Package evaluation to test TextClassification on Julia 1.13.0-DEV.1319 (9cddfda8ef*) started at 2025-10-16T21:31:20.450 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 8.9s ################################################################################ # Installation # Installing TextClassification... Resolving package versions... Updating `~/.julia/environments/v1.13/Project.toml` [8e067cb0] + TextClassification v0.6.1 Updating `~/.julia/environments/v1.13/Manifest.toml` [7d9f7c33] + Accessors v0.1.42 [79e6a3ab] + Adapt v4.4.0 [dce04be8] + ArgCheck v2.5.0 [4fba245c] + ArrayInterface v7.21.0 [a9b6321e] + Atomix v1.1.2 [198e06fe] + BangBang v0.4.4 [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 v0.4.54 [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 v1.3.0 [0d7ed370] + StaticArrayInterface v1.8.0 [90137ffa] + StaticArrays v1.9.15 [1e83bf80] + StaticArraysCore v1.4.3 [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 v0.6.4 [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 v0.7.0 [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 v10.46.0+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 5.93s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 67.43s ################################################################################ # Testing # Testing TextClassification Status `/tmp/jl_Hck6jV/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 v1.1.0 [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_Hck6jV/Manifest.toml` [7d9f7c33] Accessors v0.1.42 [79e6a3ab] Adapt v4.4.0 [4c88cf16] Aqua v0.8.14 [dce04be8] ArgCheck v2.5.0 [4fba245c] ArrayInterface v7.21.0 [a9b6321e] Atomix v1.1.2 [198e06fe] BangBang v0.4.4 [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 v0.4.54 [692b3bcd] JLLWrappers v1.7.1 [682c06a0] JSON v1.1.0 [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 v1.3.0 [0d7ed370] StaticArrayInterface v1.8.0 [90137ffa] StaticArrays v1.9.15 [1e83bf80] StaticArraysCore v1.4.3 [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 v0.6.4 [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 v0.7.0 [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 v10.46.0+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 = "_usr Yo no quería tomar _emo" t = "Ojala alguien llegara y me dijera \"Ten un meet con mario Yose que mario te hace muy feliz y mereces verlo\" _emo" t = "Cada día. . Con más fuerza Te amo _emo @ Playa Nuevo Vallarta _url" t = "_usr _usr _usr _usr _usr _usr _num,_num votos para mis héroes _usr .:*･ _emo VOTACIÓN MASIVA #CD_num #AlonsoVillalpandoTrendy #Coders #KCAMexico" t = "En la tarde me queje de que el chocolala esta en _num pesos y ahora Facebook me sugiere la página de lala... Hasta parece burla _emo _emo _emo GM" t = "mi mente sigue allí _emo _emo en Grand Sirenis Riviera… _url" t = "Y aunque te amo con locura, ya no vuelvas _emo _url" t = "BEBASO _emo _url" t = "Tu saltas, yo salto. No importa si es de paracaídas o bungee. Quiero ganar brouu. #YoMeRifoConJuanpa _emo _num" t = "No tengo con quien hablar porque mis amigas si fueron a la secu hoy y yo no _emo _emo _emo" countmap(trainlabels) = Dict{Any, Int64}("♡" => 542, "💔" => 551) countmap(testlabels) = Dict{Any, Int64}("♡" => 237, "💔" => 232) SearchModels> search params iter=0, initialpopulation=32, maxpopulation=8, bsize=2, mutbsize=8, crossbsize=8 *.* optimization finished, #iter = 892 obj = -298.935414, rho = 0.174049 nSV = 410, nBSV = 299 Total nSV = 410 .* optimization finished, #iter = 1070 obj = -294.651648, rho = 0.271195 nSV = 483, nBSV = 299 Total nSV = 483 *.* optimization finished, #iter = 993 obj = -272.487491, rho = 0.187536 nSV = 405, nBSV = 268 Total nSV = 405 *.* optimization finished, #iter = 942 obj = -236.949053, rho = 0.234653 nSV = 425, nBSV = 232 Total nSV = 425 *.* optimization finished, #iter = 868 obj = -322.668096, rho = 0.321557 nSV = 482, nBSV = 339 Total nSV = 482 *.* optimization finished, #iter = 876 obj = -238.648072, rho = 0.136115 nSV = 412, nBSV = 252 Total nSV = 412 .* optimization finished, #iter = 1102 obj = -279.612455, rho = 0.219536 nSV = 575, nBSV = 270 Total nSV = 575 *.* optimization finished, #iter = 1058 obj = -201.887439, rho = 0.175687 nSV = 431, nBSV = 186 Total nSV = 431 *.* optimization finished, #iter = 895 obj = -273.497288, rho = 0.181844 nSV = 408, nBSV = 275 Total nSV = 408 *.* optimization finished, #iter = 851 obj = -280.077610, rho = -0.008089 nSV = 410, nBSV = 294 Total nSV = 410 .* optimization finished, #iter = 1055 obj = -305.085063, rho = 0.212290 nSV = 503, nBSV = 313 Total nSV = 503 .* optimization finished, #iter = 1011 obj = -291.121496, rho = 0.213529 nSV = 527, nBSV = 293 Total nSV = 527 *.* optimization finished, #iter = 804 obj = -285.108995, rho = 0.171791 nSV = 415, nBSV = 297 Total nSV = 415 *.* optimization finished, #iter = 949 obj = -272.455482, rho = 0.130102 nSV = 418, nBSV = 283 Total nSV = 418 .* optimization finished, #iter = 1166 obj = -178.322160, rho = 0.160746 nSV = 461, nBSV = 135 Total nSV = 461 *.* optimization finished, #iter = 913 obj = -316.877836, rho = 0.258823 nSV = 494, nBSV = 330 Total nSV = 494 *.* optimization finished, #iter = 997 obj = -266.518386, rho = 0.058243 nSV = 412, nBSV = 275 Total nSV = 412 *.* optimization finished, #iter = 960 obj = -303.631752, rho = 0.266331 nSV = 525, nBSV = 310 Total nSV = 525 *.* optimization finished, #iter = 910 obj = -223.003638, rho = 0.195154 nSV = 428, nBSV = 234 Total nSV = 428 .* optimization finished, #iter = 1188 obj = -204.110702, rho = 0.274223 nSV = 463, nBSV = 186 Total nSV = 463 .* optimization finished, #iter = 1139 obj = -199.560910, rho = 0.240666 nSV = 450, nBSV = 182 Total nSV = 450 *.* optimization finished, #iter = 927 obj = -314.355477, rho = 0.286141 nSV = 497, nBSV = 323 Total nSV = 497 .* optimization finished, #iter = 1209 obj = -180.245892, rho = 0.166523 nSV = 464, nBSV = 141 Total nSV = 464 *.* optimization finished, #iter = 1044 obj = -203.353305, rho = 0.142308 nSV = 459, nBSV = 190 Total nSV = 459 *.* optimization finished, #iter = 848 obj = -283.012246, rho = 0.047063 nSV = 410, nBSV = 294 Total nSV = 410 *.* optimization finished, #iter = 852 obj = -287.722308, rho = 0.050439 nSV = 413, nBSV = 297 Total nSV = 413 *.* optimization finished, #iter = 1060 obj = -246.443790, rho = 0.190386 nSV = 403, nBSV = 245 Total nSV = 403 *.* optimization finished, #iter = 831 obj = -247.446122, rho = 0.136150 nSV = 404, nBSV = 267 Total nSV = 404 .* optimization finished, #iter = 1022 obj = -285.378340, rho = 0.152429 nSV = 546, nBSV = 283 Total nSV = 546 .* optimization finished, #iter = 1072 obj = -302.478814, rho = 0.280619 nSV = 490, nBSV = 311 Total nSV = 490 .* optimization finished, #iter = 980 obj = -321.282579, rho = 0.281764 nSV = 476, nBSV = 330 Total nSV = 476 .* optimization finished, #iter = 1154 obj = -291.603426, rho = 0.245754 nSV = 507, nBSV = 288 Total nSV = 507 SearchModels iteration 1> population: 8, bsize: 2, queue: 8, observed: 40, best-error: 0.208779761904762 worst-error: 0.23035714285714293 * optimization finished, #iter = 742 obj = -144.013543, rho = 0.193111 nSV = 629, nBSV = 500 Total nSV = 629 * optimization finished, #iter = 694 obj = -118.070465, rho = 0.250956 nSV = 472, nBSV = 387 Total nSV = 472 .*.* optimization finished, #iter = 1826 obj = -601.993459, rho = 0.329557 nSV = 456, nBSV = 169 Total nSV = 456 * optimization finished, #iter = 655 obj = -119.673238, rho = 0.164168 nSV = 485, nBSV = 397 Total nSV = 485 * optimization finished, #iter = 693 obj = -119.668369, rho = 0.206722 nSV = 476, nBSV = 386 Total nSV = 476 *.* optimization finished, #iter = 989 obj = -279.510745, rho = 0.162951 nSV = 409, nBSV = 289 Total nSV = 409 ┌ Warning: using 0 of 765 examples after vectorization using {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.TfWeighting} │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ smooth: Float64 1.0 │ min_token_ndocs: Int64 11 │ max_token_pdocs: Float64 0.0 │ weights: Symbol balance │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 0 obj = 0.000000, rho = nan nSV = 0, nBSV = 0 [ Info: ignoring configuration due to exception MethodError: no method matching LIBSVM.SVM(::Type{LIBSVM.OneClassSVM}, ::LIBSVM.Kernel.KERNEL, ::Dict{Any, Float64}, ::Int64, ::Int64, ::Int32, ::Vector{Bool}, ::Vector{Int32}, ::Vector{Float64}, ::Vector{Int32}, ::LIBSVM.SupportVectors{Vector{Float64}, SparseArrays.SparseMatrixCSC{Float32, UInt32}}, ::Float64, ::Matrix{Float64}, ::Vector{Float64}, ::Vector{Float64}, ::Vector{Float64}, ::Int32, ::Float64, ::Float64, ::Float64, ::Float64, ::Float64, ::Float64, ::Bool, ::Bool) The type `LIBSVM.SVM` exists, but no method is defined for this combination of argument types when trying to construct it. Closest candidates are: LIBSVM.SVM(::Type, ::K, ::Union{Nothing, Dict{T, Float64}}, ::Int64, ::Int64, ::Int32, !Matched::Vector{T}, ::Vector{Int32}, ::Vector{Float64}, ::Vector{Int32}, ::LIBSVM.SupportVectors, ::Float64, ::Matrix{Float64}, ::Vector{Float64}, ::Vector{Float64}, ::Vector{Float64}, ::Int32, ::Float64, ::Float64, ::Float64, ::Float64, ::Float64, ::Float64, ::Bool, ::Bool) where {T, K} @ LIBSVM ~/.julia/packages/LIBSVM/w9bLS/src/LIBSVM.jl:44 LIBSVM.SVM(!Matched::LIBSVM.SVMModel, ::Any, ::Any, ::Any, ::Any, ::Any, ::Any) @ LIBSVM ~/.julia/packages/LIBSVM/w9bLS/src/LIBSVM.jl:72 Stacktrace: [1] LIBSVM.SVM(smc::LIBSVM.SVMModel, y::Vector{Any}, X::SparseArrays.SparseMatrixCSC{Float32, UInt32}, weights::Dict{Any, Float64}, labels::Vector{Bool}, svmtype::Type, kernel::LIBSVM.Kernel.KERNEL) @ LIBSVM ~/.julia/packages/LIBSVM/w9bLS/src/LIBSVM.jl:110 [2] svmtrain(X::SparseArrays.SparseMatrixCSC{Float32, UInt32}, y::Vector{Any}; svmtype::Type, kernel::LIBSVM.Kernel.KERNEL, degree::Int64, gamma::Float64, coef0::Float64, cost::Float64, nu::Float64, epsilon::Float64, tolerance::Float64, shrinking::Bool, probability::Bool, weights::Dict{Any, Float64}, cachesize::Float64, verbose::Bool, nt::Int64) @ LIBSVM ~/.julia/packages/LIBSVM/w9bLS/src/LIBSVM.jl:375 [3] svmtrain @ ~/.julia/packages/LIBSVM/w9bLS/src/LIBSVM.jl:307 [inlined] [4] create(config::LIBSVMConfig, train_X::Vector{Dict{UInt32, Float32}}, train_y::Vector{Any}, dim::Int64) @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/libsvmconfig.jl:30 [5] #MicroTC#64 @ ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:78 [inlined] [6] MicroTC @ ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:71 [inlined] [7] MicroTC(config::MicroTC_Config{TextClassification.EntModelConfig{TextSearch.TfWeighting}, LIBSVMConfig}, train_corpus::SubArray{Any, 1, Vector{Any}, Tuple{Vector{Int64}}, false}, train_y::SubArray{Any, 1, Vector{Any}, Tuple{Vector{Int64}}, false}; textconfig::TextSearch.TextConfig, verbose::Bool, minbatch::Int64) @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:65 [8] MicroTC @ ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:45 [inlined] [9] (::TextClassification.var"#82#83"{TextClassification.var"#microtc##0#microtc##1", SubArray{Any, 1, Vector{Any}, Tuple{Vector{Int64}}, false}, SubArray{Any, 1, Vector{Any}, Tuple{Vector{Int64}}, false}, SubArray{Any, 1, Vector{Any}, Tuple{Vector{Int64}}, false}, SubArray{Any, 1, Vector{Any}, Tuple{Vector{Int64}}, false}})(config::MicroTC_Config{TextClassification.EntModelConfig{TextSearch.TfWeighting}, LIBSVMConfig}) @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/utils.jl:74 [10] evaluate_queue(errfun::TextClassification.var"#82#83"{TextClassification.var"#microtc##0#microtc##1", SubArray{Any, 1, Vector{Any}, Tuple{Vector{Int64}}, false}, SubArray{Any, 1, Vector{Any}, Tuple{Vector{Int64}}, false}, SubArray{Any, 1, Vector{Any}, Tuple{Vector{Int64}}, false}, SubArray{Any, 1, Vector{Any}, Tuple{Vector{Int64}}, false}}, evalqueue::Vector{MicroTC_Config}, population::Vector{Pair}, parallel::Symbol, tmp::Vector{Pair}) @ SearchModels ~/.julia/packages/SearchModels/TXFxK/src/search.jl:97 [11] search_models(errfun::Function, space::MicroTC_ConfigSpace, initialpopulation::Int64, params::SearchParams; accept_config::Function, inspect_population::SearchModels.var"#search_models##8#search_models##9", sort_by_best::typeof(SearchModels.sort_by_best), convergence::SearchModels.var"#search_models##10#search_models##11", parallel::Symbol) @ SearchModels ~/.julia/packages/SearchModels/TXFxK/src/search.jl:248 [12] search_models @ ~/.julia/packages/SearchModels/TXFxK/src/search.jl:214 [inlined] [13] microtc(corpus::SubArray{Any, 1, Vector{Any}, Tuple{Vector{Int64}}, false}, labels::SubArray{Any, 1, Vector{Any}, Tuple{Vector{Int64}}, false}; slist::Vector{Any}, nlist::Vector{Vector{Any}}, qlist::Vector{Vector{Int64}}, space::MicroTC_ConfigSpace, initialpopulation::Int64, score::TextClassification.var"#microtc##0#microtc##1", at::Float64, maxpopulation::Int64, bsize::Int64, mutbsize::Int64, crossbsize::Int64, maxiters::Int64, verbose::Bool, params::SearchParams, sample::Float64, parallel::Symbol) @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/utils.jl:73 [14] microtc(corpus::SubArray{Any, 1, Vector{Any}, Tuple{Vector{Int64}}, false}, labels::SubArray{Any, 1, Vector{Any}, Tuple{Vector{Int64}}, false}) @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/utils.jl:43 [15] top-level scope @ ~/.julia/packages/TextClassification/q1c16/test/runtests.jl:7 [16] macro expansion @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1961 [inlined] [17] macro expansion @ ~/.julia/packages/TextClassification/q1c16/test/runtests.jl:32 [inlined] [18] include(mapexpr::Function, mod::Module, _path::String) @ Base ./Base.jl:310 [19] top-level scope @ none:6 [20] eval(m::Module, e::Any) @ Core ./boot.jl:489 [21] exec_options(opts::Base.JLOptions) @ Base ./client.jl:310 [22] _start() @ Base ./client.jl:577[ Info: ignoring configuration due to exception empty vocabulary(VectorModelConfig = 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 0.0 , TextConfig = TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()))SearchModels iteration 2> population: 8, bsize: 2, queue: 8, observed: 48, best-error: 0.208779761904762 worst-error: 0.2266369047619048 .*.* optimization finished, #iter = 2087 obj = -604.611174, rho = -0.075082 nSV = 377, nBSV = 170 Total nSV = 377 ┌ Warning: using 762 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.EntModelConfig{TextSearch.TfWeighting} │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ smooth: Float64 0.7692307692307692 │ min_token_ndocs: Int64 11 │ max_token_pdocs: Float64 0.6 │ weights: Symbol balance │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 761 obj = -137.690780, rho = 0.293136 nSV = 483, nBSV = 438 Total nSV = 483 ┌ Warning: using 761 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.EntModelConfig{TextSearch.TfWeighting} │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ smooth: Float64 1.0 │ min_token_ndocs: Int64 15 │ max_token_pdocs: Float64 0.5 │ weights: Symbol balance │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 1046 obj = -389.041470, rho = 0.423040 nSV = 439, nBSV = 384 Total nSV = 439 *.* optimization finished, #iter = 794 obj = -281.927055, rho = 0.045761 nSV = 406, nBSV = 291 Total nSV = 406 *.* optimization finished, #iter = 765 obj = -146.521741, rho = 0.191727 nSV = 643, nBSV = 509 Total nSV = 643 * optimization finished, #iter = 712 obj = -145.207403, rho = 0.212857 nSV = 613, nBSV = 508 Total nSV = 613 ┌ Warning: using 762 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.Skipgram(2, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.TfWeighting} │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ smooth: Float64 1.0 │ min_token_ndocs: Int64 11 │ max_token_pdocs: Float64 0.5 │ weights: Symbol balance │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 782 obj = -136.876821, rho = 0.219232 nSV = 492, nBSV = 433 Total nSV = 492 * optimization finished, #iter = 684 obj = -123.371845, rho = 0.115710 nSV = 484, nBSV = 403 Total nSV = 484 SearchModels iteration 3> population: 8, bsize: 2, queue: 8, observed: 56, best-error: 0.208779761904762 worst-error: 0.22425595238095242 ┌ Warning: using 762 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.Skipgram(3, 2)], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.TpWeighting} │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ smooth: Float64 1.0 │ min_token_ndocs: Int64 9 │ max_token_pdocs: Float64 0.5 │ weights: Symbol balance │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *. WARNING: using -h 0 may be faster * optimization finished, #iter = 901 obj = -366.451382, rho = 0.287633 nSV = 428, nBSV = 361 Total nSV = 428 ┌ Warning: using 387 of 765 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, 1)], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.TfWeighting} │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ smooth: Float64 1.0 │ min_token_ndocs: Int64 15 │ max_token_pdocs: Float64 0.6 │ weights: Symbol balance │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 282 obj = -226.253048, rho = 0.161091 nSV = 247, nBSV = 221 Total nSV = 247 .*.* optimization finished, #iter = 1940 obj = -593.278436, rho = 0.032720 nSV = 372, nBSV = 176 Total nSV = 372 * optimization finished, #iter = 712 obj = -143.224923, rho = 0.219616 nSV = 606, nBSV = 499 Total nSV = 606 *.* optimization finished, #iter = 797 obj = -281.925945, rho = 0.045801 nSV = 407, nBSV = 291 Total nSV = 407 .*.* optimization finished, #iter = 1993 obj = -435.407464, rho = 0.212916 nSV = 480, nBSV = 81 Total nSV = 480 .*.* optimization finished, #iter = 1983 obj = -437.487725, rho = 0.186444 nSV = 496, nBSV = 75 Total nSV = 496 ┌ Warning: using 762 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.EntModelConfig{TextSearch.TfWeighting} │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ smooth: Float64 1.0 │ min_token_ndocs: Int64 11 │ max_token_pdocs: Float64 0.5 │ weights: Symbol balance │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 763 obj = -137.810672, rho = 0.297427 nSV = 483, nBSV = 433 Total nSV = 483 SearchModels iteration 4> population: 8, bsize: 2, queue: 5, observed: 61, best-error: 0.208779761904762 worst-error: 0.22276785714285707 *.* optimization finished, #iter = 898 obj = -317.448144, rho = 0.271114 nSV = 494, nBSV = 332 Total nSV = 494 ┌ Warning: using 762 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.EntModelConfig{TextSearch.TfWeighting} │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ smooth: Float64 1.0 │ min_token_ndocs: Int64 9 │ max_token_pdocs: Float64 0.5 │ weights: Symbol balance │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 1439 obj = -1020.361265, rho = 0.348917 nSV = 403, nBSV = 323 Total nSV = 403 .*.* optimization finished, #iter = 1913 obj = -571.209198, rho = -0.080446 nSV = 369, nBSV = 158 Total nSV = 369 * optimization finished, #iter = 716 obj = -143.569010, rho = 0.227699 nSV = 610, nBSV = 500 Total nSV = 610 ┌ 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], 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 0.65 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 739 obj = -144.993650, rho = 0.322936 nSV = 579, nBSV = 485 Total nSV = 579 SearchModels> stop by convergence error={MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3, 9], 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 0.5 LIBSVMConfig } => 0.22276785714285707, iter=5 (of 16) .* optimization finished, #iter = 1483 obj = -399.208096, rho = -0.209332 nSV = 743, nBSV = 391 Total nSV = 743 ┌ Info: ("-- 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.TpWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.5 └ LIBSVMConfig }, 0.208779761904762) ┌ Info: ("-- perf best_lists[2]:", 2, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.TfWeighting} │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ smooth: Float64 1.0 │ min_token_ndocs: Int64 11 │ max_token_pdocs: Float64 0.5 │ weights: Symbol balance └ LIBSVMConfig }, 0.21160714285714288) ┌ Info: ("-- 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.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.TfWeighting} │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ smooth: Float64 0.7692307692307692 │ min_token_ndocs: Int64 11 │ max_token_pdocs: Float64 0.5 │ weights: Symbol balance └ LIBSVMConfig }, 0.21175595238095246) ┌ Info: ("-- 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.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.FreqWeighting} │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ smooth: Float64 0.7692307692307692 │ min_token_ndocs: Int64 11 │ max_token_pdocs: Float64 0.6 │ weights: Symbol balance └ LIBSVMConfig }, 0.21175595238095246) ┌ Info: ("-- 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.Skipgram(2, 1)], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.TfWeighting} │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ smooth: Float64 1.0 │ min_token_ndocs: Int64 11 │ max_token_pdocs: Float64 0.5 │ weights: Symbol balance └ LIBSVMConfig }, 0.21339285714285716) ┌ Info: ("-- perf best_lists[6]:", 6, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3], Int8[1], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.EntModelConfig{TextSearch.TpWeighting} │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ smooth: Float64 2.0 │ min_token_ndocs: Int64 11 │ max_token_pdocs: Float64 0.7 │ weights: Symbol balance └ LIBSVMConfig }, 0.21458333333333335) ┌ Info: ("-- 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.EntModelConfig{TextSearch.FreqWeighting} │ local_weighting: TextSearch.FreqWeighting TextSearch.FreqWeighting() │ smooth: Float64 0.0 │ min_token_ndocs: Int64 3 │ max_token_pdocs: Float64 0.7 │ weights: Symbol none └ LIBSVMConfig }, 0.22083333333333333) ┌ Info: ("-- perf best_lists[8]:", 8, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[3, 9], 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 0.5 └ LIBSVMConfig }, 0.22276785714285707) ┌ Info: *** Performance on test: └ sc = (microf1 = 0.7889125799573561, precision = 0.7889125799573561, macroprecision = 0.7932600206307103, recall = 0.7889125799573561, macrorecall = 0.7895478684708279, macrof1 = 0.7883583654306356, accuracy = 0.7889125799573561, classf1 = Dict{Any, Any}("♡" => 0.7775280898876404, "💔" => 0.7991886409736308), classprecision = Dict{Any, Any}("♡" => 0.8317307692307693, "💔" => 0.7547892720306514), classrecall = Dict{Any, Any}("♡" => 0.729957805907173, "💔" => 0.8491379310344828)) SearchModels> search params iter=0, initialpopulation=32, maxpopulation=8, bsize=2, mutbsize=8, crossbsize=8 *.* optimization finished, #iter = 859 obj = -300.914149, rho = 0.165391 nSV = 453, nBSV = 314 Total nSV = 453 *.* optimization finished, #iter = 859 obj = -300.914149, rho = 0.165391 nSV = 453, nBSV = 314 Total nSV = 453 *.* optimization finished, #iter = 915 obj = -278.150375, rho = 0.119213 nSV = 508, nBSV = 279 Total nSV = 508 .* optimization finished, #iter = 1056 obj = -273.023456, rho = 0.209790 nSV = 524, nBSV = 257 Total nSV = 524 .* optimization finished, #iter = 957 obj = -305.757242, rho = 0.206731 nSV = 465, nBSV = 314 Total nSV = 465 .* optimization finished, #iter = 968 obj = -281.465415, rho = 0.162946 nSV = 473, nBSV = 285 Total nSV = 473 *.* optimization finished, #iter = 922 obj = -297.876290, rho = 0.172300 nSV = 478, nBSV = 309 Total nSV = 478 vectorizing corpus 44%|██████████████▋ | ETA: 0:00:01vectorizing corpus 0%|▏ | ETA: 0:06:33vectorizing corpus 0%|▏ | ETA: 0:07:09vectorizing corpus 0%|▏ | ETA: 0:07:57vectorizing corpus 0%|▏ | ETA: 0:08:16vectorizing corpus 0%|▏ | ETA: 0:08:16vectorizing corpus 0%|▏ | ETA: 0:08:34vectorizing corpus 0%|▏ | ETA: 0:08:34vectorizing corpus 0%|▏ | ETA: 0:08:33 *.* optimization finished, #iter = 867 obj = -262.292853, rho = 0.041339 nSV = 400, nBSV = 273 Total nSV = 400 *.* optimization finished, #iter = 934 obj = -215.839324, rho = 0.142022 nSV = 421, nBSV = 216 Total nSV = 421 *.* optimization finished, #iter = 865 obj = -235.710998, rho = 0.114298 nSV = 401, nBSV = 238 Total nSV = 401 *.* optimization finished, #iter = 851 obj = -287.112163, rho = 0.099569 nSV = 399, nBSV = 287 Total nSV = 399 *.* optimization finished, #iter = 888 obj = -240.849900, rho = 0.149299 nSV = 395, nBSV = 241 Total nSV = 395 *.* optimization finished, #iter = 868 obj = -254.562140, rho = 0.046961 nSV = 404, nBSV = 252 Total nSV = 404 *.* optimization finished, #iter = 1012 obj = -215.176730, rho = 0.123343 nSV = 435, nBSV = 215 Total nSV = 435 *.* optimization finished, #iter = 869 obj = -262.290552, rho = 0.041687 nSV = 400, nBSV = 273 Total nSV = 400 *.* optimization finished, #iter = 877 obj = -266.955886, rho = 0.066425 nSV = 405, nBSV = 281 Total nSV = 405 *.* optimization finished, #iter = 944 obj = -298.449050, rho = 0.107272 nSV = 412, nBSV = 297 Total nSV = 412 vectorizing corpus 0%|▏ | ETA: 0:09:06vectorizing corpus 0%|▏ | ETA: 0:08:59vectorizing corpus 1%|▏ | ETA: 0:04:11vectorizing corpus 1%|▏ | ETA: 0:03:52vectorizing corpus 0%|▏ | ETA: 0:07:37vectorizing corpus 0%|▏ | ETA: 0:07:28vectorizing corpus 0%|▏ | ETA: 0:07:22vectorizing corpus 1%|▏ | ETA: 0:03:11vectorizing corpus 0%|▏ | ETA: 0:06:17 vectorizing corpus 1%|▏ | ETA: 0:04:51vectorizing corpus 1%|▏ | ETA: 0:04:30vectorizing corpus 1%|▏ | ETA: 0:03:01.* optimization finished, #iter = 947 obj = -268.027657, rho = -0.119071 nSV = 515, nBSV = 266 Total nSV = 515 *.* optimization finished, #iter = 904 obj = -309.422909, rho = -0.257817 nSV = 480, nBSV = 331 Total nSV = 480   vectorizing corpus 98%|████████████████████████████████▏| ETA: 0:00:00vectorizing corpus 2%|▋ | ETA: 0:00:48vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08 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: 0:00:07vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07  *.* optimization finished, #iter = 770 obj = -271.325755, rho = -0.173691 nSV = 387, nBSV = 273 Total nSV = 387 *.* optimization finished, #iter = 904 obj = -231.973164, rho = -0.109679 nSV = 401, nBSV = 236 Total nSV = 401 *.* optimization finished, #iter = 994 obj = -242.642554, rho = -0.035265 nSV = 416, nBSV = 246 Total nSV = 416 *.* optimization finished, #iter = 928 obj = -200.889964, rho = -0.189899 nSV = 413, nBSV = 197 Total nSV = 413 *.* optimization finished, #iter = 968 obj = -258.392340, rho = -0.107882 nSV = 407, nBSV = 265 Total nSV = 407 *.* optimization finished, #iter = 995 obj = -268.576254, rho = -0.126252 nSV = 403, nBSV = 277 Total nSV = 403 *.* optimization finished, #iter = 1036 obj = -279.729110, rho = -0.108489 nSV = 402, nBSV = 276 Total nSV = 402 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05.* optimization finished, #iter = 1019 obj = -277.609670, rho = -0.258078 nSV = 533, nBSV = 285 Total nSV = 533 *.* optimization finished, #iter = 852 obj = -306.711049, rho = -0.370074 nSV = 472, nBSV = 324 Total nSV = 472  .* optimization finished, #iter = 1061 obj = -294.633210, rho = 0.195249 nSV = 493, nBSV = 300 Total nSV = 493 .* optimization finished, #iter = 997 obj = -272.468530, rho = 0.180185 nSV = 529, nBSV = 256 Total nSV = 529 *.* optimization finished, #iter = 919 obj = -310.144802, rho = 0.139673 nSV = 466, nBSV = 328 Total nSV = 466 *.* optimization finished, #iter = 908 obj = -265.747015, rho = 0.102137 nSV = 490, nBSV = 276 Total nSV = 490 .* optimization finished, #iter = 1107 obj = -311.324808, rho = 0.280667 nSV = 462, nBSV = 317 Total nSV = 462 *.* optimization finished, #iter = 919 obj = -310.144800, rho = 0.139674 nSV = 466, nBSV = 328 Total nSV = 466 .* optimization finished, #iter = 1077 obj = -266.512658, rho = 0.186628 nSV = 531, nBSV = 249 Total nSV = 531 .* optimization finished, #iter = 1061 obj = -297.674636, rho = 0.220014 nSV = 493, nBSV = 308 Total nSV = 493 *.* optimization finished, #iter = 942 obj = -312.132490, rho = 0.128845 nSV = 468, nBSV = 325 Total nSV = 468 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05 *.* optimization finished, #iter = 835 obj = -263.844602, rho = 0.028186 nSV = 413, nBSV = 269 Total nSV = 413 *.* optimization finished, #iter = 856 obj = -281.571834, rho = 0.121959 nSV = 395, nBSV = 283 Total nSV = 395 *.* optimization finished, #iter = 934 obj = -298.589361, rho = 0.099266 nSV = 414, nBSV = 298 Total nSV = 414 *.* optimization finished, #iter = 904 obj = -275.371928, rho = 0.136552 nSV = 411, nBSV = 286 Total nSV = 411 *.* optimization finished, #iter = 942 obj = -271.762528, rho = -0.006224 nSV = 409, nBSV = 280 Total nSV = 409 *.*.* optimization finished, #iter = 901 obj = -241.561677, rho = 0.112947 nSV = 417, nBSV = 246 Total nSV = 417  vectorizing corpus 1%|▏ | ETA: 0:07:11vectorizing corpus 1%|▏ | ETA: 0:06:32 vectorizing corpus 1%|▏ | ETA: 0:05:52vectorizing corpus 1%|▏ | ETA: 0:05:09vectorizing corpus 1%|▏ | ETA: 0:04:24vectorizing corpus 1%|▏ | ETA: 0:03:32 vectorizing corpus 1%|▏ | ETA: 0:04:58vectorizing corpus 1%|▏ | ETA: 0:04:44vectorizing corpus 1%|▏ | ETA: 0:04:05vectorizing corpus 1%|▏ | ETA: 0:03:48vectorizing corpus 1%|▏ | ETA: 0:03:20  *.* optimization finished, #iter = 855 obj = -283.669506, rho = -0.106143 nSV = 466, nBSV = 301 Total nSV = 466 *.* optimization finished, #iter = 838 obj = -300.980639, rho = -0.218253 nSV = 482, nBSV = 327 Total nSV = 482 *.* optimization finished, #iter = 971 obj = -279.225944, rho = -0.296312 nSV = 401, nBSV = 276 Total nSV = 401 *.* optimization finished, #iter = 1015 obj = -233.280605, rho = -0.182563 nSV = 401, nBSV = 229 Total nSV = 401 .* optimization finished, #iter = 1052 obj = -240.038779, rho = -0.109917 nSV = 412, nBSV = 244 Total nSV = 412 *.* optimization finished, #iter = 884 obj = -211.127081, rho = -0.193582 nSV = 418, nBSV = 211 Total nSV = 418 *.* optimization finished, #iter = 914 obj = -269.043678, rho = -0.324636 nSV = 411, nBSV = 275 Total nSV = 411 *.* optimization finished, #iter = 753 obj = -278.272305, rho = -0.333836 nSV = 402, nBSV = 291 Total nSV = 402 *.* optimization finished, #iter = 996 obj = -303.178669, rho = -0.198695 nSV = 464, nBSV = 321 Total nSV = 464 *.* optimization finished, #iter = 810 obj = -313.597158, rho = -0.096502 nSV = 462, nBSV = 342 Total nSV = 462 *.* optimization finished, #iter = 851 obj = -284.508621, rho = -0.174094 nSV = 406, nBSV = 285 Total nSV = 406 .* optimization finished, #iter = 981 obj = -263.002523, rho = -0.144819 nSV = 521, nBSV = 265 Total nSV = 521 *.* optimization finished, #iter = 945 obj = -294.068093, rho = -0.286577 nSV = 481, nBSV = 308 Total nSV = 481 *.* optimization finished, #iter = 823 obj = -316.238342, rho = -0.091901 nSV = 466, nBSV = 338 Total nSV = 466 vectorizing corpus 0%|▏ | ETA: 0:08:01vectorizing corpus 0%|▏ | ETA: 0:08:01vectorizing corpus 0%|▏ | ETA: 0:08:00 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:09vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 1%|▏ | ETA: 0:05:36vectorizing corpus 1%|▏ | ETA: 0:04:55vectorizing corpus 1%|▏ | ETA: 0:04:51vectorizing corpus 1%|▏ | ETA: 0:04:32vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 1%|▏ | ETA: 0:04:31vectorizing corpus 1%|▏ | ETA: 0:04:13vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:08vectorizing corpus 1%|▏ | ETA: 0:03:49vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07vectorizing corpus 1%|▏ | ETA: 0:03:23vectorizing corpus 1%|▏ | ETA: 0:03:08 *.* optimization finished, #iter = 911 obj = -232.910567, rho = -0.159486 nSV = 417, nBSV = 236 Total nSV = 417 *.* optimization finished, #iter = 937 obj = -258.270985, rho = -0.101810 nSV = 409, nBSV = 267 Total nSV = 409 *.* optimization finished, #iter = 883 obj = -205.859625, rho = -0.169131 nSV = 407, nBSV = 204 Total nSV = 407 *.* optimization finished, #iter = 939 obj = -229.665901, rho = -0.058568 nSV = 416, nBSV = 229 Total nSV = 416 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:07vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06   *.* optimization finished, #iter = 918 obj = -314.769759, rho = -0.299206 nSV = 469, nBSV = 325 Total nSV = 469 .* optimization finished, #iter = 1033 obj = -275.098756, rho = -0.258425 nSV = 537, nBSV = 280 Total nSV = 537 .* optimization finished, #iter = 1039 obj = -297.375389, rho = -0.384539 nSV = 495, nBSV = 308 Total nSV = 495 *.* optimization finished, #iter = 905 obj = -316.755205, rho = -0.301303 nSV = 474, nBSV = 327 Total nSV = 474 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05 *.* optimization finished, #iter = 873 obj = -293.710608, rho = -0.074260 nSV = 451, nBSV = 315 Total nSV = 451 *.* optimization finished, #iter = 873 obj = -293.710608, rho = -0.074260 nSV = 451, nBSV = 315 Total nSV = 451 *.* optimization finished, #iter = 929 obj = -278.452471, rho = -0.196166 nSV = 507, nBSV = 288 Total nSV = 507 *.* optimization finished, #iter = 915 obj = -231.637355, rho = -0.194935 nSV = 402, nBSV = 238 Total nSV = 402 *.* optimization finished, #iter = 905 obj = -269.228827, rho = -0.321088 nSV = 410, nBSV = 277 Total nSV = 410 *.* optimization finished, #iter = 776 obj = -210.625249, rho = -0.273460 nSV = 403, nBSV = 215 Total nSV = 403 *.* optimization finished, #iter = 856 obj = -226.255144, rho = -0.153077 nSV = 398, nBSV = 234 Total nSV = 398 .* optimization finished, #iter = 1042 obj = -290.343893, rho = -0.254852 nSV = 486, nBSV = 305 Total nSV = 486 *.* optimization finished, #iter = 962 obj = -268.562715, rho = -0.100287 nSV = 528, nBSV = 266 Total nSV = 528 *.* optimization finished, #iter = 810 obj = -313.597159, rho = -0.096502 nSV = 462, nBSV = 342 Total nSV = 462 *.* optimization finished, #iter = 861 obj = -265.139199, rho = -0.153851 nSV = 493, nBSV = 279 Total nSV = 493 vectorizing corpus 1%|▏ | ETA: 0:04:33vectorizing corpus 1%|▏ | ETA: 0:04:14vectorizing corpus 1%|▏ | ETA: 0:04:13vectorizing corpus 1%|▏ | ETA: 0:03:50vectorizing corpus 1%|▏ | ETA: 0:03:22vectorizing corpus 1%|▏ | ETA: 0:03:02  vectorizing corpus 0%|▏ | ETA: 0:16:30vectorizing corpus 0%|▏ | ETA: 0:16:23vectorizing corpus 0%|▏ | ETA: 0:16:16vectorizing corpus 0%|▏ | ETA: 0:16:09vectorizing corpus 0%|▏ | ETA: 0:16:04*.* optimization finished, #iter = 935 obj = -290.205975, rho = -0.321135 nSV = 477, nBSV = 305 Total nSV = 477 *.* optimization finished, #iter = 870 obj = -299.829986, rho = -0.326179 nSV = 478, nBSV = 313 Total nSV = 478 .* optimization finished, #iter = 997 obj = -305.621855, rho = -0.255516 nSV = 494, nBSV = 315 Total nSV = 494 .* optimization finished, #iter = 1036 obj = -277.463269, rho = -0.247445 nSV = 541, nBSV = 280 Total nSV = 541 *.* optimization finished, #iter = 918 obj = -314.769758, rho = -0.299206 nSV = 469, nBSV = 325 Total nSV = 469 *.* optimization finished, #iter = 909 obj = -271.367908, rho = -0.262149 nSV = 508, nBSV = 272 Total nSV = 508 .* optimization finished, #iter = 1023 obj = -313.349765, rho = -0.335207 nSV = 464, nBSV = 324 Total nSV = 464 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 1%|▏ | ETA: 0:03:14  vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:06  vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:02*.* optimization finished, #iter = 782 obj = -245.913416, rho = -0.087001 nSV = 402, nBSV = 260 Total nSV = 402 *.* optimization finished, #iter = 885 obj = -265.562304, rho = -0.193005 nSV = 388, nBSV = 268 Total nSV = 388 *.* optimization finished, #iter = 916 obj = -280.832742, rho = -0.105157 nSV = 405, nBSV = 282 Total nSV = 405 *.* optimization finished, #iter = 961 obj = -264.258150, rho = -0.139983 nSV = 398, nBSV = 262 Total nSV = 398 *.* optimization finished, #iter = 893 obj = -267.195582, rho = 0.016785 nSV = 412, nBSV = 281 Total nSV = 412  *.* optimization finished, #iter = 947 obj = -300.572724, rho = -0.304448 nSV = 472, nBSV = 315 Total nSV = 472 *.* optimization finished, #iter = 947 obj = -300.572724, rho = -0.304448 nSV = 472, nBSV = 315 Total nSV = 472 *.* optimization finished, #iter = 935 obj = -281.095860, rho = -0.274649 nSV = 507, nBSV = 285 Total nSV = 507 *.* optimization finished, #iter = 942 obj = -250.281459, rho = -0.121625 nSV = 420, nBSV = 256 Total nSV = 420 *.* optimization finished, #iter = 891 obj = -274.644829, rho = -0.260119 nSV = 393, nBSV = 275 Total nSV = 393 *.* optimization finished, #iter = 839 obj = -283.727483, rho = -0.187871 nSV = 401, nBSV = 289 Total nSV = 401 *.* optimization finished, #iter = 868 obj = -267.259899, rho = -0.309262 nSV = 398, nBSV = 267 Total nSV = 398 *.* optimization finished, #iter = 972 obj = -278.414897, rho = -0.139839 nSV = 414, nBSV = 280 Total nSV = 414 SearchModels iteration 1> population: 8, bsize: 2, queue: 9, observed: 41, best-error: 0.20035875443662243 worst-error: 0.22429027088080955 [ Info: TaskFailedException(Task (failed) @0x00007b4401843d00) [ Info: ignoring configuration due to exception * optimization finished, #iter = 653 obj = -130.715496, rho = 0.204328 nSV = 535, nBSV = 445 Total nSV = 535 .*.* optimization finished, #iter = 2137 obj = -405.256786, rho = 0.165044 nSV = 467, nBSV = 67 Total nSV = 467 .* optimization finished, #iter = 1178 obj = -264.565169, rho = 0.217385 nSV = 511, nBSV = 258 Total nSV = 511 *.* optimization finished, #iter = 963 obj = -280.543251, rho = 0.084679 nSV = 479, nBSV = 279 Total nSV = 479 * optimization finished, #iter = 672 obj = -136.375555, rho = 0.262224 nSV = 524, nBSV = 455 Total nSV = 524 [ Info: TaskFailedException(Task (failed) @0x00007b4401843df0) [ 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, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 683 obj = -139.843913, rho = 0.232030 nSV = 507, nBSV = 455 Total nSV = 507 ┌ 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.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 1014 obj = -333.092548, rho = 0.424583 nSV = 447, nBSV = 347 Total nSV = 447 * optimization finished, #iter = 621 obj = -131.235816, rho = -0.227982 nSV = 529, nBSV = 445 Total nSV = 529 .*.* optimization finished, #iter = 2096 obj = -390.619550, rho = -0.155975 nSV = 460, nBSV = 57 Total nSV = 460 .* optimization finished, #iter = 1022 obj = -262.165914, rho = -0.196657 nSV = 499, nBSV = 263 Total nSV = 499 *.* optimization finished, #iter = 855 obj = -280.646704, rho = -0.130698 nSV = 469, nBSV = 300 Total nSV = 469 * optimization finished, #iter = 625 obj = -138.379583, rho = -0.187937 nSV = 526, nBSV = 463 Total nSV = 526 vectorizing corpus 1%|▏ | ETA: 0:03:53┌ 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, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 684 obj = -140.446854, rho = -0.143130 nSV = 511, nBSV = 452 Total nSV = 511 * optimization finished, #iter = 638 obj = -131.710050, rho = -0.279955 nSV = 538, nBSV = 440 Total nSV = 538 .*.* optimization finished, #iter = 2103 obj = -420.062393, rho = -0.384194 nSV = 471, nBSV = 71 Total nSV = 471 .* optimization finished, #iter = 975 obj = -276.601726, rho = -0.260718 nSV = 514, nBSV = 274 Total nSV = 514 *.* optimization finished, #iter = 931 obj = -287.104914, rho = -0.305098 nSV = 483, nBSV = 305 Total nSV = 483 * optimization finished, #iter = 649 obj = -138.045129, rho = -0.368757 nSV = 530, nBSV = 460 Total nSV = 530 vectorizing corpus 1%|▏ | ETA: 0:03:50vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:05 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:02 ┌ 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.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 987 obj = -334.853247, rho = -0.398723 nSV = 450, nBSV = 350 Total nSV = 450 ┌ 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, 2], TextSearch.Skipgram[], TextSearch.IdentityTokenTransformation()) TextClassification.VectorModelConfig{TextSearch.TpWeighting, TextSearch.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.TpWeighting TextSearch.TpWeighting() │ min_token_ndocs: Int64 7 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 647 obj = -140.652825, rho = -0.260104 nSV = 507, nBSV = 456 Total nSV = 507 ┌ 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.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 1081 obj = -350.165877, rho = -0.633704 nSV = 462, nBSV = 358 Total nSV = 462 SearchModels iteration 2> population: 8, bsize: 2, queue: 8, observed: 49, best-error: 0.20035875443662243 worst-error: 0.2194337128318553 *.* optimization finished, #iter = 973 obj = -297.779602, rho = 0.204417 nSV = 459, nBSV = 310 Total nSV = 459 ┌ 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, 2], TextSearch.Skipgram[TextSearch.Skipgram(2, 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 7 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .*.* optimization finished, #iter = 1841 obj = -407.122738, rho = 0.103607 nSV = 471, nBSV = 63 Total nSV = 471 .* optimization finished, #iter = 1112 obj = -268.185227, rho = 0.237368 nSV = 502, nBSV = 263 Total nSV = 502 .* optimization finished, #iter = 968 obj = -281.465415, rho = 0.162946 nSV = 473, nBSV = 285 Total nSV = 473 .* optimization finished, #iter = 987 obj = -331.457820, rho = 0.165951 nSV = 467, nBSV = 350 Total nSV = 467 ┌ Warning: using 723 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.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 10 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 937 obj = -360.634247, rho = 0.584983 nSV = 434, nBSV = 376 Total nSV = 434 ┌ 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.TfWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 1126 obj = -315.160062, rho = 0.425419 nSV = 457, nBSV = 331 Total nSV = 457 ┌ 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 4 │ max_token_pdocs: Float64 0.65 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 677 obj = -136.043155, rho = 0.295834 nSV = 507, nBSV = 450 Total nSV = 507 vectorizing corpus 1%|▏ | ETA: 0:03:38*.* optimization finished, #iter = 805 obj = -294.529409, rho = -0.087750 nSV = 450, nBSV = 317 Total nSV = 450 .*.* optimization finished, #iter = 1761 obj = -394.587513, rho = -0.129696 nSV = 474, nBSV = 61 Total nSV = 474 .* optimization finished, #iter = 1006 obj = -267.246500, rho = -0.213043 nSV = 501, nBSV = 273 Total nSV = 501 *.* optimization finished, #iter = 855 obj = -283.669506, rho = -0.106143 nSV = 466, nBSV = 301 Total nSV = 466 *.* optimization finished, #iter = 1039 obj = -325.478624, rho = -0.131753 nSV = 464, nBSV = 336 Total nSV = 464 ┌ 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.TfWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 969 obj = -310.586901, rho = -0.302760 nSV = 438, nBSV = 321 Total nSV = 438 ┌ 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 4 │ max_token_pdocs: Float64 0.65 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:03* optimization finished, #iter = 641 obj = -136.133276, rho = -0.196384 nSV = 505, nBSV = 442 Total nSV = 505 *.* optimization finished, #iter = 948 obj = -301.862144, rho = -0.348888 nSV = 464, nBSV = 313 Total nSV = 464 .*.* optimization finished, #iter = 1758 obj = -411.146493, rho = -0.310070 nSV = 482, nBSV = 70 Total nSV = 482 .* optimization finished, #iter = 1078 obj = -281.833023, rho = -0.277209 nSV = 512, nBSV = 284 Total nSV = 512 *.* optimization finished, #iter = 935 obj = -290.205975, rho = -0.321135 nSV = 477, nBSV = 305 Total nSV = 477 ┌ 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, 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 7 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 1089 obj = -337.248885, rho = -0.230158 nSV = 467, nBSV = 334 Total nSV = 467 ┌ 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.TfWeighting, TextSearch.IdfWeighting} │ global_weighting: TextSearch.IdfWeighting TextSearch.IdfWeighting() │ local_weighting: TextSearch.TfWeighting TextSearch.TfWeighting() │ min_token_ndocs: Int64 4 │ max_token_pdocs: Float64 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 vectorizing corpus 1%|▏ | ETA: 0:03:20.* optimization finished, #iter = 1022 obj = -340.777419, rho = -0.394231 nSV = 464, nBSV = 342 Total nSV = 464 ┌ 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 4 │ max_token_pdocs: Float64 0.65 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 694 obj = -138.885489, rho = -0.335179 nSV = 511, nBSV = 448 Total nSV = 511 ┌ 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.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 10 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 . WARNING: using -h 0 may be faster * optimization finished, #iter = 1161 obj = -357.167885, rho = -0.412408 nSV = 438, nBSV = 369 Total nSV = 438 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:01 ┌ 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.BinaryGlobalWeighting} │ global_weighting: TextSearch.BinaryGlobalWeighting TextSearch.BinaryGlobalWeighting() │ local_weighting: TextSearch.BinaryLocalWeighting TextSearch.BinaryLocalWeighting() │ min_token_ndocs: Int64 10 │ max_token_pdocs: Float64 0.7 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 1143 obj = -375.768566, rho = -0.750497 nSV = 450, nBSV = 380 Total nSV = 450 SearchModels iteration 3> population: 8, bsize: 2, queue: 8, observed: 57, best-error: 0.20035875443662243 worst-error: 0.2124480482981661 *.* optimization finished, #iter = 902 obj = -292.692178, rho = 0.395090 nSV = 453, nBSV = 312 Total nSV = 453 *.* optimization finished, #iter = 978 obj = -281.418056, rho = 0.161738 nSV = 472, nBSV = 286 Total nSV = 472 *.* optimization finished, #iter = 915 obj = -278.150371, rho = 0.119213 nSV = 508, nBSV = 279 Total nSV = 508 .* optimization finished, #iter = 987 obj = -282.240128, rho = 0.139083 nSV = 502, nBSV = 282 Total nSV = 502 * optimization finished, #iter = 693 obj = -135.179954, rho = 0.310479 nSV = 577, nBSV = 476 Total nSV = 577 * optimization finished, #iter = 646 obj = -132.706422, rho = 0.198302 nSV = 558, nBSV = 458 Total nSV = 558 *.* optimization finished, #iter = 895 obj = -264.156718, rho = 0.057357 nSV = 502, nBSV = 267 Total nSV = 502 ┌ 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, 2], TextSearch.Skipgram[TextSearch.Skipgram(2, 1)], 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 0.5384615384615384 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 655 obj = -137.382954, rho = 0.226806 nSV = 515, nBSV = 455 Total nSV = 515 vectorizing corpus 10%|███▏ | ETA: 0:00:10 vectorizing corpus 1%|▏ | ETA: 0:03:26*.* optimization finished, #iter = 894 obj = -283.638408, rho = -0.102502 nSV = 467, nBSV = 301 Total nSV = 467 *.* optimization finished, #iter = 929 obj = -278.452464, rho = -0.196166 nSV = 507, nBSV = 288 Total nSV = 507 *.* optimization finished, #iter = 905 obj = -283.075349, rho = -0.210648 nSV = 500, nBSV = 295 Total nSV = 500 * optimization finished, #iter = 689 obj = -134.158091, rho = -0.276424 nSV = 579, nBSV = 469 Total nSV = 579 * optimization finished, #iter = 670 obj = -131.739672, rho = -0.281234 nSV = 563, nBSV = 454 Total nSV = 563 *.* optimization finished, #iter = 940 obj = -261.760791, rho = -0.165120 nSV = 505, nBSV = 265 Total nSV = 505 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:04 *.* optimization finished, #iter = 893 obj = -290.098861, rho = -0.317730 nSV = 480, nBSV = 307 Total nSV = 480 *.* optimization finished, #iter = 935 obj = -281.095856, rho = -0.274649 nSV = 507, nBSV = 285 Total nSV = 507 *.* optimization finished, #iter = 925 obj = -285.915806, rho = -0.304290 nSV = 502, nBSV = 295 Total nSV = 502 * optimization finished, #iter = 699 obj = -138.914977, rho = -0.280416 nSV = 593, nBSV = 487 Total nSV = 593 * optimization finished, #iter = 679 obj = -133.337390, rho = -0.281614 nSV = 569, nBSV = 449 Total nSV = 569 .* optimization finished, #iter = 1012 obj = -267.935074, rho = -0.247966 nSV = 512, nBSV = 269 Total nSV = 512 *.* optimization finished, #iter = 946 obj = -291.366631, rho = -0.401467 nSV = 453, nBSV = 311 Total nSV = 453 * optimization finished, #iter = 648 obj = -136.553053, rho = -0.193707 nSV = 520, nBSV = 454 Total nSV = 520 *.* optimization finished, #iter = 880 obj = -297.035196, rho = -0.623585 nSV = 456, nBSV = 317 Total nSV = 456 ┌ 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, 1)], 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 0.5384615384615384 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 644 obj = -137.501358, rho = -0.265599 nSV = 514, nBSV = 453 Total nSV = 514 SearchModels iteration 4> population: 8, bsize: 2, queue: 9, observed: 66, best-error: 0.20035875443662243 worst-error: 0.2087182156754871 [ Info: TaskFailedException(Task (failed) @0x00007b43f0bc1780) [ Info: ignoring configuration due to exception .*.* optimization finished, #iter = 1833 obj = -428.642468, rho = 0.039359 nSV = 470, nBSV = 83 Total nSV = 470 *.* optimization finished, #iter = 931 obj = -265.593680, rho = 0.108576 nSV = 496, nBSV = 274 Total nSV = 496 ..*.* optimization finished, #iter = 2312 obj = -430.586182, rho = 0.235062 nSV = 463, nBSV = 79 Total nSV = 463 .* optimization finished, #iter = 1073 obj = -266.811082, rho = 0.208810 nSV = 513, nBSV = 261 Total nSV = 513 *.* optimization finished, #iter = 926 obj = -269.507677, rho = 0.121821 nSV = 480, nBSV = 276 Total nSV = 480 [ Info: TaskFailedException(Task (failed) @0x00007b43f0bc1870) [ Info: ignoring configuration due to exception ┌ 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.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 = 1940 obj = -749.323365, rho = 0.570715 nSV = 408, nBSV = 231 Total nSV = 408 ┌ 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 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 1140 obj = -304.635120, rho = 0.321295 nSV = 481, nBSV = 315 Total nSV = 481 .*.* optimization finished, #iter = 1783 obj = -409.183609, rho = -0.095625 nSV = 470, nBSV = 67 Total nSV = 470 *.* optimization finished, #iter = 895 obj = -263.779234, rho = -0.140720 nSV = 504, nBSV = 273 Total nSV = 504 .*.* optimization finished, #iter = 1995 obj = -421.897621, rho = -0.155858 nSV = 456, nBSV = 74 Total nSV = 456 .* optimization finished, #iter = 997 obj = -261.575126, rho = -0.220113 nSV = 498, nBSV = 270 Total nSV = 498 *.* optimization finished, #iter = 901 obj = -268.488104, rho = -0.161820 nSV = 484, nBSV = 274 Total nSV = 484 vectorizing corpus 34%|███████████▎ | ETA: 0:00:02 vectorizing corpus 0%|▏ | ETA: 0:06:07┌ 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 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 *.* optimization finished, #iter = 917 obj = -300.953142, rho = -0.325883 nSV = 468, nBSV = 311 Total nSV = 468 .* optimization finished, #iter = 970 obj = -269.306758, rho = -0.247502 nSV = 514, nBSV = 267 Total nSV = 514 .*.* optimization finished, #iter = 1996 obj = -450.360718, rho = -0.442566 nSV = 458, nBSV = 86 Total nSV = 458 .* optimization finished, #iter = 963 obj = -276.769964, rho = -0.305101 nSV = 523, nBSV = 289 Total nSV = 523 .* optimization finished, #iter = 947 obj = -276.308195, rho = -0.294373 nSV = 495, nBSV = 287 Total nSV = 495 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:03 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:03 ┌ 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 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .* optimization finished, #iter = 1032 obj = -329.795381, rho = -0.291351 nSV = 478, nBSV = 338 Total nSV = 478 ┌ 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.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 = 2109 obj = -747.115896, rho = -0.535188 nSV = 412, nBSV = 235 Total nSV = 412 .*.* optimization finished, #iter = 1850 obj = -427.856906, rho = -0.465677 nSV = 475, nBSV = 68 Total nSV = 475 ┌ 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.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 = 2002 obj = -802.138179, rho = -0.739864 nSV = 417, nBSV = 248 Total nSV = 417 SearchModels iteration 5> population: 8, bsize: 2, queue: 8, observed: 74, best-error: 0.19855623607919304 worst-error: 0.20575776090551512 [ Info: TaskFailedException(Task (failed) @0x00007b43f0409960) [ Info: ignoring configuration due to exception * optimization finished, #iter = 657 obj = -134.909260, rho = 0.353769 nSV = 536, nBSV = 459 Total nSV = 536 .*.* optimization finished, #iter = 1977 obj = -422.430138, rho = 0.045335 nSV = 472, nBSV = 71 Total nSV = 472 * optimization finished, #iter = 631 obj = -136.863191, rho = 0.209193 nSV = 522, nBSV = 459 Total nSV = 522 .*.* optimization finished, #iter = 1861 obj = -422.305551, rho = 0.067802 nSV = 471, nBSV = 70 Total nSV = 471 [ Info: TaskFailedException(Task (failed) @0x00007b43f0409a50) [ Info: ignoring configuration due to exception ┌ 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(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 4 │ max_token_pdocs: Float64 0.65 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 698 obj = -138.306327, rho = 0.309780 nSV = 545, nBSV = 464 Total nSV = 545 ┌ 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.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 = 2107 obj = -754.185922, rho = 0.601849 nSV = 406, nBSV = 237 Total nSV = 406 * optimization finished, #iter = 660 obj = -136.306056, rho = -0.369443 nSV = 547, nBSV = 458 Total nSV = 547 .*.* optimization finished, #iter = 1813 obj = -403.821647, rho = -0.124161 nSV = 457, nBSV = 62 Total nSV = 457 ┌ 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.Skipgram(3, 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 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 .*.* optimization finished, #iter = 1843 obj = -401.056021, rho = -0.117349 nSV = 467, nBSV = 63 Total nSV = 467 * optimization finished, #iter = 628 obj = -135.485271, rho = -0.134681 nSV = 516, nBSV = 448 Total nSV = 516 ┌ 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.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 4 │ max_token_pdocs: Float64 0.65 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 vectorizing corpus 1%|▏ | ETA: 0:03:27* optimization finished, #iter = 665 obj = -135.617751, rho = -0.141257 nSV = 543, nBSV = 460 Total nSV = 543 ┌ 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.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 = 649 obj = -136.612947, rho = -0.477646 nSV = 544, nBSV = 467 Total nSV = 544 .*.* optimization finished, #iter = 1979 obj = -427.023143, rho = -0.297715 nSV = 473, nBSV = 75 Total nSV = 473 .*.* optimization finished, #iter = 2090 obj = -752.377786, rho = -0.553566 nSV = 405, nBSV = 233 Total nSV = 405 .*.* optimization finished, #iter = 2064 obj = -425.453312, rho = -0.314806 nSV = 470, nBSV = 76 Total nSV = 470 vectorizing corpus 100%|█████████████████████████████████| Time: 0:00:03┌ 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(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 4 │ max_token_pdocs: Float64 0.65 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 667 obj = -143.254384, rho = -0.202915 nSV = 554, nBSV = 481 Total nSV = 554 ┌ 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.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 = 1973 obj = -819.283487, rho = -0.733878 nSV = 415, nBSV = 253 Total nSV = 415 ┌ 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(3, 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 0.5 │ LIBSVMConfig } └ @ TextClassification ~/.julia/packages/TextClassification/q1c16/src/microtc.jl:64 * optimization finished, #iter = 673 obj = -137.897667, rho = -0.294354 nSV = 518, nBSV = 451 Total nSV = 518 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.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 } => 0.20575776090551512, iter=6 (of 16) *.* optimization finished, #iter = 1304 obj = -391.156113, rho = -0.163142 nSV = 705, nBSV = 409 Total nSV = 705 ┌ Info: ("-- microtc_kfolds - perf best_lists[1]:", 1, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[4], Int8[1, 3], 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 4 │ max_token_pdocs: Float64 0.5 └ LIBSVMConfig }, 0.19855623607919304) ┌ Info: ("-- microtc_kfolds - perf best_lists[2]:", 2, {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.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 }, 0.20035875443662243) ┌ Info: ("-- microtc_kfolds - perf best_lists[3]:", 3, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[4], Int8[1, 2], TextSearch.Skipgram[TextSearch.Skipgram(3, 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 7 │ max_token_pdocs: Float64 0.7 └ LIBSVMConfig }, 0.2010978172382899) ┌ Info: ("-- microtc_kfolds - perf best_lists[4]:", 4, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[4], 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 7 │ max_token_pdocs: Float64 0.7 └ LIBSVMConfig }, 0.2020289159347518) ┌ Info: ("-- microtc_kfolds - perf best_lists[5]:", 5, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[4], 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 7 │ max_token_pdocs: Float64 0.5384615384615384 └ LIBSVMConfig }, 0.2020289159347518) ┌ Info: ("-- microtc_kfolds - perf best_lists[6]:", 6, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[4], Int8[1], TextSearch.Skipgram[TextSearch.Skipgram(2, 1)], 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 }, 0.20313605417283365) ┌ Info: ("-- microtc_kfolds - perf best_lists[7]:", 7, {MicroTC_Config TextSearch.TextConfig(true, false, false, true, true, true, false, true, 0, true, Int8[4], Int8[], 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 }, 0.20317704296880612) ┌ 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.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 }, 0.20575776090551512) ┌ Info: *** Performance microtc_kfolds on test: └ sc = (microf1 = 0.7953091684434968, precision = 0.7953091684434968, macroprecision = 0.7960109289617486, recall = 0.7953091684434968, macrorecall = 0.7955587079877783, macrof1 = 0.7952635599694424, accuracy = 0.7953091684434968, classf1 = Dict{Any, Any}("♡" => 0.7922077922077922, "💔" => 0.7983193277310925), classprecision = Dict{Any, Any}("♡" => 0.8133333333333334, "💔" => 0.7786885245901639), classrecall = Dict{Any, Any}("♡" => 0.7721518987341772, "💔" => 0.8189655172413793)) Test Summary: | Pass Total Time microtc | 2 2 3m47.2s Testing TextClassification tests passed Testing completed after 535.86s PkgEval succeeded after 635.57s