295.986946, rho = -0.213395 nSV = 464, nBSV = 310 Total nSV = 464 * optimization finished, #iter = 629 obj = -128.957126, rho = -0.204160 nSV = 524, nBSV = 432 Total nSV = 524 .* optimization finished, #iter = 1342 obj = -329.120125, rho = -0.256065 nSV = 445, nBSV = 327 Total nSV = 445 ┌ Warning: using 725 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 5], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 6 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 597 obj = -130.301861, rho = -0.272768 nSV = 476, nBSV = 426 Total nSV = 476 ┌ Warning: using 725 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[TextSearch.Skipgram(3, 1)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.FreqWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 6 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 870 obj = -320.158848, rho = -0.252132 nSV = 427, nBSV = 332 Total nSV = 427 SearchModels iteration 2> population: 8, bsize: 2, queue: 11, observed: 53, best-error: 0.20693599463142254 worst-error: 0.21651824586248303 [ Info: TaskFailedException(Task (failed) @0x0000711e565c2e00) [ Info: ignoring configuration due to exception * optimization finished, #iter = 722 obj = -135.539621, rho = 0.125098 nSV = 601, nBSV = 482 Total nSV = 601 * optimization finished, #iter = 705 obj = -137.849306, rho = 0.121670 nSV = 606, nBSV = 484 Total nSV = 606 * optimization finished, #iter = 632 obj = -131.957313, rho = 0.121743 nSV = 526, nBSV = 447 Total nSV = 526 *.* optimization finished, #iter = 840 obj = -291.245658, rho = 0.126568 nSV = 472, nBSV = 304 Total nSV = 472 * optimization finished, #iter = 645 obj = -131.809608, rho = 0.044067 nSV = 534, nBSV = 448 Total nSV = 534 .*.* optimization finished, #iter = 1777 obj = -620.218841, rho = 0.046664 nSV = 422, nBSV = 178 Total nSV = 422 [ Info: TaskFailedException(Task (failed) @0x0000711e565c2ef0) [ Info: ignoring configuration due to exception [ Info: TaskFailedException(Task (failed) @0x0000711e565c2fe0) vectorizing corpus 1%|▏ | ETA: 0:03:08[ Info: ignoring configuration due to exception ┌ Warning: using 726 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[TextSearch.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.FreqWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.5384615384615384 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 639 obj = -137.444367, rho = 0.212904 nSV = 508, nBSV = 456 Total nSV = 508 * optimization finished, #iter = 677 obj = -139.147749, rho = -0.222548 nSV = 606, nBSV = 495 Total nSV = 606 * optimization finished, #iter = 653 obj = -130.540513, rho = -0.325625 nSV = 538, nBSV = 446 Total nSV = 538 ┌ Warning: using 726 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 3], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.FreqWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 868 obj = -285.289756, rho = -0.319778 nSV = 479, nBSV = 298 Total nSV = 479 * optimization finished, #iter = 688 obj = -130.688015, rho = -0.250412 nSV = 542, nBSV = 439 Total nSV = 542 .*.* optimization finished, #iter = 1770 obj = -602.567454, rho = -0.281858 nSV = 420, nBSV = 171 Total nSV = 420 .* optimization finished, #iter = 1056 obj = -358.932729, rho = 0.316450 nSV = 449, nBSV = 369 Total nSV = 449 vectorizing corpus 1%|▏ | ETA: 0:03:33vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04 * optimization finished, #iter = 664 obj = -135.740105, rho = -0.160162 nSV = 593, nBSV = 489 Total nSV = 593 * optimization finished, #iter = 623 obj = -127.033124, rho = -0.314133 nSV = 513, nBSV = 431 Total nSV = 513 *.* optimization finished, #iter = 893 obj = -279.074454, rho = -0.236928 nSV = 475, nBSV = 289 Total nSV = 475 * optimization finished, #iter = 606 obj = -126.952499, rho = -0.244864 nSV = 521, nBSV = 434 Total nSV = 521 .*.* optimization finished, #iter = 2102 obj = -590.707570, rho = -0.240326 nSV = 424, nBSV = 162 Total nSV = 424 ┌ Warning: using 727 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 3], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.FreqWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 854 obj = -353.350453, rho = -0.526787 nSV = 445, nBSV = 376 Total nSV = 445 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04 * optimization finished, #iter = 671 obj = -136.501506, rho = -0.229773 nSV = 597, nBSV = 485 Total nSV = 597 ┌ Warning: using 726 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 3], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.FreqWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *. WARNING: using -h 0 may be faster * optimization finished, #iter = 939 obj = -339.490618, rho = -0.355559 nSV = 420, nBSV = 347 Total nSV = 420 ┌ Warning: using 728 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[TextSearch.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.FreqWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.5384615384615384 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 656 obj = -133.815309, rho = -0.182158 nSV = 586, nBSV = 479 Total nSV = 586 * optimization finished, #iter = 634 obj = -138.133548, rho = -0.411717 nSV = 517, nBSV = 458 Total nSV = 517 ┌ Warning: using 726 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[TextSearch.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.FreqWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.5384615384615384 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 603 obj = -130.062021, rho = -0.240204 nSV = 490, nBSV = 430 Total nSV = 490 SearchModels iteration 3> population: 8, bsize: 2, queue: 9, observed: 62, best-error: 0.20693599463142254 worst-error: 0.21430609196172357 [ Info: TaskFailedException(Task (failed) @0x0000711e4fcdac20) [ Info: ignoring configuration due to exception * optimization finished, #iter = 685 obj = -60.697936, rho = 0.487378 nSV = 658, nBSV = 614 Total nSV = 658 * optimization finished, #iter = 709 obj = -137.096213, rho = 0.111068 nSV = 594, nBSV = 487 Total nSV = 594 * optimization finished, #iter = 717 obj = -136.307153, rho = 0.116863 nSV = 600, nBSV = 479 Total nSV = 600 ┌ Warning: using 578 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[], TextSearch.Skipgram[TextSearch.Skipgram(2, 2), TextSearch.Skipgram(3, 1)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 520 obj = -48.609370, rho = 0.574155 nSV = 503, nBSV = 481 Total nSV = 503 ┌ Warning: using 726 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 705 obj = -133.776173, rho = 0.333533 nSV = 525, nBSV = 452 Total nSV = 525 ┌ Warning: using 727 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 2], TextSearch.Skipgram[TextSearch.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5384615384615384 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 1046 obj = -307.579387, rho = 0.155211 nSV = 480, nBSV = 313 Total nSV = 480 ┌ Warning: using 727 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 4], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5384615384615384 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 686 obj = -135.181818, rho = 0.196339 nSV = 554, nBSV = 463 Total nSV = 554 ┌ Warning: using 604 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[], TextSearch.Skipgram[TextSearch.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5384615384615384 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 688 obj = -60.620645, rho = -0.548819 nSV = 662, nBSV = 606 Total nSV = 662 * optimization finished, #iter = 680 obj = -137.871790, rho = -0.230166 nSV = 599, nBSV = 493 Total nSV = 599 * optimization finished, #iter = 700 obj = -136.879762, rho = -0.216364 nSV = 599, nBSV = 483 Total nSV = 599 * optimization finished, #iter = 543 obj = -127.469791, rho = 0.375389 nSV = 485, nBSV = 425 Total nSV = 485 ┌ Warning: using 568 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[], TextSearch.Skipgram[TextSearch.Skipgram(2, 2), TextSearch.Skipgram(3, 1)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 517 obj = -48.013929, rho = -0.671513 nSV = 489, nBSV = 463 Total nSV = 489 vectorizing corpus 0%|▏ | ETA: 0:09:09.* optimization finished, #iter = 1025 obj = -301.191235, rho = -0.328821 nSV = 489, nBSV = 306 Total nSV = 489 * optimization finished, #iter = 682 obj = -137.855072, rho = -0.272828 nSV = 559, nBSV = 471 Total nSV = 559 * optimization finished, #iter = 657 obj = -59.577384, rho = -0.524816 nSV = 652, nBSV = 603 Total nSV = 652 * optimization finished, #iter = 645 obj = -135.383060, rho = -0.183931 nSV = 590, nBSV = 487 Total nSV = 590 * optimization finished, #iter = 640 obj = -134.531662, rho = -0.172209 nSV = 588, nBSV = 485 Total nSV = 588 ┌ Warning: using 617 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[], TextSearch.Skipgram[TextSearch.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5384615384615384 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 552 obj = -137.072665, rho = -0.471117 nSV = 500, nBSV = 441 Total nSV = 500 ┌ Warning: using 590 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[], TextSearch.Skipgram[TextSearch.Skipgram(2, 2), TextSearch.Skipgram(3, 1)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 514 obj = -48.619433, rho = -0.621408 nSV = 501, nBSV = 474 Total nSV = 501 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04┌ Warning: using 727 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 2], TextSearch.Skipgram[TextSearch.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5384615384615384 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 902 obj = -293.523824, rho = -0.291366 nSV = 462, nBSV = 295 Total nSV = 462 ┌ Warning: using 727 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 4], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5384615384615384 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 684 obj = -134.388962, rho = -0.190107 nSV = 561, nBSV = 457 Total nSV = 561 ┌ Warning: using 635 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[], TextSearch.Skipgram[TextSearch.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5384615384615384 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 590 obj = -133.337661, rho = -0.355783 nSV = 509, nBSV = 443 Total nSV = 509 ┌ Warning: using 727 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 666 obj = -136.064420, rho = -0.290469 nSV = 542, nBSV = 464 Total nSV = 542 ┌ Warning: using 725 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 685 obj = -132.511626, rho = -0.265957 nSV = 530, nBSV = 450 Total nSV = 530 SearchModels iteration 4> population: 8, bsize: 2, queue: 9, observed: 71, best-error: 0.20319603027767796 worst-error: 0.2128983162957535 [ Info: TaskFailedException(Task (failed) @0x0000711e4fcd9870) [ Info: ignoring configuration due to exception * optimization finished, #iter = 692 obj = -137.795531, rho = 0.048821 nSV = 598, nBSV = 482 Total nSV = 598 * optimization finished, #iter = 688 obj = -60.643198, rho = 0.505362 nSV = 659, nBSV = 614 Total nSV = 659 * optimization finished, #iter = 725 obj = -135.044811, rho = 0.115540 nSV = 596, nBSV = 477 Total nSV = 596 * optimization finished, #iter = 670 obj = -56.221565, rho = 0.136834 nSV = 632, nBSV = 593 Total nSV = 632 .* optimization finished, #iter = 1023 obj = -257.401146, rho = 0.101954 nSV = 548, nBSV = 249 Total nSV = 548 ┌ Warning: using 727 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 3], TextSearch.Skipgram[TextSearch.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 686 obj = -60.431825, rho = 0.467821 nSV = 656, nBSV = 622 Total nSV = 656 ┌ Warning: using 726 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 5], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TfWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 vectorizing corpus 1%|▏ | ETA: 0:03:31* optimization finished, #iter = 659 obj = -56.882018, rho = 0.291604 nSV = 636, nBSV = 608 Total nSV = 636 ┌ Warning: using 727 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 5], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 646 obj = -55.303554, rho = 0.122019 nSV = 597, nBSV = 576 Total nSV = 597 * optimization finished, #iter = 680 obj = -60.266280, rho = -0.555972 nSV = 660, nBSV = 605 Total nSV = 660 * optimization finished, #iter = 689 obj = -135.893272, rho = -0.220836 nSV = 595, nBSV = 478 Total nSV = 595 * optimization finished, #iter = 647 obj = -56.053673, rho = -0.285331 nSV = 624, nBSV = 598 Total nSV = 624 .* optimization finished, #iter = 999 obj = -253.262654, rho = -0.240801 nSV = 541, nBSV = 242 Total nSV = 541 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 87%|████████████████████████████▌ | ETA: 0:00:00vectorizing corpus 0%|▏ | ETA: 0:06:05┌ Warning: using 727 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 5], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TfWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 655 obj = -58.038541, rho = -0.360367 nSV = 635, nBSV = 615 Total nSV = 635 ┌ Warning: using 728 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 5], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 630 obj = -55.122212, rho = -0.280255 nSV = 594, nBSV = 575 Total nSV = 594 * optimization finished, #iter = 638 obj = -133.438907, rho = -0.178150 nSV = 582, nBSV = 486 Total nSV = 582 * optimization finished, #iter = 624 obj = -54.259784, rho = -0.273118 nSV = 604, nBSV = 575 Total nSV = 604 *.* optimization finished, #iter = 934 obj = -252.457764, rho = -0.147626 nSV = 538, nBSV = 248 Total nSV = 538 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:03vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:03┌ Warning: using 725 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 5], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TfWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 637 obj = -56.625074, rho = -0.288230 nSV = 625, nBSV = 602 Total nSV = 625 ┌ Warning: using 726 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 5], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 678 obj = -138.968639, rho = -0.199377 nSV = 595, nBSV = 490 Total nSV = 595 * optimization finished, #iter = 624 obj = -52.880291, rho = -0.144589 nSV = 579, nBSV = 547 Total nSV = 579 * optimization finished, #iter = 676 obj = -61.035178, rho = -0.548752 nSV = 657, nBSV = 614 Total nSV = 657 * optimization finished, #iter = 645 obj = -137.460777, rho = -0.118844 nSV = 587, nBSV = 492 Total nSV = 587 * optimization finished, #iter = 651 obj = -59.316432, rho = -0.548573 nSV = 650, nBSV = 598 Total nSV = 650 ┌ Warning: using 727 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 3], TextSearch.Skipgram[TextSearch.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 654 obj = -59.541855, rho = -0.507940 nSV = 646, nBSV = 597 Total nSV = 646 SearchModels iteration 5> population: 8, bsize: 2, queue: 8, observed: 79, best-error: 0.20319603027767796 worst-error: 0.21283534947792704 [ Info: TaskFailedException(Task (failed) @0x0000711e4fcd83d0) [ Info: ignoring configuration due to exception * optimization finished, #iter = 675 obj = -56.465112, rho = 0.148811 nSV = 634, nBSV = 595 Total nSV = 634 * optimization finished, #iter = 685 obj = -60.076964, rho = 0.432050 nSV = 653, nBSV = 613 Total nSV = 653 *.* optimization finished, #iter = 730 obj = -138.617904, rho = 0.120649 nSV = 612, nBSV = 486 Total nSV = 612 ┌ Warning: using 727 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[TextSearch.Skipgram(3, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 675 obj = -57.950539, rho = 0.295887 nSV = 647, nBSV = 618 Total nSV = 647 ┌ Warning: using 727 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 671 obj = -57.998776, rho = 0.375850 nSV = 644, nBSV = 618 Total nSV = 644 ┌ Warning: using 726 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 3], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 656 obj = -135.507165, rho = 0.214912 nSV = 536, nBSV = 462 Total nSV = 536 ┌ Warning: using 727 of 728 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[TextSearch.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5384615384615384 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 vectorizing corpus 1%|▏ | ETA: 0:04:19* optimization finished, #iter = 681 obj = -60.030446, rho = 0.462602 nSV = 653, nBSV = 619 Total nSV = 653 * optimization finished, #iter = 647 obj = -56.398478, rho = -0.285853 nSV = 630, nBSV = 599 Total nSV = 630 * optimization finished, #iter = 664 obj = -60.248002, rho = -0.521315 nSV = 651, nBSV = 611 Total nSV = 651 * optimization finished, #iter = 687 obj = -137.135619, rho = -0.238585 nSV = 607, nBSV = 494 Total nSV = 607 ┌ Warning: using 728 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 658 obj = -58.779906, rho = -0.501054 nSV = 641, nBSV = 614 Total nSV = 641 ┌ Warning: using 728 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 3], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04* optimization finished, #iter = 693 obj = -137.591611, rho = -0.273359 nSV = 556, nBSV = 464 Total nSV = 556 * optimization finished, #iter = 678 obj = -60.680104, rho = -0.561228 nSV = 654, nBSV = 611 Total nSV = 654 * optimization finished, #iter = 627 obj = -54.580585, rho = -0.267708 nSV = 611, nBSV = 576 Total nSV = 611 * optimization finished, #iter = 643 obj = -59.240831, rho = -0.505464 nSV = 640, nBSV = 601 Total nSV = 640 * optimization finished, #iter = 666 obj = -135.552347, rho = -0.219367 nSV = 594, nBSV = 482 Total nSV = 594 vectorizing corpus 57%|██████████████████▊ | ETA: 0:00:01┌ Warning: using 726 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1, 3], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 719 obj = -132.830103, rho = -0.188201 nSV = 543, nBSV = 450 Total nSV = 543 ┌ Warning: using 727 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[TextSearch.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5384615384615384 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 651 obj = -59.181146, rho = -0.509954 nSV = 643, nBSV = 605 Total nSV = 643 ┌ Warning: using 728 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[TextSearch.Skipgram(3, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 663 obj = -58.753745, rho = -0.426935 nSV = 642, nBSV = 618 Total nSV = 642 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:01 ┌ Warning: using 726 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[TextSearch.Skipgram(3, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 638 obj = -57.421319, rho = -0.363607 nSV = 630, nBSV = 608 Total nSV = 630 ┌ Warning: using 726 of 729 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 640 obj = -57.502676, rho = -0.450205 nSV = 632, nBSV = 605 Total nSV = 632 SearchModels> stop by convergence error={MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[4], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TfWeighting, TextSearch.IdfWeighting} global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() min_token_ndocs: Int64 3 max_token_pdocs: Float64 0.7 LIBSVMConfig } => 0.21283534947792704, iter=6 (of 16) * optimization finished, #iter = 980 obj = -192.998997, rho = -0.172559 nSV = 838, nBSV = 673 Total nSV = 838 ┌ Info: ("-- microtc_kfolds - perf best_lists[1]:", 1, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 └ LIBSVMConfig }, 0.20319603027767796) ┌ Info: ("-- microtc_kfolds - perf best_lists[2]:", 2, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1, 3], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.5 └ LIBSVMConfig }, 0.2066196847481696) ┌ Info: ("-- microtc_kfolds - perf best_lists[3]:", 3, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1], TextSearch.Skipgram[TextSearch.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5384615384615384 └ LIBSVMConfig }, 0.20693599463142254) ┌ Info: ("-- microtc_kfolds - perf best_lists[4]:", 4, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1], TextSearch.Skipgram[TextSearch.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.5384615384615384 └ LIBSVMConfig }, 0.20863028816202667) ┌ Info: ("-- microtc_kfolds - perf best_lists[5]:", 5, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.FreqWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.5 └ LIBSVMConfig }, 0.2113048985438727) ┌ Info: ("-- microtc_kfolds - perf best_lists[6]:", 6, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1, 5], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.FreqWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ min_token_ndocs: Int64 6 │ max_token_pdocs: Float64 0.5 └ LIBSVMConfig }, 0.21134497525615537) ┌ Info: ("-- microtc_kfolds - perf best_lists[7]:", 7, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.BinaryLocalWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 └ LIBSVMConfig }, 0.21143954979642554) ┌ Info: ("-- microtc_kfolds - perf best_lists[8]:", 8, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[4], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TfWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 └ LIBSVMConfig }, 0.21283534947792704) ┌ Info: *** Performance microtc_kfolds on test: └ sc = (microf1 = 0.767590618336887, precision = 0.767590618336887, macroprecision = 0.7859823103725543, recall = 0.767590618336887, macrorecall = 0.7720055355256928, macrof1 = 0.7655268891182204, accuracy = 0.767590618336887, classf1 = Dict{Any, Any}("♡" => 0.743529411764706, "💔" => 0.7875243664717348), classprecision = Dict{Any, Any}("♡" => 0.8681318681318682, "💔" => 0.7038327526132404), classrecall = Dict{Any, Any}("♡" => 0.6502057613168725, "💔" => 0.8938053097345132)) Test Summary: | Pass Total Time microtc | 2 2 3m48.2s Testing TextClassification tests passed Testing completed after 261.52s PkgEval succeeded after 427.74s